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 1051–1100 of 1149 papers

TitleStatusHype
Recurring the Transformer for Video Action Recognition—0
Relational Space-Time Query in Long-Form Videos—0
Residual Frames with Efficient Pseudo-3D CNN for Human Action Recognition—0
ResNetVLLM -- Multi-modal Vision LLM for the Video Understanding Task—0
Rethinking Image-to-Video Adaptation: An Object-centric Perspective—0
Rethinking Video-Text Understanding: Retrieval from Counterfactually Augmented Data—0
Retrieval-based Video Language Model for Efficient Long Video Question Answering—0
RETTA: Retrieval-Enhanced Test-Time Adaptation for Zero-Shot Video Captioning—0
Revealing Occlusions with 4D Neural Fields—0
Revisiting Kernel Temporal Segmentation as an Adaptive Tokenizer for Long-form Video Understanding—0
ReWind: Understanding Long Videos with Instructed Learnable Memory—0
SA-NET.v2: Real-time vehicle detection from oblique UAV images with use of uncertainty estimation in deep meta-learning—0
SAVEn-Vid: Synergistic Audio-Visual Integration for Enhanced Understanding in Long Video Context—0
Scene-centric Joint Parsing of Cross-view Videos—0
Scene Detection Policies and Keyframe Extraction Strategies for Large-Scale Video Analysis—0
SceneRAG: Scene-level Retrieval-Augmented Generation for Video Understanding—0
MM-SEAL: A Large-scale Video Dataset of Multi-person Multi-grained Spatio-temporally Action Localization—0
SEAL: Semantic Attention Learning for Long Video Representation—0
Search-Map-Search: A Frame Selection Paradigm for Action Recognition—0
Seed1.5-VL Technical Report—0
Selective Structured State-Spaces for Long-Form Video Understanding—0
Self-alignment of Large Video Language Models with Refined Regularized Preference Optimization—0
Self-ReS: Self-Reflection in Large Vision-Language Models for Long Video Understanding—0
Self-supervised Motion Representation via Scattering Local Motion Cues—0
Self-Supervised Object Detection from Egocentric Videos—0
Self-Supervised Spatiotemporal Feature Learning via Video Rotation Prediction—0
Self-supervised video pretraining yields robust and more human-aligned visual representations—0
Semantics-aware Test-time Adaptation for 3D Human Pose Estimation—0
Semantic Segmentation on VSPW Dataset through Masked Video Consistency—0
Semi-Parametric Video-Grounded Text Generation—0
SF2T: Self-supervised Fragment Finetuning of Video-LLMs for Fine-Grained Understanding—0
ShotVL: Human-Centric Highlight Frame Retrieval via Language Queries—0
SkillFormer: Unified Multi-View Video Understanding for Proficiency Estimation—0
Skimming and Scanning for Untrimmed Video Action Recognition—0
Slicing Convolutional Neural Network for Crowd Video Understanding—0
Slot-VLM: SlowFast Slots for Video-Language Modeling—0
SlowFast-LLaVA-1.5: A Family of Token-Efficient Video Large Language Models for Long-Form Video Understanding—0
SpaceVLLM: Endowing Multimodal Large Language Model with Spatio-Temporal Video Grounding Capability—0
Spacewalk-18: A Benchmark for Multimodal and Long-form Procedural Video Understanding—0
Sparse-to-Dense: A Free Lunch for Lossless Acceleration of Video Understanding in LLMs—0
Spatio-Temporal Context for Action Detection—0
Spatio-Temporal Crop Aggregation for Video Representation Learning—0
Spatio-Temporal Vector of Locally Max Pooled Features for Action Recognition in Videos—0
Spatio-Temporal Video Representation Learning for AI Based Video Playback Style Prediction—0
Speeding Up Action Recognition Using Dynamic Accumulation of Residuals in Compressed Domain—0
Spherical World-Locking for Audio-Visual Localization in Egocentric Videos—0
Spoken Moments: Learning Joint Audio-Visual Representations from Video Descriptions—0
SPOT! Revisiting Video-Language Models for Event Understanding—0
Stacked Temporal Attention: Improving First-person Action Recognition by Emphasizing Discriminative Clips—0
STEP: Enhancing Video-LLMs' Compositional Reasoning by Spatio-Temporal Graph-guided Self-Training—0
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