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| "arxivId": "2305.10415", |
| "title": "PMC-VQA: Visual Instruction Tuning for Medical Visual Question Answering" |
| }, |
| "2310.14566": { |
| "arxivId": "2310.14566", |
| "title": "HallusionBench: You See What You Think? Or You Think What You See? An Image-Context Reasoning Benchmark Challenging for GPT-4V(ision), LLaVA-1.5, and Other Multi-modality Models" |
| }, |
| "2212.10846": { |
| "arxivId": "2212.10846", |
| "title": "From Images to Textual Prompts: Zero-shot Visual Question Answering with Frozen Large Language Models" |
| }, |
| "2305.04160": { |
| "arxivId": "2305.04160", |
| "title": "X-LLM: Bootstrapping Advanced Large Language Models by Treating Multi-Modalities as Foreign Languages" |
| }, |
| "2303.06594": { |
| "arxivId": "2303.06594", |
| "title": "ChatGPT Asks, BLIP-2 Answers: Automatic Questioning Towards Enriched Visual Descriptions" |
| }, |
| "2307.02499": { |
| "arxivId": "2307.02499", |
| "title": "mPLUG-DocOwl: Modularized Multimodal Large Language Model for Document Understanding" |
| }, |
| "2305.14167": { |
| "arxivId": "2305.14167", |
| "title": "DetGPT: Detect What You Need via Reasoning" |
| }, |
| "2211.11682": { |
| "arxivId": "2211.11682", |
| "title": "PointCLIP V2: Adapting CLIP for Powerful 3D Open-world Learning" |
| }, |
| "2309.16058": { |
| "arxivId": "2309.16058", |
| "title": "AnyMAL: An Efficient and Scalable Any-Modality Augmented Language Model" |
| }, |
| "2402.11684": { |
| "arxivId": "2402.11684", |
| "title": "ALLaVA: Harnessing GPT4V-Synthesized Data for Lite Vision-Language Models" |
| }, |
| "2211.16198": { |
| "arxivId": "2211.16198", |
| "title": "SuS-X: Training-Free Name-Only Transfer of Vision-Language Models" |
| }, |
| "2310.16045": { |
| "arxivId": "2310.16045", |
| "title": "Woodpecker: Hallucination Correction for Multimodal Large Language Models" |
| }, |
| "2311.07574": { |
| "arxivId": "2311.07574", |
| "title": "To See is to Believe: Prompting GPT-4V for Better Visual Instruction Tuning" |
| }, |
| "2307.14539": { |
| "arxivId": "2307.14539", |
| "title": "Jailbreak in pieces: Compositional Adversarial Attacks on Multi-Modal Language Models" |
| }, |
| "2305.15023": { |
| "arxivId": "2305.15023", |
| "title": "Cheap and Quick: Efficient Vision-Language Instruction Tuning for Large Language Models" |
| }, |
| "2305.02677": { |
| "arxivId": "2305.02677", |
| "title": "Caption Anything: Interactive Image Description with Diverse Multimodal Controls" |
| }, |
| "2311.07397": { |
| "arxivId": "2311.07397", |
| "title": "An LLM-free Multi-dimensional Benchmark for MLLMs Hallucination Evaluation" |
| }, |
| "2311.05332": { |
| "arxivId": "2311.05332", |
| "title": "On the Road with GPT-4V(ision): Early Explorations of Visual-Language Model on Autonomous Driving" |
| }, |
| "2307.02469": { |
| "arxivId": "2307.02469", |
| "title": "What Matters in Training a GPT4-Style Language Model with Multimodal Inputs?" |
| }, |
| "2402.03766": { |
| "arxivId": "2402.03766", |
| "title": "MobileVLM V2: Faster and Stronger Baseline for Vision Language Model" |
| }, |
| "2312.14135": { |
| "arxivId": "2312.14135", |
| "title": "V*: Guided Visual Search as a Core Mechanism in Multimodal LLMs" |
| }, |
| "2202.06767": { |
| "arxivId": "2202.06767", |
| "title": "Wukong: A 100 Million Large-scale Chinese Cross-modal Pre-training Benchmark" |
| }, |
| "2403.12895": { |
| "arxivId": "2403.12895", |
| "title": "mPLUG-DocOwl 1.5: Unified Structure Learning for OCR-free Document Understanding" |
| }, |
| "2311.12871": { |
| "arxivId": "2311.12871", |
| "title": "An Embodied Generalist Agent in 3D World" |
| }, |
| "2310.16436": { |
| "arxivId": "2310.16436", |
| "title": "DDCoT: Duty-Distinct Chain-of-Thought Prompting for Multimodal Reasoning in Language Models" |
| }, |
| "2402.12226": { |
| "arxivId": "2402.12226", |
| "title": "AnyGPT: Unified Multimodal LLM with Discrete Sequence Modeling" |
| }, |
| "2310.05126": { |
| "arxivId": "2310.05126", |
| "title": "UReader: Universal OCR-free Visually-situated Language Understanding with Multimodal Large Language Model" |
| }, |
| "2308.15126": { |
| "arxivId": "2308.15126", |
| "title": "Evaluation and Analysis of Hallucination in Large Vision-Language Models" |
| }, |
| "2401.16158": { |
| "arxivId": "2401.16158", |
