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The dataset generation failed because of a cast error
Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 1 new columns ({'text'}) and 4 missing columns ({'num_passages', 'num_embeddings', 'embedding_offset', 'passage_offset'}).
This happened while the json dataset builder was generating data using
hf://datasets/CVPR2024/CVPR2024-papers-abstract-index/collection.json (at revision b3f04bb06d7a94ec9fff6b041705d09089c44f3f)
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2011, in _prepare_split_single
writer.write_table(table)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 585, in write_table
pa_table = table_cast(pa_table, self._schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2302, in table_cast
return cast_table_to_schema(table, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2256, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
text: string
to
{'passage_offset': Value(dtype='int64', id=None), 'num_passages': Value(dtype='int64', id=None), 'num_embeddings': Value(dtype='int64', id=None), 'embedding_offset': Value(dtype='int64', id=None)}
because column names don't match
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1317, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 932, in convert_to_parquet
builder.download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1027, in download_and_prepare
self._download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1122, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1882, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2013, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 1 new columns ({'text'}) and 4 missing columns ({'num_passages', 'num_embeddings', 'embedding_offset', 'passage_offset'}).
This happened while the json dataset builder was generating data using
hf://datasets/CVPR2024/CVPR2024-papers-abstract-index/collection.json (at revision b3f04bb06d7a94ec9fff6b041705d09089c44f3f)
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
passage_offset int64 | num_passages int64 | num_embeddings int64 | embedding_offset int64 | text string |
|---|---|---|---|---|
0 | 3,485 | 645,932 | 0 | null |
null | null | null | null | Hyperspectral images (HSIs) have extensive applications in various fields such as medicine agriculture and industry. Nevertheless acquiring high signal-to-noise ratio HSI poses a challenge due to narrow-band spectral filtering. Consequently the importance of HSI denoising is substantial especially for snapshot hyperspe... |
null | null | null | null | This network reconstructs noise-free Unmixing probability distributions effectively mitigating noise-induced degradations within these components. Finally the reconstructed HSI is reconstructed through unmixing reconstruction by blending the diffusion-adjusted abundance map with the spectral endmembers. Experimental re... |
null | null | null | null | The reflective nature of the human eye is an under-appreciated source of information about what the world around us looks like. By imaging the eyes of a moving person we capture multiple views of a scene outside the camera's direct line of sight through the reflections in the eyes. In this paper we reconstruct a radian... |
null | null | null | null | The recovery of occluded human meshes poses challenges for current methods due to the difficulty in extracting effective image features under severe occlusion. In this paper we introduce DPMesh an innovative framework for occluded human mesh recovery that capitalizes on the profound knowledge about object structure and... |
null | null | null | null | Extensive quantitative and qualitative experiments affirm the efficacy of our framework as we outperform state-of-the-art methods on both occlusion-specific and standard datasets underscoring its ability to achieve precise and robust 3D human mesh recovery particularly in challenging scenarios involving occlusion and c... |
null | null | null | null | The training of contemporary deep learning models heavily relies on publicly available data posing a risk of unauthorized access to online data and raising concerns about data privacy. Current approaches to creating unlearnable data involve incorporating small specially designed noises but these methods strictly limit ... |
null | null | null | null | Monocular 3D lane detection has become a fundamental problem in the context of autonomous driving which comprises the tasks of finding the road surface and locating lane markings. One major challenge lies in a flexible but robust line representation capable of modeling complex lane structures while still avoiding unpre... |
null | null | null | null | 3D city generation is a desirable yet challenging task since humans are more sensitive to structural distortions in urban environments. Additionally generating 3D cities is more complex than 3D natural scenes since buildings as objects of the same class exhibit a wider range of appearances compared to the relatively co... |
null | null | null | null | High-resolution wide-angle fisheye images are becoming more and more important for robotics applications such as autonomous driving. However using ordinary convolutional neural networks or vision transformers on this data is problematic due to projection and distortion losses introduced when projecting to a rectangular... |
