arXiv:2210.00174
공식 코드 · Papers with Code
Improving ProtoNet for Few-Shot Video Object Recognition: Winner of ORBIT Challenge 2022
원문 초록
In this work, we present the winning solution for ORBIT Few-Shot Video Object Recognition Challenge 2022. Built upon the ProtoNet baseline, the performance of our method is improved with three effective techniques. These techniques include the embedding adaptation, the uniform video clip sampler and the invalid frame detection. In addition, we re-factor and re-implement the official codebase to encourage modularity, compatibility and improved performance. Our implementation accelerates the data loading in both training and testing.
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ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark Evaluation
SASep: Saliency-Aware Structured Separation of Geometry and Feature for Open Set Learning on Point Clouds
STSBench: A Spatio-temporal Scenario Benchmark for Multi-modal Large Language Models in Autonomous Driving
InstructSAM: A Training-Free Framework for Instruction-Oriented Remote Sensing Object Recognition
Improving Unsupervised Task-driven Models of Ventral Visual Stream via Relative Position Predictivity
Simple Semi-supervised Knowledge Distillation from Vision-Language Models via $\mathbf{\texttt{D}}$ual-$\mathbf{\texttt{H}}$ead $\mathbf{\texttt{O}}$ptimization
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