Papers Video Understanding
“Video Understanding” 태그가 달린 논문 1,149편 · 필터 해제
VideoITG: Multimodal Video Understanding with Instructed Temporal Grounding
Recent studies have revealed that selecting informative and relevant video frames can significantly improve the performance of Video Large Language Models (Video-LLMs). Current methods, such as reducing inter-frame redun…
Video GroundingVideo UnderstandingUGC-VideoCaptioner: An Omni UGC Video Detail Caption Model and New Benchmarks
Real-world user-generated videos, especially on platforms like TikTok, often feature rich and intertwined audio visual content. However, existing video captioning benchmarks and models remain predominantly visual centric…
Video CaptioningVideo UnderstandingEmbRACE-3K: Embodied Reasoning and Action in Complex Environments
Recent advanced vision-language models(VLMs) have demonstrated strong performance on passive, offline image and video understanding tasks. However, their effectiveness in embodied settings, which require online interacti…
Scene UnderstandingSpatial ReasoningVideo UnderstandingChat with AI: The Surprising Turn of Real-time Video Communication from Human to AI
AI Video Chat emerges as a new paradigm for Real-time Communication (RTC), where one peer is not a human, but a Multimodal Large Language Model (MLLM). This makes interaction between humans and AI more intuitive, as if c…
Large Language ModelMultimodal Large Language ModelVideo UnderstandingBeyond Appearance: Geometric Cues for Robust Video Instance Segmentation
Video Instance Segmentation (VIS) fundamentally struggles with pervasive challenges including object occlusions, motion blur, and appearance variations during temporal association. To overcome these limitations, this wor…
Depth EstimationDepth PredictionInstance SegmentationMonocular Depth Estimation+4Omni-Video: Democratizing Unified Video Understanding and Generation
Notable breakthroughs in unified understanding and generation modeling have led to remarkable advancements in image understanding, reasoning, production and editing, yet current foundational models predominantly focus on…
Video GenerationVideo UnderstandingMCAM: Multimodal Causal Analysis Model for Ego-Vehicle-Level Driving Video Understanding
Accurate driving behavior recognition and reasoning are critical for autonomous driving video understanding. However, existing methods often tend to dig out the shallow causal, fail to address spurious correlations acros…
Autonomous DrivingVideo UnderstandingVideo Event Reasoning and Prediction by Fusing World Knowledge from LLMs with Vision Foundation Models
Current video understanding models excel at recognizing "what" is happening but fall short in high-level cognitive tasks like causal reasoning and future prediction, a limitation rooted in their lack of commonsense world…
Future predictionLarge Language ModelVideo UnderstandingWorld Knowledge+1Kwai Keye-VL Technical Report
While Multimodal Large Language Models (MLLMs) demonstrate remarkable capabilities on static images, they often fall short in comprehending dynamic, information-dense short-form videos, a dominant medium in today's digit…
Instruction FollowingReinforcement Learning (RL)Video UnderstandingLarge Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges
Crash detection from video feeds is a critical problem in intelligent transportation systems. Recent developments in large language models (LLMs) and vision-language models (VLMs) have transformed how we process, reason …
Video UnderstandingCAVALRY-V: A Large-Scale Generator Framework for Adversarial Attacks on Video MLLMs
Video Multimodal Large Language Models (V-MLLMs) have shown impressive capabilities in temporal reasoning and cross-modal understanding, yet their vulnerability to adversarial attacks remains underexplored due to unique …
Text GenerationVideo UnderstandingGLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning
We present GLM-4.1V-Thinking, a vision-language model (VLM) designed to advance general-purpose multimodal understanding and reasoning. In this report, we share our key findings in the development of the reasoning-centri…
document understandingMultimodal ReasoningVideo UnderstandingFlash-VStream: Efficient Real-Time Understanding for Long Video Streams
Benefiting from the advances in large language models and cross-modal alignment, existing multimodal large language models have achieved prominent performance in image and short video understanding. However, the understa…
cross-modal alignmentEgoSchemaMMEMVBench+2ActAlign: Zero-Shot Fine-Grained Video Classification via Language-Guided Sequence Alignment
We address the task of zero-shot fine-grained video classification, where no video examples or temporal annotations are available for unseen action classes. While contrastive vision-language models such as SigLIP demonst…
Dynamic Time WarpingLarge Language ModelOpen Set Learningtext similarity+2LLaVA-Scissor: Token Compression with Semantic Connected Components for Video LLMs
In this paper, we present LLaVA-Scissor, a training-free token compression strategy designed for video multimodal large language models. Previous methods mostly attempt to compress tokens based on attention scores, but f…
Question AnsweringVideo Question AnsweringVideo UnderstandingQ-Frame: Query-aware Frame Selection and Multi-Resolution Adaptation for Video-LLMs
Multimodal Large Language Models (MLLMs) have demonstrated significant success in visual understanding tasks. However, challenges persist in adapting these models for video comprehension due to the large volume of data a…
MMEVideo MMEVideo UnderstandingIPFormer-VideoLLM: Enhancing Multi-modal Video Understanding for Multi-shot Scenes
Video Large Language Models (VideoLLMs) have demonstrated remarkable understanding capabilities, but are found struggling to tackle multi-shot scenarios,e.g., video clips with varying camera angles or scene changes. This…
AttributeQuestion AnsweringVideo UnderstandingTask-Aware KV Compression For Cost-Effective Long Video Understanding
Long-video understanding (LVU) remains a severe challenge for existing multimodal large language models (MLLMs), primarily due to the prohibitive computational cost. Recent approaches have explored KV compression to miti…
Video UnderstandingPEVLM: Parallel Encoding for Vision-Language Models
Vision-Language Models (VLMs) have demonstrated strong performance in video-language tasks, yet their application to long video understanding remains constrained by the quadratic complexity of standard attention mechanis…
Autonomous DrivingVideo UnderstandingGRPO-CARE: Consistency-Aware Reinforcement Learning for Multimodal Reasoning
Recent reinforcement learning approaches, such as outcome-supervised GRPO, have advanced Chain-of-Thought reasoning in large language models (LLMs), yet their adaptation to multimodal LLMs (MLLMs) is unexplored. To addre…
Multimodal Reasoningreinforcement-learningReinforcement LearningVideo Understanding