Multimodal Pathway: Improve Transformers with Irrelevant Data from Other Modalities
Paper
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2401.14405
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Published
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13
General Multimodal Learning
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CharXiv: Charting Gaps in Realistic Chart Understanding in Multimodal LLMs
Paper • 2406.18521 • Published • 29 -
xGen-VideoSyn-1: High-fidelity Text-to-Video Synthesis with Compressed Representations
Paper • 2408.12590 • Published • 36 -
Law of Vision Representation in MLLMs
Paper • 2408.16357 • Published • 95 -
CogVLM2: Visual Language Models for Image and Video Understanding
Paper • 2408.16500 • Published • 57 -
Building and better understanding vision-language models: insights and future directions
Paper • 2408.12637 • Published • 127 -
Oryx MLLM: On-Demand Spatial-Temporal Understanding at Arbitrary Resolution
Paper • 2409.12961 • Published • 25 -
Revisit Large-Scale Image-Caption Data in Pre-training Multimodal Foundation Models
Paper • 2410.02740 • Published • 53 -
Video Instruction Tuning With Synthetic Data
Paper • 2410.02713 • Published • 38 -
AuroraCap: Efficient, Performant Video Detailed Captioning and a New Benchmark
Paper • 2410.03051 • Published • 6 -
Grounded-VideoLLM: Sharpening Fine-grained Temporal Grounding in Video Large Language Models
Paper • 2410.03290 • Published • 7 -
A Spark of Vision-Language Intelligence: 2-Dimensional Autoregressive Transformer for Efficient Finegrained Image Generation
Paper • 2410.01912 • Published • 14 -
MIO: A Foundation Model on Multimodal Tokens
Paper • 2409.17692 • Published • 53 -
Emu3: Next-Token Prediction is All You Need
Paper • 2409.18869 • Published • 94 -
MM1.5: Methods, Analysis & Insights from Multimodal LLM Fine-tuning
Paper • 2409.20566 • Published • 56 -
Janus: Decoupling Visual Encoding for Unified Multimodal Understanding and Generation
Paper • 2410.13848 • Published • 34 -
Eagle: Exploring The Design Space for Multimodal LLMs with Mixture of Encoders
Paper • 2408.15998 • Published • 87 -
VideoGLaMM: A Large Multimodal Model for Pixel-Level Visual Grounding in Videos
Paper • 2411.04923 • Published • 22 -
VideoLights: Feature Refinement and Cross-Task Alignment Transformer for Joint Video Highlight Detection and Moment Retrieval
Paper • 2412.01558 • Published • 4 -
AV-Odyssey Bench: Can Your Multimodal LLMs Really Understand Audio-Visual Information?
Paper • 2412.02611 • Published • 24 -
VisionZip: Longer is Better but Not Necessary in Vision Language Models
Paper • 2412.04467 • Published • 110 -
Florence-VL: Enhancing Vision-Language Models with Generative Vision Encoder and Depth-Breadth Fusion
Paper • 2412.04424 • Published • 62 -
Euclid: Supercharging Multimodal LLMs with Synthetic High-Fidelity Visual Descriptions
Paper • 2412.08737 • Published • 53 -
Multimodal Latent Language Modeling with Next-Token Diffusion
Paper • 2412.08635 • Published • 44 -
OLA-VLM: Elevating Visual Perception in Multimodal LLMs with Auxiliary Embedding Distillation
Paper • 2412.09585 • Published • 11 -
InstanceCap: Improving Text-to-Video Generation via Instance-aware Structured Caption
Paper • 2412.09283 • Published • 19 -
MegaPairs: Massive Data Synthesis For Universal Multimodal Retrieval
Paper • 2412.14475 • Published • 54 -
LongBench v2: Towards Deeper Understanding and Reasoning on Realistic Long-context Multitasks
Paper • 2412.15204 • Published • 34 -
Descriptive Caption Enhancement with Visual Specialists for Multimodal Perception
Paper • 2412.14233 • Published • 6 -
ABC: Achieving Better Control of Multimodal Embeddings using VLMs
Paper • 2503.00329 • Published • 18 -
Scaling Vision Pre-Training to 4K Resolution
Paper • 2503.19903 • Published • 21