SOTAVerified

Video Understanding

A crucial task of Video Understanding is to recognise and localise (in space and time) different actions or events appearing in the video.

Source: Action Detection from a Robot-Car Perspective

Papers

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Showing 801–850 of 1149 papers

TitleStatusHype
Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges—0
Large-Scale Video Classification with Feature Space Augmentation coupled with Learned Label Relations and Ensembling—0
Large Scale Video Representation Learning via Relational Graph Clustering—0
Large-Scale YouTube-8M Video Understanding with Deep Neural Networks—0
LASER: A Neuro-Symbolic Framework for Learning Spatial-Temporal Scene Graphs with Weak Supervision—0
Learning an Augmented RGB Representation with Cross-Modal Knowledge Distillation for Action Detection—0
Learning Audio-guided Video Representation with Gated Attention for Video-Text Retrieval—0
Learning Dynamic MRI Reconstruction with Convolutional Network Assisted Reconstruction Swin Transformer—0
Learning Dynamics via Graph Neural Networks for Human Pose Estimation and Tracking—0
Learning Fine-grained View-Invariant Representations from Unpaired Ego-Exo Videos via Temporal Alignment—0
Learning from Multiple Sources for Video Summarisation—0
Learning Higher-order Object Interactions for Keypoint-based Video Understanding—0
Learning Object State Changes in Videos: An Open-World Perspective—0
Learning reusable concepts across different egocentric video understanding tasks—0
Learning Space-Time Semantic Correspondences—0
Learning text-to-video retrieval from image captioning—0
Learning to Focus on the Foreground for Temporal Sentence Grounding—0
Learning to Visually Connect Actions and their Effects—0
Learning without Prejudice: Avoiding Bias in Webly-Supervised Action Recognition—0
Less than Few: Self-Shot Video Instance Segmentation—0
Leveraging Foundation Models for Multimodal Graph-Based Action Recognition—0
Leveraging Local Temporal Information for Multimodal Scene Classification—0
LIGAR: Lightweight General-purpose Action Recognition—0
LiveVLM: Efficient Online Video Understanding via Streaming-Oriented KV Cache and Retrieval—0
LiVLR: A Lightweight Visual-Linguistic Reasoning Framework for Video Question Answering—0
LLaVA-MLB: Mitigating and Leveraging Attention Bias for Training-Free Video LLMs—0
LLaVA-Octopus: Unlocking Instruction-Driven Adaptive Projector Fusion for Video Understanding—0
LLAVIDAL: A Large LAnguage VIsion Model for Daily Activities of Living—0
LLM4Brain: Training a Large Language Model for Brain Video Understanding—0
LLMs Meet Long Video: Advancing Long Video Question Answering with An Interactive Visual Adapter in LLMs—0
Localizing Events in Videos with Multimodal Queries—0
Localizing Unseen Activities in Video via Image Query—0
Logic-in-Frames: Dynamic Keyframe Search via Visual Semantic-Logical Verification for Long Video Understanding—0
Long Activity Video Understanding using Functional Object-Oriented Network—0
LongCaptioning: Unlocking the Power of Long Caption Generation in Large Multimodal Models—0
Long-Short Temporal Contrastive Learning of Video Transformers—0
LongVILA: Scaling Long-Context Visual Language Models for Long Videos—0
LongViTU: Instruction Tuning for Long-Form Video Understanding—0
Long-VMNet: Accelerating Long-Form Video Understanding via Fixed Memory—0
Look Every Frame All at Once: Video-Ma^2mba for Efficient Long-form Video Understanding with Multi-Axis Gradient Checkpointing—0
Low-Fidelity End-to-End Video Encoder Pre-training for Temporal Action Localization—0
LVAgent: Long Video Understanding by Multi-Round Dynamical Collaboration of MLLM Agents—0
LV-XAttn: Distributed Cross-Attention for Long Visual Inputs in Multimodal Large Language Models—0
M^33D: Learning 3D priors using Multi-Modal Masked Autoencoders for 2D image and video understanding—0
M^3Net: Multi-view Encoding, Matching, and Fusion for Few-shot Fine-grained Action Recognition—0
MaCP: Minimal yet Mighty Adaptation via Hierarchical Cosine Projection—0
Making Every Frame Matter: Continuous Video Understanding for Large Models via Adaptive State Modeling—0
MAMBA4D: Efficient Long-Sequence Point Cloud Video Understanding with Disentangled Spatial-Temporal State Space Models—0
MambaMia: A State-Space-Model-Based Compression for Efficient Video Understanding in Large Multimodal Models—0
MASH-VLM: Mitigating Action-Scene Hallucination in Video-LLMs through Disentangled Spatial-Temporal Representations—0
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