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 926–950 of 1149 papers

TitleStatusHype
Memory-Guided Semantic Learning Network for Temporal Sentence Grounding—0
VRDFormer: End-to-End Video Visual Relation Detection With Transformers—0
YouMVOS: An Actor-Centric Multi-Shot Video Object Segmentation Dataset—0
Improving Video Model Transfer With Dynamic Representation Learning—0
UBoCo: Unsupervised Boundary Contrastive Learning for Generic Event Boundary Detection—0
Recurring the Transformer for Video Action Recognition—0
Exploiting Long-Term Dependencies for Generating Dynamic Scene GraphsCode0
Discrete neural representations for explainable anomaly detection—0
Auto-X3D: Ultra-Efficient Video Understanding via Finer-Grained Neural Architecture Search—0
Suppressing Static Visual Cues via Normalizing Flows for Self-Supervised Video Representation LearningCode0
Stacked Temporal Attention: Improving First-person Action Recognition by Emphasizing Discriminative Clips—0
LiVLR: A Lightweight Visual-Linguistic Reasoning Framework for Video Question Answering—0
UBoCo : Unsupervised Boundary Contrastive Learning for Generic Event Boundary Detection—0
Fill-in-the-Blank: A Challenging Video Understanding Evaluation Framework—0
Occluded Video Instance Segmentation: Dataset and ICCV 2021 Challenge—0
Re-ID-AR: Improved Person Re-identification in Video via Joint Weakly Supervised Action RecognitionCode0
Gradient Frequency Modulation for Visually Explaining Video Understanding Models—0
Distantly Supervised Semantic Text Detection and Recognition for Broadcast Sports Videos Understanding—0
Leveraging Local Temporal Information for Multimodal Scene Classification—0
Can't Fool Me: Adversarially Robust Transformer for Video Understanding—0
NoisyActions2M: A Multimedia Dataset for Video Understanding from Noisy LabelsCode0
CLIP4Caption: CLIP for Video Caption—0
TAda! Temporally-Adaptive Convolutions for Video UnderstandingCode0
Toward a Human-Level Video Understanding Intelligence—0
Efficient Modelling Across Time of Human Actions and Interactions—0
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