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 1001–1025 of 1149 papers

TitleStatusHype
Video Action UnderstandingCode0
Global Self-Attention Networks for Image Recognition—0
Features Understanding in 3D CNNs for Actions Recognition in VideoCode0
Residual Frames with Efficient Pseudo-3D CNN for Human Action Recognition—0
Self-supervised Motion Representation via Scattering Local Motion Cues—0
Detection and Localization of Robotic Tools in Robot-Assisted Surgery Videos Using Deep Neural Networks for Region Proposal and Detection—0
Perceptron Synthesis Network: Rethinking the Action Scale Variances in Videos—0
MovieNet: A Holistic Dataset for Movie Understanding—0
Auto-captions on GIF: A Large-scale Video-sentence Dataset for Vision-language Pre-training—0
Video Understanding as Machine Translation—0
Screencast Tutorial Video UnderstandingCode0
Large Scale Video Representation Learning via Relational Graph Clustering—0
CARPe Posterum: A Convolutional Approach for Real-time Pedestrian Path PredictionCode0
DramaQA: Character-Centered Video Story Understanding with Hierarchical QACode0
HLVU : A New Challenge to Test Deep Understanding of Movies the Way Humans do—0
CATER: A diagnostic dataset for Compositional Actions & TEmporal Reasoning—0
Beyond Instructional Videos: Probing for More Diverse Visual-Textual Grounding on YouTubeCode0
DriftNet: Aggressive Driving Behavior Classification using 3D EfficientNet ArchitectureCode0
Knowledge-Based Visual Question Answering in Videos—0
Real-Time Segmentation Networks should be Latency Aware—0
Context Modulated Dynamic Networks for Actor and Action Video Segmentation with Language Queries—0
Fully Automated Hand Hygiene Monitoring\ Operating Room using 3D Convolutional Neural Network—0
Beyond the Camera: Neural Networks in World Coordinates—0
CTM: Collaborative Temporal Modeling for Action Recognition—0
Cut-Based Graph Learning Networks to Discover Compositional Structure of Sequential Video Data—0
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