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Found 6 papers, 5 papers with code
Context Matters: Leveraging Spatiotemporal Metadata for Semi-Supervised Learning on Remote Sensing Images
In this paper, we propose to exploit spatiotemporal metainformation in SSL to improve the quality of pseudo-labels and, therefore, the final model performance.
RGNet: A Unified Clip Retrieval and Grounding Network for Long Videos
Adapting existing short video (5-30 seconds) grounding methods to this problem yields poor performance.
GRAtt-VIS: Gated Residual Attention for Auto Rectifying Video Instance Segmentation
Secondly, we propose a novel inter-instance interaction using gate activation as a mask for self-attention.
Ranked #4 on
Video Instance Segmentation
on YouTube-VIS 2021
(using extra training data)
InstanceFormer: An Online Video Instance Segmentation Framework
We propose three novel components to model short-term and long-term dependency and temporal coherence.
Ranked #5 on
Video Instance Segmentation
on Youtube-VIS 2022 Validation
(using extra training data)
Box Supervised Video Segmentation Proposal Network
Video Object Segmentation (VOS) has been targeted by various fully-supervised and self-supervised approaches.
Prediction of soft proton intensities in the near-Earth space using machine learning
The average correlation between the observed and predicted data is about 56%, which is reasonable in light of the complex dynamics of fast-moving energetic protons in the magnetosphere.
