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Found 5 papers, 5 papers with code
DIOD: Self-Distillation Meets Object Discovery
In the present paper we propose DIOD (self DIstillation meets Object Discovery) the first method that places the motion-guided object discovery within a framework of continuous improvement through knowledge distillation providing solutions to existing limitations (i) DIOD robustly eliminates the noise present in the exploited motion maps providing accurate motion-supervision (ii) DIOD leverages the discovered objects within an iterative pseudo-labeling framework enriching the initial motion-supervision with static objects which results in a cost-efficient increase in performance.
SoccerNet 2023 Challenges Results
More information on the tasks, challenges, and leaderboards are available on https://www. soccer-net. org.
COMEDIAN: Self-Supervised Learning and Knowledge Distillation for Action Spotting using Transformers
We present COMEDIAN, a novel pipeline to initialize spatiotemporal transformers for action spotting, which involves self-supervised learning and knowledge distillation.
Ranked #1 on
Action Spotting
on SoccerNet-v2
Similarity Contrastive Estimation for Image and Video Soft Contrastive Self-Supervised Learning
A good data representation should contain relations between the instances, or semantic similarity and dissimilarity, that contrastive learning harms by considering all negatives as noise.
Ranked #1 on
Self-supervised Video Retrieval
on HMDB51
Similarity Contrastive Estimation for Self-Supervised Soft Contrastive Learning
To circumvent this issue, we propose a novel formulation of contrastive learning using semantic similarity between instances called Similarity Contrastive Estimation (SCE).
Ranked #72 on
Self-Supervised Image Classification
on ImageNet
