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Collections including paper arxiv:2406.08085
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VoCo-LLaMA: Towards Vision Compression with Large Language Models
Paper • 2406.12275 • Published • 29 -
TroL: Traversal of Layers for Large Language and Vision Models
Paper • 2406.12246 • Published • 34 -
Multimodal Task Vectors Enable Many-Shot Multimodal In-Context Learning
Paper • 2406.15334 • Published • 8 -
Benchmarking Multi-Image Understanding in Vision and Language Models: Perception, Knowledge, Reasoning, and Multi-Hop Reasoning
Paper • 2406.12742 • Published • 14
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PLLaVA : Parameter-free LLaVA Extension from Images to Videos for Video Dense Captioning
Paper • 2404.16994 • Published • 35 -
VideoMamba: State Space Model for Efficient Video Understanding
Paper • 2403.06977 • Published • 27 -
VideoAgent: Long-form Video Understanding with Large Language Model as Agent
Paper • 2403.10517 • Published • 31 -
Video Mamba Suite: State Space Model as a Versatile Alternative for Video Understanding
Paper • 2403.09626 • Published • 13
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EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters
Paper • 2402.04252 • Published • 25 -
Vision Superalignment: Weak-to-Strong Generalization for Vision Foundation Models
Paper • 2402.03749 • Published • 12 -
ScreenAI: A Vision-Language Model for UI and Infographics Understanding
Paper • 2402.04615 • Published • 38 -
EfficientViT-SAM: Accelerated Segment Anything Model Without Performance Loss
Paper • 2402.05008 • Published • 19
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Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer
Paper • 1701.06538 • Published • 4 -
Sparse Networks from Scratch: Faster Training without Losing Performance
Paper • 1907.04840 • Published • 3 -
ZeRO: Memory Optimizations Toward Training Trillion Parameter Models
Paper • 1910.02054 • Published • 4 -
A Mixture of h-1 Heads is Better than h Heads
Paper • 2005.06537 • Published • 2