null | null | null | null | We present 3D Paintbrush a technique for automatically texturing local semantic regions on meshes via text descriptions. Our method is designed to operate directly on meshes producing texture maps which seamlessly integrate into standard graphics pipelines. We opt to simultaneously produce a localization map (to specif... |
null | null | null | null | Out-of-Distribution (OOD) detection aims to address the excessive confidence prediction by neural networks by triggering an alert when the input sample deviates significantly from the training distribution (in-distribution) indicating that the output may not be reliable. Current OOD detection approaches explore all kin... |
null | null | null | null | By using a simple linear regression as a test time adaptation we can make a more precise OOD prediction. We further propose an online variant of the proposed method which achieves promising performance and is more practical for real applications. Theoretical analysis is given to prove the effectiveness of our methods. ... |
null | null | null | null | Unsupervised video object segmentation aims to segment the most prominent object in a video sequence. However the existence of complex backgrounds and multiple foreground objects make this task challenging. To address this issue we propose a guided slot attention network to reinforce spatial structural information and ... |
null | null | null | null | Significant progress in image deblurring has been achieved by deep learning methods especially the remarkable performance of supervised models on paired synthetic data. However real-world quality degradation is more complex than synthetic datasets and acquiring paired data in real-world scenarios poses significant chal... |
null | null | null | null | Action detection aims to localize the starting and ending points of action instances in untrimmed videos and predict the classes of those instances. In this paper we make the observation that the outputs of the action detection task can be formulated as images. Thus from a novel perspective we tackle action detection v... |
null | null | null | null | Character animation in real-world scenarios necessitates a variety of constraints such as trajectories key-frames interactions etc. Existing methodologies typically treat single or a finite set of these constraint(s) as separate control tasks. These methods are often specialized and the tasks they address are rarely ex... |
null | null | null | null | Consequently the generated motion not only inherits the prior of the generative model but also satisfies the requirements of the compounded constraints. Our experiments demonstrate that our approach can generate high-quality motions when addressing a wide range of unseen tasks. These tasks encompass motion control by m... |
null | null | null | null | Self-supervised landmark estimation is a challenging task that demands the formation of locally distinct feature representations to identify sparse facial landmarks in the absence of annotated data. To tackle this task existing state-of-the-art (SOTA) methods (1) extract coarse features from backbones that are trained ... |
null | null | null | null | Camouflaged vision perception is an important vision task with numerous practical applications. Due to the expensive collection and labeling costs this community struggles with a major bottleneck that the species category of its datasets is limited to a small number of object species. However the existing camouflaged g... |
null | null | null | null | Recently diffusion models have emerged as a new powerful generative method for 3D point cloud generation tasks. However few works study the effect of the architecture of the diffusion model in the 3D point cloud resorting to the typical UNet model developed for 2D images. Inspired by the wide adoption of Transformers w... |
null | null | null | null | In this work we propose a method to address the challenge of rendering a 3D human from a single image in a free-view manner. Some existing approaches could achieve this by using generalizable pixel-aligned implicit fields to reconstruct a textured mesh of a human or by employing a 2D diffusion model as guidance with th... |
null | null | null | null | Existing text-based person retrieval datasets often have relatively coarse-grained text annotations. This hinders the model to comprehend the fine-grained semantics of query texts in real scenarios. To address this problem we contribute a new benchmark named UFineBench for text-based person retrieval with ultra-fine gr... |
null | null | null | null | It achieves fine granularity mining by adopting a shared cross-modal granularity decoder and hard negative match mechanism. With standard in-domain evaluation CFAM establishes competitive performance across various datasets especially on our ultra fine-grained UFine6926. Furthermore by evaluating on UFine3C we demonstr... |
null | null | null | null | Hyperparameter Optimization and Neural Architecture Search are powerful in attaining state-of-the-art machine learning models with Bayesian Optimization (BO) standing out as a mainstream method. Extending BO into the multi-fidelity setting has been an emerging research topic in this field but faces the challenge of det... |
