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 551–600 of 1149 papers

TitleStatusHype
VELOCITI: Benchmarking Video-Language Compositional Reasoning with Strict Entailment—0
VersaVid-R1: A Versatile Video Understanding and Reasoning Model from Question Answering to Captioning Tasks—0
VEU-Bench: Towards Comprehensive Understanding of Video Editing—0
ViaRL: Adaptive Temporal Grounding via Visual Iterated Amplification Reinforcement Learning—0
ViBe: A Text-to-Video Benchmark for Evaluating Hallucination in Large Multimodal Models—0
ViCaS: A Dataset for Combining Holistic and Pixel-level Video Understanding using Captions with Grounded Segmentation—0
VidCompress: Memory-Enhanced Temporal Compression for Video Understanding in Large Language Models—0
Video4MRI: An Empirical Study on Brain Magnetic Resonance Image Analytics with CNN-based Video Classification Frameworks—0
VideoAgent: A Memory-augmented Multimodal Agent for Video Understanding—0
VideoAutoArena: An Automated Arena for Evaluating Large Multimodal Models in Video Analysis through User Simulation—0
Perceptron Synthesis Network: Rethinking the Action Scale Variances in Videos—0
Video Domain Incremental Learning for Human Action Recognition in Home Environments—0
Video Event Reasoning and Prediction by Fusing World Knowledge from LLMs with Vision Foundation Models—0
VideoExpert: Augmented LLM for Temporal-Sensitive Video Understanding—0
VideoGLUE: Video General Understanding Evaluation of Foundation Models—0
Video-GroundingDINO: Towards Open-Vocabulary Spatio-Temporal Video Grounding—0
VideoGrounding-DINO: Towards Open-Vocabulary Spatio-Temporal Video Grounding—0
VideoHallucer: Evaluating Intrinsic and Extrinsic Hallucinations in Large Video-Language Models—0
VideoITG: Multimodal Video Understanding with Instructed Temporal Grounding—0
Video Language Model Pretraining with Spatio-temporal Masking—0
VideoLLaMB: Long-context Video Understanding with Recurrent Memory Bridges—0
VideoLLM Benchmarks and Evaluation: A Survey—0
VideoMCC: a New Benchmark for Video Comprehension—0
Video-of-Thought: Step-by-Step Video Reasoning from Perception to Cognition—0
VideoPrism: A Foundational Visual Encoder for Video Understanding—0
Videoprompter: an ensemble of foundational models for zero-shot video understanding—0
Video Quality Assessment for Online Processing: From Spatial to Temporal Sampling—0
Video RWKV:Video Action Recognition Based RWKV—0
VideoSAVi: Self-Aligned Video Language Models without Human Supervision—0
VideoScan: Enabling Efficient Streaming Video Understanding via Frame-level Semantic Carriers—0
Video Swin Transformers for Egocentric Video Understanding @ Ego4D Challenges 2022—0
Video Time: Properties, Encoders and Evaluation—0
Video Token Merging for Long-form Video Understanding—0
Video Understanding as Machine Translation—0
Video Watermarking: Safeguarding Your Video from (Unauthorized) Annotations by Video-based LLMs—0
Video-XL-Pro: Reconstructive Token Compression for Extremely Long Video Understanding—0
VidHalluc: Evaluating Temporal Hallucinations in Multimodal Large Language Models for Video Understanding—0
VidLPRO: A Video-Language Pre-training Framework for Robotic and Laparoscopic Surgery—0
ViFi-ReID: A Two-Stream Vision-WiFi Multimodal Approach for Person Re-identification—0
VISTA: Enhancing Long-Duration and High-Resolution Video Understanding by Video Spatiotemporal Augmentation—0
Visual Context Window Extension: A New Perspective for Long Video Understanding—0
Visual Subtitle Feature Enhanced Video Outline Generation—0
VLM: Task-agnostic Video-Language Model Pre-training for Video Understanding—0
VRDFormer: End-to-End Video Visual Relation Detection With Transformers—0
V-STaR: Benchmarking Video-LLMs on Video Spatio-Temporal Reasoning—0
VUDG: A Dataset for Video Understanding Domain Generalization—0
Wasserstein Dependency Measure for Representation Learning—0
Watch and Learn: Leveraging Expert Knowledge and Language for Surgical Video Understanding—0
Weakly Supervised Multiclass Video Segmentation—0
Weakly Supervised Temporal Action Localization via Dual-Prior Collaborative Learning Guided by Multimodal Large Language Models—0
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