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Found 8 papers, 1 papers with code
EgoExoLearn: A Dataset for Bridging Asynchronous Ego- and Exo-centric View of Procedural Activities in Real World
Along with the videos we record high-quality gaze data and provide detailed multimodal annotations, formulating a playground for modeling the human ability to bridge asynchronous procedural actions from different viewpoints.
Weakly Supervised Temporal Sentence Grounding With Uncertainty-Guided Self-Training
The task of weakly supervised temporal sentence grounding aims at finding the corresponding temporal moments of a language description in the video, given video-language correspondence only at video-level.
DeCo: Decomposition and Reconstruction for Compositional Temporal Grounding via Coarse-To-Fine Contrastive Ranking
Compositional temporal grounding is the task of localizing dense action by using known words combined in novel ways in the form of novel query sentences for the actual grounding.
Compound Prototype Matching for Few-shot Action Recognition
Each global prototype is encouraged to summarize a specific aspect from the entire video, for example, the start/evolution of the action.
Interact Before Align: Leveraging Cross-Modal Knowledge for Domain Adaptive Action Recognition
Different from previous works, we find that the cross-domain alignment can be more effectively done by using cross-modal interaction first.
Stacked Temporal Attention: Improving First-person Action Recognition by Emphasizing Discriminative Clips
Previous works explored to address this problem by applying temporal attention but failed to consider the global context of the full video, which is critical for determining the relatively significant parts.
Leveraging Human Selective Attention for Medical Image Analysis with Limited Training Data
Then we design a novel Auxiliary Attention Block (AAB) to allow information from SAN to be utilized by the backbone encoder to focus on selective areas.
EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge for Action Recognition 2021: Team M3EM Technical Report
In this report, we describe the technical details of our submission to the 2021 EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge for Action Recognition.
