26 citations · 75 across the 13 of their papers we have counts for
5 papers · 1 filter
DATE: Dual Assignment for End-to-End Fully Convolutional Object Detection
Yiqun Chen, Qiang Chen, Qinghao Hu +1
Fully convolutional detectors discard the one-to-many assignment and adopt a one-to-one assigning strategy to achieve end-to-end detection but suffer from the slow convergence issu…
SelfMix: Robust Learning Against Textual Label Noise with Self-Mixup Training
Dan Qiao, Chenchen Dai, Yuyang Ding +4
The conventional success of textual classification relies on annotated data, and the new paradigm of pre-trained language models (PLMs) still requires a few labeled data for downst…
Singular Value Fine-tuning: Few-shot Segmentation requires Few-parameters Fine-tuning
Yanpeng Sun, Qiang Chen, Xiangyu He +7
Freezing the pre-trained backbone has become a standard paradigm to avoid overfitting in few-shot segmentation. In this paper, we rethink the paradigm and explore a new regime: {\e…
MixFormer: Mixing Features across Windows and Dimensions
Qiang Chen, Qiman Wu, Jian Wang +5
While local-window self-attention performs notably in vision tasks, it suffers from limited receptive field and weak modeling capability issues. This is mainly because it performs…
Improving Transferability for Domain Adaptive Detection Transformers
Kaixiong Gong, Shuang Li, Shugang Li +3
DETR-style detectors stand out amongst in-domain scenarios, but their properties in domain shift settings are under-explored. This paper aims to build a simple but effective baseli…