How Far Are We from Intelligent Visual Deductive Reasoning?
Paper
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2403.04732
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Published
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18
VisualLLM
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MoAI: Mixture of All Intelligence for Large Language and Vision Models
Paper • 2403.07508 • Published • 69 -
DragAnything: Motion Control for Anything using Entity Representation
Paper • 2403.07420 • Published • 11 -
Learning and Leveraging World Models in Visual Representation Learning
Paper • 2403.00504 • Published • 24 -
Mora: Enabling Generalist Video Generation via A Multi-Agent Framework
Paper • 2403.13248 • Published • 71 -
Magic Fixup: Streamlining Photo Editing by Watching Dynamic Videos
Paper • 2403.13044 • Published • 13 -
Vid2Robot: End-to-end Video-conditioned Policy Learning with Cross-Attention Transformers
Paper • 2403.12943 • Published • 13 -
LLaVA-UHD: an LMM Perceiving Any Aspect Ratio and High-Resolution Images
Paper • 2403.11703 • Published • 13 -
VideoAgent: A Memory-augmented Multimodal Agent for Video Understanding
Paper • 2403.11481 • Published • 10 -
Uni-SMART: Universal Science Multimodal Analysis and Research Transformer
Paper • 2403.10301 • Published • 50 -
RAFT: Adapting Language Model to Domain Specific RAG
Paper • 2403.10131 • Published • 58 -
VideoAgent: Long-form Video Understanding with Large Language Model as Agent
Paper • 2403.10517 • Published • 28 -
Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models
Paper • 2403.18814 • Published • 37 -
Improving Text-to-Image Consistency via Automatic Prompt Optimization
Paper • 2403.17804 • Published • 14 -
Can large language models explore in-context?
Paper • 2403.15371 • Published • 30 -
LLM2LLM: Boosting LLMs with Novel Iterative Data Enhancement
Paper • 2403.15042 • Published • 24 -
InternVideo2: Scaling Video Foundation Models for Multimodal Video Understanding
Paper • 2403.15377 • Published • 16 -
DragAPart: Learning a Part-Level Motion Prior for Articulated Objects
Paper • 2403.15382 • Published • 9 -
OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments
Paper • 2404.07972 • Published • 40 -
Rho-1: Not All Tokens Are What You Need
Paper • 2404.07965 • Published • 79 -
WILBUR: Adaptive In-Context Learning for Robust and Accurate Web Agents
Paper • 2404.05902 • Published • 20 -
Ferret-v2: An Improved Baseline for Referring and Grounding with Large Language Models
Paper • 2404.07973 • Published • 28 -
Best Practices and Lessons Learned on Synthetic Data for Language Models
Paper • 2404.07503 • Published • 25 -
OmniFusion Technical Report
Paper • 2404.06212 • Published • 73 -
Ferret-UI: Grounded Mobile UI Understanding with Multimodal LLMs
Paper • 2404.05719 • Published • 56 -
ByteEdit: Boost, Comply and Accelerate Generative Image Editing
Paper • 2404.04860 • Published • 24 -
AutoWebGLM: Bootstrap And Reinforce A Large Language Model-based Web Navigating Agent
Paper • 2404.03648 • Published • 22 -
MiniGPT4-Video: Advancing Multimodal LLMs for Video Understanding with Interleaved Visual-Textual Tokens
Paper • 2404.03413 • Published • 21 -
Scaling Instructable Agents Across Many Simulated Worlds
Paper • 2404.10179 • Published • 23