null | null | null | null | Real-time rendering of photorealistic and controllable human avatars stands as a cornerstone in Computer Vision and Graphics. While recent advances in neural implicit rendering have unlocked unprecedented photorealism for digital avatars real-time performance has mostly been demonstrated for static scenes only. To addr... |
null | null | null | null | Adversarial training is often formulated as a min-max problem however concentrating only on the worst adversarial examples causes alternating repetitive confusion of the model i.e. previously defended or correctly classified samples are not defensible or accurately classifiable in subsequent adversarial training. We ch... |
null | null | null | null | This work introduces ArtAdapter a transformative text-to-image (T2I) style transfer framework that transcends traditional limitations of color brushstrokes and object shape capturing high-level style elements such as composition and distinctive artistic expression. The integration of a multi-level style encoder with ou... |
null | null | null | null | This paper tackles a novel yet challenging problem: how to transfer knowledge from the emerging Segment Anything Model (SAM) -- which reveals impressive zero-shot instance segmentation capacity -- to learn a compact panoramic semantic segmentation model i.e. student without requiring any labeled data. This poses consid... |
null | null | null | null | DAR then incorporates a cross-task complementary fusion block to adaptively merge the predictions of SAM and TA to obtain more reliable ensemble logits. Moreover we introduce a Multi-level Knowledge Adaptation (MKA) module to efficiently transfer the multi-level feature knowledge from TA and ensemble logits to learn a ... |
null | null | null | null | In this paper we focus on a challenging Online Task-Free Class Incremental Learning (OTFCIL) problem. Different from the existing methods that continuously learn the feature space from data streams we propose a novel compute-and-align paradigm for the OTFCIL. It first computes an optimal geometry i.e. the class prototy... |
null | null | null | null | Experimental comparison results on four benchmark datasets including CIFAR10 CIFAR100 CUB200 and CoRe50 demonstrate the efficiency and superiority of the DYSON method. The source code is provided in the supplementary material. |
null | null | null | null | An ideal model for dense video captioning -- predicting captions localized temporally in a video -- should be able to handle long input videos predict rich detailed textual descriptions and be able to produce outputs before processing the entire video. Current state-of-the-art models however process a fixed number of d... |
null | null | null | null | Despite the growing demand for accurate surface normal estimation models existing methods use general-purpose dense prediction models adopting the same inductive biases as other tasks. In this paper we discuss the inductive biases needed for surface normal estimation and propose to (1) utilize the per-pixel ray directi... |
null | null | null | null | Event sensors offer high temporal resolution visual sensing which makes them ideal for perceiving fast visual phenomena without suffering from motion blur. Certain applications in robotics and vision-based navigation require 3D perception of an object undergoing circular or spinning motion in front of a static camera s... |
null | null | null | null | Event camera has significant advantages in capturingdynamic scene information while being prone to noise interferenceparticularly in challenging conditions like lowthreshold and low illumination. However most existing researchfocuses on gentle situations hindering event cameraapplications in realistic complex scenarios... |
null | null | null | null | Federated learning (FL) has emerged as a new paradigm for privacy-preserving collaborative training. Under domain skew the current FL approaches are biased and face two fairness problems. 1) Parameter Update Conflict: data disparity among clients leads to varying parameter importance and inconsistent update directions.... |
null | null | null | null | In this work we study a novel problem which focuses on person identification while performing daily activities. Learning biometric features from RGB videos is challenging due to spatio-temporal complexity and presence of appearance biases such as clothing color and background. We propose ABNet a novel framework which l... |
null | null | null | null | Despite the remarkable progress in image style transfer formulating style in the context of art is inherently subjective and challenging. In contrast to existing methods this study shows that vanilla diffusion models can directly extract style information and seamlessly integrate the generative prior into the content i... |
null | null | null | null | Visual interactivity understanding within visual scenes presents a significant challenge in computer vision. Existing methods focus on complex interactivities while leveraging a simple relationship model. These methods however struggle with a diversity of appearance situation position interaction and relation in videos... |
