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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 576–600 of 1149 papers

TitleStatusHype
Empowering LLMs with Pseudo-Untrimmed Videos for Audio-Visual Temporal Understanding—0
AV-Reasoner: Improving and Benchmarking Clue-Grounded Audio-Visual Counting for MLLMs—0
AVT: Audio-Video Transformer for Multimodal Action Recognition—0
BATMAN: Bilateral Attention Transformer in Motion-Appearance Neighboring Space for Video Object Segmentation—0
BEARCUBS: A benchmark for computer-using web agents—0
BERT for Large-scale Video Segment Classification with Test-time Augmentation—0
Beyond Appearance: Geometric Cues for Robust Video Instance Segmentation—0
Beyond Boxes: Mask-Guided Spatio-Temporal Feature Aggregation for Video Object Detection—0
Beyond still images: Temporal features and input variance resilience—0
Beyond the Camera: Neural Networks in World Coordinates—0
Beyond Training: Dynamic Token Merging for Zero-Shot Video Understanding—0
BioVL-QR: Egocentric Biochemical Vision-and-Language Dataset Using Micro QR Codes—0
Breaking Down Video LLM Benchmarks: Knowledge, Spatial Perception, or True Temporal Understanding?—0
Breaking the Encoder Barrier for Seamless Video-Language Understanding—0
Bridging Audio and Vision: Zero-Shot Audiovisual Segmentation by Connecting Pretrained Models—0
Bringing Image Scene Structure to Video via Frame-Clip Consistency of Object Tokens—0
Building a Mind Palace: Structuring Environment-Grounded Semantic Graphs for Effective Long Video Analysis with LLMs—0
Building Scalable Video Understanding Benchmarks through Sports—0
C^3: Compositional Counterfactual Contrastive Learning for Video-grounded Dialogues—0
CA^2ST: Cross-Attention in Audio, Space, and Time for Holistic Video Recognition—0
CAG-QIL: Context-Aware Actionness Grouping via Q Imitation Learning for Online Temporal Action Localization—0
Camera Calibration and Player Localization in SoccerNet-v2 and Investigation of their Representations for Action Spotting—0
Can CLIP Count Stars? An Empirical Study on Quantity Bias in CLIP—0
FIOVA: A Multi-Annotator Benchmark for Human-Aligned Video Captioning—0
Can MLLMs Guide Weakly-Supervised Temporal Action Localization Tasks?—0
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