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 301–325 of 1149 papers

TitleStatusHype
FocusChat: Text-guided Long Video Understanding via Spatiotemporal Information Filtering—0
ShotVL: Human-Centric Highlight Frame Retrieval via Language Queries—0
CG-Bench: Clue-grounded Question Answering Benchmark for Long Video Understanding—0
Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video RecognitionCode2
Overview of TREC 2024 Medical Video Question Answering (MedVidQA) Track—0
IQViC: In-context, Question Adaptive Vision Compressor for Long-term Video Understanding LMMs—0
Apollo: An Exploration of Video Understanding in Large Multimodal Models—0
B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal TokensCode0
VCA: Video Curious Agent for Long Video Understanding—0
ViCaS: A Dataset for Combining Holistic and Pixel-level Video Understanding using Captions with Grounded Segmentation—0
PVC: Progressive Visual Token Compression for Unified Image and Video Processing in Large Vision-Language Models—0
Neptune: The Long Orbit to Benchmarking Long Video UnderstandingCode2
COEF-VQ: Cost-Efficient Video Quality Understanding through a Cascaded Multimodal LLM Framework—0
3DSRBench: A Comprehensive 3D Spatial Reasoning Benchmark—0
Multi-Scale Contrastive Learning for Video Temporal Grounding—0
GEXIA: Granularity Expansion and Iterative Approximation for Scalable Multi-grained Video-language Learning—0
Towards Long Video Understanding via Fine-detailed Video Story Generation—0
Beyond Boxes: Mask-Guided Spatio-Temporal Feature Aggregation for Video Object Detection—0
LinVT: Empower Your Image-level Large Language Model to Understand VideosCode2
Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling—0
Espresso: High Compression For Rich Extraction From Videos for Your Vision-Language Model—0
VisionZip: Longer is Better but Not Necessary in Vision Language ModelsCode3
VidHalluc: Evaluating Temporal Hallucinations in Multimodal Large Language Models for Video Understanding—0
AIM: Adaptive Inference of Multi-Modal LLMs via Token Merging and PruningCode2
Streaming Detection of Queried Event StartCode0
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