null | null | null | null | Trajectory prediction is fundamental in computer vision and autonomous driving particularly for understanding pedestrian behavior and enabling proactive decision-making. Existing approaches in this field often assume precise and complete observational data neglecting the challenges associated with out-of-view objects a... |
null | null | null | null | Learning fair representation in deep learning is essential to mitigate discriminatory outcomes and enhance trustworthiness. However previous research has been commonly established on inappropriate assumptions prone to unrealistic counterfactuals and performance degradation. Although some proposed alternative approaches... |
null | null | null | null | Current controls over diffusion models (e.g. through text or ControlNet) for image generation fall short in recognizing abstract continuous attributes like illumination direction or non-rigid shape change. In this paper we present an approach for allowing users of text-to-image models to have fine-grained control of se... |
null | null | null | null | Modern text-to-image generation models produce high-quality images that are both photorealistic and faithful to the text prompts. However this quality comes at significant computational cost: nearly all of these models are iterative and require running sampling multiple times with large models. This iterative process i... |
null | null | null | null | The perception of motion behavior in a dynamic environment holds significant importance for autonomous driving systems wherein class-agnostic motion prediction methods directly predict the motion of the entire point cloud. While most existing methods rely on fully-supervised learning the manual labeling of point cloud ... |
null | null | null | null | In this paper we address the problem of efficient point searching and sampling for volume neural rendering. Within this realm two typical approaches are employed: rasterization and ray tracing. The rasterization-based methods enable real-time rendering at the cost of increased memory and lower fidelity. In contrast the... |
null | null | null | null | In recent interactive segmentation algorithms previous probability maps are used as network input to help predictions in the current segmentation round. However despite the utilization of previous masks useful information contained in the probability maps is not well propagated to the current predictions. In this paper... |
null | null | null | null | Domain adaptive object detection aims to adapt detection models to domains where annotated data is unavailable. Existing methods have been proposed to address the domain gap using the semi-supervised student-teacher framework. However a fundamental issue arises from the class imbalance in the labelled training set whic... |
null | null | null | null | We describe a novel method StyLitGAN for relighting and resurfacing images in the absence of labeled data. StyLitGAN generates images with realistic lighting effects including cast shadows soft shadows inter-reflections and glossy effects without the need for paired or CGI data. StyLitGAN uses an intrinsic image method... |
null | null | null | null | The laws of model size data volume computation and model performance have been extensively studied in the field of Natural Language Processing (NLP). However the scaling laws in Scene Text Recognition (STR) have not yet been investigated. To address this we conducted comprehensive studies that involved examining the co... |
null | null | null | null | We tackle the problem of 3D point cloud localization based on a few natural linguistic descriptions and introduce a novel neural network Text2Loc that fully interprets the semantic relationship between points and text. Text2Loc follows a coarse-to-fine localization pipeline: text-submap global place recognition followe... |
null | null | null | null | Tensor network (TN) representation is a powerful technique for computer vision and machine learning. TN structure search (TN-SS) aims to search for a customized structure to achieve a compact representation which is a challenging NP-hard problem. Recent "sampling-evaluation"-based methods require sampling an extensive ... |
null | null | null | null | Medical vision language pre-training (VLP) has emerged as a frontier of research enabling zero-shot pathological recognition by comparing the query image with the textual descriptions for each disease. Due to the complex semantics of biomedical texts current methods struggle to align medical images with key pathologica... |
null | null | null | null | We introduce MoMask a novel masked modeling framework for text-driven 3D human motion generation. In MoMask a hierarchical quantization scheme is employed to represent human motion as multi-layer discrete motion tokens with high-fidelity details. Starting at the base layer with a sequence of motion tokens obtained by v... |
null | null | null | null | 0.141 of T2M-GPT) on the HumanML3D dataset and 0.228 (vs 0.514) on KIT-ML respectively. MoMask can also be seamlessly applied in related tasks without further model fine-tuning such as text-guided temporal inpainting. |
