most citedA New Knowledge Distillation Network for Incremental Few-Shot Surface Defect Detection

3 citations · 4 across the 7 of their papers we have counts for

collaborators

7 papers

cs.SD2023

LooPy: A Research-Friendly Mix Framework for Music Information Retrieval on Electronic Dance Music

Xinyu Li

Music information retrieval (MIR) has gone through an explosive development with the advancement of deep learning in recent years. However, music genres like electronic dance music…

quant-ph2023

Implementing arbitrary quantum operations via quantum walks on a cycle graph

Jia-Yi Lin, Xin-Yu Li, Yu-Hao Shao +2

The quantum circuit model is the most commonly used model for implementing quantum computers and quantum neural networks whose essential tasks are to realize certain unitary operat…

cs.CV2023

Revisiting Multimodal Representation in Contrastive Learning: From Patch and Token Embeddings to Finite Discrete Tokens

Yuxiao Chen, Jianbo Yuan, Yu Tian +5

Contrastive learning-based vision-language pre-training approaches, such as CLIP, have demonstrated great success in many vision-language tasks. These methods achieve cross-modal a…

cs.SD2023

CAT: Causal Audio Transformer for Audio Classification

Xiaoyu Liu, Hanlin Lu, Jianbo Yuan +1

The attention-based Transformers have been increasingly applied to audio classification because of their global receptive field and ability to handle long-term dependency. However,…

cs.CV20231 cited

Nearest-Neighbor Inter-Intra Contrastive Learning from Unlabeled Videos

David Fan, Deyu Yang, Xinyu Li +2

Contrastive learning has recently narrowed the gap between self-supervised and supervised methods in image and video domain. State-of-the-art video contrastive learning methods suc…

cs.CV20223 cited

A New Knowledge Distillation Network for Incremental Few-Shot Surface Defect Detection

Chen Sun, Liang Gao, Xinyu Li +1

Surface defect detection is one of the most essential processes for industrial quality inspection. Deep learning-based surface defect detection methods have shown great potential.…