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Crosspoint: Self-supervised cross-modal contrastive learning for 3d point cloud understanding
Manual annotation of large-scale point cloud dataset for varying tasks such as 3D object
classification, segmentation and detection is often laborious owing to the irregular structure …
classification, segmentation and detection is often laborious owing to the irregular structure …
Temporal query networks for fine-grained video understanding
Our objective in this work is fine-grained classification of actions in untrimmed videos, where
the actions may be temporally extended or may span only a few frames of the video. We cast …
the actions may be temporally extended or may span only a few frames of the video. We cast …
Self-supervised video representation learning by context and motion decoupling
A key challenge in self-supervised video representation learning is how to effectively
capture motion information besides context bias. While most existing works implicitly …
capture motion information besides context bias. While most existing works implicitly …
Self-supervised motion perception for spatiotemporal representation learning
In this study, we propose a novel pretext task and a self-supervised motion perception (SMP)
method for spatiotemporal representation learning. The pretext task is defined as video …
method for spatiotemporal representation learning. The pretext task is defined as video …
Repeat and learn: Self-supervised visual representations learning by Repeated Scene Localization
Large labeled datasets are crucial for video understanding progress. However, the labeling
process is time-consuming, expensive, and tiresome. To overcome this impediment, various …
process is time-consuming, expensive, and tiresome. To overcome this impediment, various …
STCLR: Sparse Temporal Contrastive Learning for Video Representation
Abstract Temporal Contrastive Learning for Video Representation (TCLR) is the first
contrastive framework that uses temporal losses to enforce the temporal distinctiveness of …
contrastive framework that uses temporal losses to enforce the temporal distinctiveness of …
Boosting video representation learning with multi-faceted integration
Video content is multifaceted, consisting of objects, scenes, interactions or actions. The
existing datasets mostly label only one of the facets for model training, resulting in the video …
existing datasets mostly label only one of the facets for model training, resulting in the video …
Temporal transformer networks with self-supervision for action recognition
In recent years, Internet of Things (IoT) has made rapid development, and IoT devices are
develo** toward intelligence. IoT terminal devices represented by surveillance cameras …
develo** toward intelligence. IoT terminal devices represented by surveillance cameras …
Collaboratively Self-supervised Video Representation Learning for Action Recognition
Considering the close connection between action recognition and human pose estimation,
we design a Collaboratively Self-supervised Video Representation (CSVR) learning …
we design a Collaboratively Self-supervised Video Representation (CSVR) learning …
Benchmarking self-supervised video representation learning
Self-supervised learning is an effective way for label-free model pre-training, especially in
the video domain where labeling is expensive. Existing self-supervised works in the video …
the video domain where labeling is expensive. Existing self-supervised works in the video …