null | null | null | null | Inverse rendering aims at recovering both geometry and materials of objects. It provides a more compatible reconstruction for conventional rendering engines compared with the neural radiance fields (NeRFs). On the other hand existing NeRF-based inverse rendering methods cannot handle glossy objects with local light int... |
null | null | null | null | Large vision-language models (VLMs) like CLIP have demonstrated good zero-shot learning performance in the unsupervised domain adaptation task. Yet most transfer approaches for VLMs focus on either the language or visual branches overlooking the nuanced interplay between both modalities. In this work we introduce a Uni... |
null | null | null | null | Affine subspaces of Euclidean spaces are also referred to as flats. A standard task in computer vision or more generally in engineering and applied sciences is fitting a flat to a set of points which is commonly solved using the PCA. We generalize this technique to enable fitting a flat to a set of other flats possibly... |
null | null | null | null | As wearable cameras become more popular an important question emerges: how to identify camera wearers within the perspective of conventional static cameras. The drastic difference between first-person (egocentric) and third-person (exocentric) camera views makes this a challenging task. We present PersonEnvironmentNet ... |
null | null | null | null | Point cloud matching a crucial technique in computer vision medical and robotics fields is primarily concerned with finding correspondences between pairs of point clouds or voxels. In some practical scenarios emphasizing local differences is crucial for accurately identifying a correct match thereby enhancing the overa... |
null | null | null | null | Additionally we define a new medical task called automatic Bone Side Estimation (BSE) which we address through a global similarity score derived from coupled eigenspaces. In order to test it we propose a benchmark collecting bone surface structures from various public datasets. Our matching technique based on Coupled L... |
null | null | null | null | Foundation models encompass an extensive knowledge base and offer remarkable transferability. However this knowledge becomes outdated or insufficient over time. The challenge lies in continuously updating foundation models to accommodate novel information while retaining their original capabilities. Leveraging the fact... |
null | null | null | null | Templates serve as a good starting point to implement a design (e.g. banner slide) but it takes great effort from designers to manually create. In this paper we present Desigen an automatic template creation pipeline which generates background images as well as harmonious layout elements over the background. Different ... |
null | null | null | null | When editing a video a piece of attractive background music is indispensable. However video background music generation tasks face several challenges for example the lack of suitable training datasets and the difficulties in flexibly controlling the music generation process and sequentially aligning the video and music... |
null | null | null | null | Audiovisual representation learning typically relies on the correspondence between sight and sound. However there are often multiple audio tracks that can correspond with a visual scene. Consider for example different conversations on the same crowded street. The effect of such counterfactual pairs on audiovisual repre... |
null | null | null | null | Vision Transformer (ViT) has emerged as a prominent backbone for computer vision. For more efficient ViTs recent works lessen the quadratic cost of the self-attention layer by pruning or fusing the redundant tokens. However these works faced the speed-accuracy trade-off caused by the loss of information. Here we argue ... |
null | null | null | null | Experimental results prove that MCTF consistently surpasses the previous reduction methods with and without training. Specifically DeiT-T and DeiT-S with MCTF reduce FLOPs by about 44% while improving the performance (+0.5% and +0.3%) over the base model respectively. We also demonstrate the applicability of MCTF in va... |
null | null | null | null | The spiking cameras offer the benefits of high dynamic range (HDR) high temporal resolution and low data redundancy. However reconstructing HDR videos in high-speed conditions using single-bit spikings presents challenges due to the limited bit depth. Increasing the bit depth of the spikings is advantageous for boostin... |
null | null | null | null | Long-form video content constitutes a significant portion of internet traffic making automated video summarization an essential research problem. However existing video summarization datasets are notably limited in their size constraining the effectiveness of state-of-the-art methods for generalization. Our work aims t... |
null | null | null | null | Most current arbitrary-scale image super-resolution (SR) methods has commonly relied on simulated data generated by simple synthetic degradation models (e.g. bicubic downsampling) at continuous various scales thereby falling short in capturing the complex degradation of real-world images. This limitation hinders the vi... |