| "title": "Mobile-Agent: Autonomous Multi-Modal Mobile Device Agent with Visual Perception" |
| }, |
| "2403.04473": { |
| "arxivId": "2403.04473", |
| "title": "TextMonkey: An OCR-Free Large Multimodal Model for Understanding Document" |
| }, |
| "2309.09971": { |
| "arxivId": "2309.09971", |
| "title": "MindAgent: Emergent Gaming Interaction" |
| }, |
| "2308.12067": { |
| "arxivId": "2308.12067", |
| "title": "InstructionGPT-4: A 200-Instruction Paradigm for Fine-Tuning MiniGPT-4" |
| }, |
| "2312.12436": { |
| "arxivId": "2312.12436", |
| "title": "A Challenger to GPT-4V? Early Explorations of Gemini in Visual Expertise" |
| }, |
| "2312.10665": { |
| "arxivId": "2312.10665", |
| "title": "Silkie: Preference Distillation for Large Visual Language Models" |
| }, |
| "2312.10032": { |
| "arxivId": "2312.10032", |
| "title": "Osprey: Pixel Understanding with Visual Instruction Tuning" |
| }, |
| "2305.16103": { |
| "arxivId": "2305.16103", |
| "title": "ChatBridge: Bridging Modalities with Large Language Model as a Language Catalyst" |
| }, |
| "2305.14705": { |
| "arxivId": "2305.14705", |
| "title": "Mixture-of-Experts Meets Instruction Tuning: A Winning Combination for Large Language Models" |
| }, |
| "2310.01779": { |
| "arxivId": "2310.01779", |
| "title": "HallE-Switch: Rethinking and Controlling Object Existence Hallucinations in Large Vision Language Models for Detailed Caption" |
| }, |
| "2305.14985": { |
| "arxivId": "2305.14985", |
| "title": "IdealGPT: Iteratively Decomposing Vision and Language Reasoning via Large Language Models" |
| }, |
| "2311.18651": { |
| "arxivId": "2311.18651", |
| "title": "LL3DA: Visual Interactive Instruction Tuning for Omni-3D Understanding, Reasoning, and Planning" |
| }, |
| "2308.12038": { |
| "arxivId": "2308.12038", |
| "title": "Large Multilingual Models Pivot Zero-Shot Multimodal Learning across Languages" |
| }, |
| "2311.16103": { |
| "arxivId": "2311.16103", |
| "title": "Video-Bench: A Comprehensive Benchmark and Toolkit for Evaluating Video-based Large Language Models" |
| }, |
| "2310.00582": { |
| "arxivId": "2310.00582", |
| "title": "Pink: Unveiling the Power of Referential Comprehension for Multi-modal LLMs" |
| }, |
| "2312.06968": { |
| "arxivId": "2312.06968", |
| "title": "Hallucination Augmented Contrastive Learning for Multimodal Large Language Model" |
| }, |
| "2309.09958": { |
| "arxivId": "2309.09958", |
| "title": "An Empirical Study of Scaling Instruct-Tuned Large Multimodal Models" |
| }, |
| "2305.02317": { |
| "arxivId": "2305.02317", |
| "title": "Visual Chain of Thought: Bridging Logical Gaps with Multimodal Infillings" |
| }, |
| "2311.01477": { |
| "arxivId": "2311.01477", |
| "title": "FAITHSCORE: Evaluating Hallucinations in Large Vision-Language Models" |
| }, |
| "2309.15564": { |
| "arxivId": "2309.15564", |
| "title": "Jointly Training Large Autoregressive Multimodal Models" |
| }, |
| "2304.07919": { |
| "arxivId": "2304.07919", |
| "title": "Chain of Thought Prompt Tuning in Vision Language Models" |
| }, |
| "2401.12915": { |
| "arxivId": "2401.12915", |
| "title": "Red Teaming Visual Language Models" |
| }, |
| "2311.18248": { |
| "arxivId": "2311.18248", |
| "title": "mPLUG-PaperOwl: Scientific Diagram Analysis with the Multimodal Large Language Model" |
| }, |
| "2312.02153": { |
| "arxivId": "2312.02153", |
| "title": "Aligning and Prompting Everything All at Once for Universal Visual Perception" |
| }, |
| "2311.01487": { |
| "arxivId": "2311.01487", |
| "title": "What Makes for Good Visual Instructions? Synthesizing Complex Visual Reasoning Instructions for Visual Instruction Tuning" |
| }, |
| "2308.07891": { |
| "arxivId": "2308.07891", |
| "title": "Link-Context Learning for Multimodal LLMs" |
| }, |
| "2401.06395": { |
| "arxivId": "2401.06395", |
| "title": "ModaVerse: Efficiently Transforming Modalities with LLMs" |
| }, |
| "2312.07553": { |
| "arxivId": "2312.07553", |
| "title": "Hijacking Context in Large Multi-modal Models" |
| }, |
| "2312.02520": { |
| "arxivId": "2312.02520", |
| "title": "Towards More Unified In-Context Visual Understanding" |
| }, |
| "2305.13903": { |
| "arxivId": "2305.13903", |
| "title": "Let's Think Frame by Frame: Evaluating Video Chain of Thought with Video Infilling and Prediction" |
| } |
| } |
