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Collections including paper arxiv:2404.03413

Papers - University - Harvard University

MiniGPT4-Video: Advancing Multimodal LLMs for Video Understanding with Interleaved Visual-Textual Tokens

Paper • 2404.03413 • Published Apr 4 • 25
Scaling Data-Constrained Language Models

Paper • 2305.16264 • Published May 25, 2023 • 17
Emergence of Hidden Capabilities: Exploring Learning Dynamics in Concept Space

Paper • 2406.19370 • Published Jun 27 • 1

MiniGPT4-Video: Advancing Multimodal LLMs for Video Understanding with Interleaved Visual-Textual Tokens

Paper • 2404.03413 • Published Apr 4 • 25

Papers - Video - Fine-tuning

Direct Preference Optimization of Video Large Multimodal Models from Language Model Reward

Paper • 2404.01258 • Published Apr 1 • 10
MiniGPT4-Video: Advancing Multimodal LLMs for Video Understanding with Interleaved Visual-Textual Tokens

Paper • 2404.03413 • Published Apr 4 • 25

Papers - Encoders - Image - Clip

EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters

Paper • 2402.04252 • Published Feb 6 • 25
MiniGPT4-Video: Advancing Multimodal LLMs for Video Understanding with Interleaved Visual-Textual Tokens

Paper • 2404.03413 • Published Apr 4 • 25
openai/clip-vit-large-patch14-336

Zero-Shot Image Classification • Updated Oct 4, 2022 • 7.87M • 182
openai/clip-vit-base-patch32

Zero-Shot Image Classification • Updated Feb 29 • 26.9M • 479

VideoAgent: Long-form Video Understanding with Large Language Model as Agent

Paper • 2403.10517 • Published Mar 15 • 30
VideoAgent: A Memory-augmented Multimodal Agent for Video Understanding

Paper • 2403.11481 • Published Mar 18 • 11
VideoMamba: State Space Model for Efficient Video Understanding

Paper • 2403.06977 • Published Mar 11 • 27
MovieLLM: Enhancing Long Video Understanding with AI-Generated Movies

Paper • 2403.01422 • Published Mar 3 • 26

How Far Are We from Intelligent Visual Deductive Reasoning?

Paper • 2403.04732 • Published Mar 7 • 18
MoAI: Mixture of All Intelligence for Large Language and Vision Models

Paper • 2403.07508 • Published Mar 12 • 75
DragAnything: Motion Control for Anything using Entity Representation

Paper • 2403.07420 • Published Mar 12 • 12
Learning and Leveraging World Models in Visual Representation Learning

Paper • 2403.00504 • Published Mar 1 • 29

Papers - Video - Understanding

Video Mamba Suite: State Space Model as a Versatile Alternative for Video Understanding

Paper • 2403.09626 • Published Mar 14 • 12
VideoAgent: Long-form Video Understanding with Large Language Model as Agent

Paper • 2403.10517 • Published Mar 15 • 30
VSTAR: Generative Temporal Nursing for Longer Dynamic Video Synthesis

Paper • 2403.13501 • Published Mar 20 • 9
LITA: Language Instructed Temporal-Localization Assistant

Paper • 2403.19046 • Published Mar 27 • 17

Papers - Video - Synthetic Data Generator

MovieLLM: Enhancing Long Video Understanding with AI-Generated Movies

Paper • 2403.01422 • Published Mar 3 • 26
VisionGPT-3D: A Generalized Multimodal Agent for Enhanced 3D Vision Understanding

Paper • 2403.09530 • Published Mar 14 • 8
VidToMe: Video Token Merging for Zero-Shot Video Editing

Paper • 2312.10656 • Published Dec 17, 2023 • 9
TC4D: Trajectory-Conditioned Text-to-4D Generation

Paper • 2403.17920 • Published Mar 26 • 15

Video as the New Language for Real-World Decision Making

Paper • 2402.17139 • Published Feb 27 • 18
VideoCrafter1: Open Diffusion Models for High-Quality Video Generation

Paper • 2310.19512 • Published Oct 30, 2023 • 15
VideoMamba: State Space Model for Efficient Video Understanding

Paper • 2403.06977 • Published Mar 11 • 27
VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models

Paper • 2401.09047 • Published Jan 17 • 13

EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters

Paper • 2402.04252 • Published Feb 6 • 25
Vision Superalignment: Weak-to-Strong Generalization for Vision Foundation Models

Paper • 2402.03749 • Published Feb 6 • 12
ScreenAI: A Vision-Language Model for UI and Infographics Understanding

Paper • 2402.04615 • Published Feb 7 • 36
EfficientViT-SAM: Accelerated Segment Anything Model Without Performance Loss

Paper • 2402.05008 • Published Feb 7 • 19

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