null | null | null | null | Our LMI model exhibits the superior effectiveness compared to other models. This study is of great significance in developing more efficient algorithms and improving the performance of arbitrary-scale image SR methods in practical applications. Our dataset and codes are available at https://github.com/pf0607/COZ. |
null | null | null | null | Gaze is a powerful form of non-verbal communication that humans develop from an early age. As such modeling this behavior is an important task that can benefit a broad set of application domains ranging from robotics to sociology. In particular the gaze following task in computer vision is defined as the prediction of ... |
null | null | null | null | Thus it is difficult to quantitatively evaluate these methods reliably with the available benchmarks or integrate them into a larger human behavior understanding system. Instead we are the first to propose a multi-person transformer-based architecture that maintains the original task formulation and ensures control ove... |
null | null | null | null | Novel-view synthesis through diffusion models has demonstrated remarkable potential for generating diverse and high-quality images. Yet the independent process of image generation in these prevailing methods leads to challenges in maintaining multiple-view consistency. To address this we introduce ViewFusion a novel tr... |
null | null | null | null | We propose SketchINR to advance the representation of vector sketches with implicit neural models. A variable length vector sketch is compressed into a latent space of fixed dimension that implicitly encodes the underlying shape as a function of time and strokes. The learned function predicts the xy point coordinates i... |
null | null | null | null | This paper studies open-vocabulary segmentation (OVS) through calibrating in-vocabulary and domain-biased embedding space with generalized contextual prior of CLIP. As the core of open-vocabulary understanding alignment of visual content with the semantics of unbounded text has become the bottleneck of this field. To a... |
null | null | null | null | Recent learning methods for object pose estimation require resource-intensive training for each individual object instance or category hampering their scalability in real applications when confronted with previously unseen objects. In this paper we propose MatchU a Fuse-Describe-Match strategy for 6D pose estimation fr... |
null | null | null | null | Progress in lighting estimation is tracked by computing existing image quality assessment (IQA) metrics on images from standard datasets. While this may appear to be a reasonable approach we demonstrate that doing so does not correlate to human preference when the estimated lighting is used to relight a virtual scene i... |
null | null | null | null | The annotation of blind image quality assessment (BIQA) is labor-intensive and time-consuming especially for authentic images. Training on synthetic data is expected to be beneficial but synthetically trained models often suffer from poor generalization in real domains due to domain gaps. In this work we make a key obs... |
null | null | null | null | Data replay is a successful incremental learning technique for images. It prevents catastrophic forgetting by keeping a reservoir of previous data original or synthesized to ensure the model retains past knowledge while adapting to novel concepts. However its application in the video domain is rudimentary as it simply ... |
null | null | null | null | We have recently seen tremendous progress in photo-real human modeling and rendering. Yet efficiently rendering realistic human performance and integrating it into the rasterization pipeline remains challenging. In this paper we present HiFi4G an explicit and compact Gaussian-based approach for high-fidelity human perf... |
null | null | null | null | This paper proposes a novel task named "3D part grouping". Suppose there is a mixed set containing scattered parts from various shapes. This task requires algorithms to find out every possible combination among all the parts. To address this challenge we propose the so called Gradient Field-based Auto-Regressive Sampli... |
null | null | null | null | We develop a novel vectorized image representation scheme accommodating both shape/geometry and texture in a decoupled way particularly tailored for reconstruction and editing tasks of artistic/design images such as Emojis and Cliparts. In the heart of this representation is a set of sparsely and unevenly located 2D co... |
null | null | null | null | Diffusion probabilistic models (DPMs) are a key component in modern generative models. DPM-solvers have achieved reduced latency and enhanced quality significantly but have posed challenges to find the exact inverse (i.e. finding the initial noise from the given image). Here we investigate the exact inversions for DPM-... |
null | null | null | null | Segment Anything Model (SAM) has emerged as a powerful tool for numerous vision applications. A key component that drives the impressive performance for zero-shot transfer and high versatility is a super large Transformer model trained on the extensive high-quality SA-1B dataset. While beneficial the huge computation c... |
