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Found 9 papers, 3 papers with code
Alignment-guided Temporal Attention for Video Action Recognition
Temporal modeling is crucial for various video learning tasks.
Semantic-aligned Fusion Transformer for One-shot Object Detection
One-shot object detection aims at detecting novel objects according to merely one given instance.
Rethinking Minimal Sufficient Representation in Contrastive Learning
It significantly improves the performance of several classic contrastive learning models in downstream tasks.
What Makes for Good Representations for Contrastive Learning
Therefore, we assume the task-relevant information that is not shared between views can not be ignored and theoretically prove that the minimal sufficient representation in contrastive learning is not sufficient for the downstream tasks, which causes performance degradation.
Cross-Stage Transformer for Video Learning
By inserting the proposed cross-stage mechanism in existing spatial and temporal transformer blocks, we build a separable transformer network for video learning based on ViT structure, in which self-attentions and features are progressively aggregated from one block to the next.
Self-Supervised Video Representation Learning with Meta-Contrastive Network
Our method contains two training stages based on model-agnostic meta learning (MAML), each of which consists of a contrastive branch and a meta branch.
Ranked #23 on
Self-Supervised Action Recognition
on UCF101
SSAN: Separable Self-Attention Network for Video Representation Learning
However, spatial correlations and temporal correlations represent different contextual information of scenes and temporal reasoning.
In Defense of the Classification Loss for Person Re-Identification
The recent research for person re-identification has been focused on two trends.
An End-to-End Compression Framework Based on Convolutional Neural Networks
The second CNN, named reconstruction convolutional neural network (RecCNN), is used to reconstruct the decoded image with high-quality in the decoding end.
