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Multi-view action recognition using contrastive learning
In this work, we present a method for RGB-based action recognition using multi-view videos.
We present a supervised contrastive learning framework to learn a feature embedding …
We present a supervised contrastive learning framework to learn a feature embedding …
Mitigating and evaluating static bias of action representations in the background and the foreground
In video action recognition, shortcut static features can interfere with the learning of motion
features, resulting in poor out-of-distribution (OOD) generalization. The video background is …
features, resulting in poor out-of-distribution (OOD) generalization. The video background is …
Uncovering the hidden dynamics of video self-supervised learning under distribution shifts
Video self-supervised learning (VSSL) has made significant progress in recent years.
However, the exact behavior and dynamics of these models under different forms of …
However, the exact behavior and dynamics of these models under different forms of …
Enabling detailed action recognition evaluation through video dataset augmentation
It is well-known in the video understanding community that human action recognition models
suffer from background bias, ie, over-relying on scene cues in making their predictions …
suffer from background bias, ie, over-relying on scene cues in making their predictions …
Enhancing motion visual cues for self-supervised video representation learning
Building the general feature from unlabeled videos is the core of self-supervised video
representation learning. However, recent research on video representation focuses on static …
representation learning. However, recent research on video representation focuses on static …
Attentive spatial-temporal contrastive learning for self-supervised video representation
X Yang, S **ong, K Wu, D Shan, Z **e - Image and Vision Computing, 2023 - Elsevier
Most existing self-supervised works learn video representation by using a single pretext
task. A single pretext task, providing single supervision from unlabeled data, may neglect to …
task. A single pretext task, providing single supervision from unlabeled data, may neglect to …
Frequency selective augmentation for video representation learning
Recent self-supervised video representation learning methods focus on maximizing the
similarity between multiple augmented views from the same video and largely rely on the …
similarity between multiple augmented views from the same video and largely rely on the …
[PDF][PDF] Unifying Video Self-Supervised Learning across Families of Tasks: A Survey
Video self-supervised learning (VideoSSL) offers significant potential for reducing
annotation costs and enhancing a wide range of downstream tasks in video understanding …
annotation costs and enhancing a wide range of downstream tasks in video understanding …