null | null | null | null | On segment anything task such as zero-shot instance segmentation our EfficientSAMs with SAMI-pretrained lightweight image encoders perform favorably with a significant gain (e.g. 4 AP on COCO/LVIS) over other fast SAM models. Our EfficientSAM code and models are available at https://github.com/yformer/EfficientSAM. |
null | null | null | null | We present ChatScene a Large Language Model (LLM)-based agent that leverages the capabilities of LLMs to generate safety-critical scenarios for autonomous vehicles. Given unstructured language instructions the agent first generates textually described traffic scenarios using LLMs. These scenario descriptions are subseq... |
null | null | null | null | Extensive experimental results underscore the efficacy of ChatScene in improving the safety of autonomous vehicles. For instance the scenarios generated by ChatScene show a 15% increase in collision rates compared to state-of-the-art baselines when tested against different reinforcement learning-based ego vehicles. Fur... |
null | null | null | null | Text-driven video editing poses significant challenges in exhibiting flicker-free visual continuity while preserving the inherent motion patterns of original videos. Existing methods operate under a paradigm where motion and appearance are intricately intertwined. This coupling leads to the network either over-fitting ... |
null | null | null | null | Teeth localization segmentation and labeling in 2D images have great potential in modern dentistry to enhance dental diagnostics treatment planning and population-based studies on oral health. However general instance segmentation frameworks are incompetent due to 1) the subtle differences between some teeth' shapes (e... |
null | null | null | null | Besides we collect 3) the first open-sourced intraoral image dataset IO150K which comprises over 150k intraoral photos and all photos are annotated by orthodontists using a human-machine hybrid algorithm. Experiments on IO150K demonstrate that our TeethSEG outperforms the state-of-the-art segmentation models on dental ... |
null | null | null | null | The Segment Anything Model (SAM) marks a notable milestone in segmentation models highlighted by its robust zero-shot capabilities and ability to handle diverse prompts. SAM follows a pipeline that separates interactive segmentation into image preprocessing through a large encoder and interactive inference via a lightw... |
null | null | null | null | Dwin-MSA localizes attention computations around the target object enhancing object-related embeddings with minimal computational overhead. Second we propose Pixel-wise Dynamic ReLU (P-DyReLU) to enable sufficient integration of interactive information from a few initial clicks that have significant impacts on the over... |
null | null | null | null | We explore visual reinforcement learning (RL) using two complementary visual modalities: frame-based RGB camera and event-based Dynamic Vision Sensor (DVS). Existing multi-modality visual RL methods often encounter challenges in effectively extracting task-relevant information from multiple modalities while suppressing... |
null | null | null | null | Recently a number of image-mixing-based augmentation techniques have been introduced to improve the generalization of deep neural networks. In these techniques two or more randomly selected natural images are mixed together to generate an augmented image. Such methods may not only omit important portions of the input i... |
null | null | null | null | Reward finetuning has emerged as a promising approach to aligning foundation models with downstream objectives. Remarkable success has been achieved in the language domain by using reinforcement learning (RL) to maximize rewards that reflect human preference. However in the vision domain existing RL-based reward finetu... |
null | null | null | null | We further develop an online algorithm with proximal updates to stably optimize the RDP objective. In experiments we demonstrate that PRDP can match the reward maximization ability of well-established RL-based methods in small-scale training. Furthermore through large-scale training on text prompts from the Human Prefe... |
null | null | null | null | Data-Free Meta-Learning (DFML) aims to extract knowledge from a collection of pre-trained models without requiring the original data presenting practical benefits in contexts constrained by data privacy concerns. Current DFML methods primarily focus on the data recovery from these pre-trained models. However they suffe... |
null | null | null | null | We present Bayesian Diffusion Models (BDM) a prediction algorithm that performs effective Bayesian inference by tightly coupling the top-down (prior) information with the bottom-up (data-driven) procedure via joint diffusion processes. We demonstrate the application of BDM on the 3D shape reconstruction task. Compared ... |
null | null | null | null | General image fusion aims at integrating important information from multi-source images. However due to the significant cross-task gap the respective fusion mechanism varies considerably in practice resulting in limited performance across subtasks. To handle this problem we propose a novel task-customized mixture of ad... |
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