Collections
Discover the best community collections!
Collections including paper arxiv:2412.00947
-
Multimodal Self-Instruct: Synthetic Abstract Image and Visual Reasoning Instruction Using Language Model
Paper • 2407.07053 • Published • 47 -
LMMs-Eval: Reality Check on the Evaluation of Large Multimodal Models
Paper • 2407.12772 • Published • 35 -
VLMEvalKit: An Open-Source Toolkit for Evaluating Large Multi-Modality Models
Paper • 2407.11691 • Published • 16 -
MMIU: Multimodal Multi-image Understanding for Evaluating Large Vision-Language Models
Paper • 2408.02718 • Published • 62
-
EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters
Paper • 2402.04252 • Published • 30 -
Vision Superalignment: Weak-to-Strong Generalization for Vision Foundation Models
Paper • 2402.03749 • Published • 15 -
ScreenAI: A Vision-Language Model for UI and Infographics Understanding
Paper • 2402.04615 • Published • 44 -
EfficientViT-SAM: Accelerated Segment Anything Model Without Performance Loss
Paper • 2402.05008 • Published • 23
-
VisOnlyQA: Large Vision Language Models Still Struggle with Visual Perception of Geometric Information
Paper • 2412.00947 • Published • 8 -
ryokamoi/VisOnlyQA_Eval_Real_v1.1
Viewer • Updated • 900 • 161 -
ryokamoi/VisOnlyQA_Eval_Synthetic
Viewer • Updated • 700 • 124 • 2 -
ryokamoi/VisOnlyQA_Train
Viewer • Updated • 70k • 274 • 2
-
MS MARCO Web Search: a Large-scale Information-rich Web Dataset with Millions of Real Click Labels
Paper • 2405.07526 • Published • 21 -
Automatic Data Curation for Self-Supervised Learning: A Clustering-Based Approach
Paper • 2405.15613 • Published • 17 -
A Touch, Vision, and Language Dataset for Multimodal Alignment
Paper • 2402.13232 • Published • 16 -
How Do Large Language Models Acquire Factual Knowledge During Pretraining?
Paper • 2406.11813 • Published • 31
-
VisOnlyQA: Large Vision Language Models Still Struggle with Visual Perception of Geometric Information
Paper • 2412.00947 • Published • 8 -
ryokamoi/VisOnlyQA_Eval_Real_v1.1
Viewer • Updated • 900 • 161 -
ryokamoi/VisOnlyQA_Eval_Synthetic
Viewer • Updated • 700 • 124 • 2 -
ryokamoi/VisOnlyQA_Train
Viewer • Updated • 70k • 274 • 2
-
Multimodal Self-Instruct: Synthetic Abstract Image and Visual Reasoning Instruction Using Language Model
Paper • 2407.07053 • Published • 47 -
LMMs-Eval: Reality Check on the Evaluation of Large Multimodal Models
Paper • 2407.12772 • Published • 35 -
VLMEvalKit: An Open-Source Toolkit for Evaluating Large Multi-Modality Models
Paper • 2407.11691 • Published • 16 -
MMIU: Multimodal Multi-image Understanding for Evaluating Large Vision-Language Models
Paper • 2408.02718 • Published • 62
-
MS MARCO Web Search: a Large-scale Information-rich Web Dataset with Millions of Real Click Labels
Paper • 2405.07526 • Published • 21 -
Automatic Data Curation for Self-Supervised Learning: A Clustering-Based Approach
Paper • 2405.15613 • Published • 17 -
A Touch, Vision, and Language Dataset for Multimodal Alignment
Paper • 2402.13232 • Published • 16 -
How Do Large Language Models Acquire Factual Knowledge During Pretraining?
Paper • 2406.11813 • Published • 31
-
EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters
Paper • 2402.04252 • Published • 30 -
Vision Superalignment: Weak-to-Strong Generalization for Vision Foundation Models
Paper • 2402.03749 • Published • 15 -
ScreenAI: A Vision-Language Model for UI and Infographics Understanding
Paper • 2402.04615 • Published • 44 -
EfficientViT-SAM: Accelerated Segment Anything Model Without Performance Loss
Paper • 2402.05008 • Published • 23