12 citations · 66 across the 39 of their papers we have counts for
13 papers · 1 filter
Compound Batch Normalization for Long-tailed Image Classification
Lechao Cheng, Chaowei Fang, Dingwen Zhang +2
Significant progress has been made in learning image classification neural networks under long-tail data distribution using robust training algorithms such as data re-sampling, re-…
Text-Guided Mask-free Local Image Retouching
Zerun Liu, Fan Zhang, Jingxuan He +3
In the realm of multi-modality, text-guided image retouching techniques emerged with the advent of deep learning. Most currently available text-guided methods, however, rely on obj…
SASFormer: Transformers for Sparsely Annotated Semantic Segmentation
Hui Su, Yue Ye, Wei Hua +2
Semantic segmentation based on sparse annotation has advanced in recent years. It labels only part of each object in the image, leaving the remainder unlabeled. Most of the existin…
Transferability Estimation Based On Principal Gradient Expectation
Huiyan Qi, Lechao Cheng, Jingjing Chen +4
Transfer learning aims to improve the performance of target tasks by transferring knowledge acquired in source tasks. The standard approach is pre-training followed by fine-tuning…
Combating Noisy Labels in Long-Tailed Image Classification
Chaowei Fang, Lechao Cheng, Huiyan Qi +1
Most existing methods that cope with noisy labels usually assume that the class distributions are well balanced, which has insufficient capacity to deal with the practical scenario…
A Survey of Neural Trees
Haoling Li, Jie Song, Mengqi Xue +4
Neural networks (NNs) and decision trees (DTs) are both popular models of machine learning, yet coming with mutually exclusive advantages and limitations. To bring the best of the…
Dual Domain-Adversarial Learning for Audio-Visual Saliency Prediction
Yingzi Fan, Longfei Han, Yue Zhang +3
Both visual and auditory information are valuable to determine the salient regions in videos. Deep convolution neural networks (CNN) showcase strong capacity in coping with the aud…
ProtoPFormer: Concentrating on Prototypical Parts in Vision Transformers for Interpretable Image Recognition
Mengqi Xue, Qihan Huang, Haofei Zhang +4
Prototypical part network (ProtoPNet) has drawn wide attention and boosted many follow-up studies due to its self-explanatory property for explainable artificial intelligence (XAI)…
Re-Attention Transformer for Weakly Supervised Object Localization
Hui Su, Yue Ye, Zhiwei Chen +2
Weakly supervised object localization is a challenging task which aims to localize objects with coarse annotations such as image categories. Existing deep network approaches are ma…
Long-term Leap Attention, Short-term Periodic Shift for Video Classification
Hao Zhang, Lechao Cheng, Yanbin Hao +1
Video transformer naturally incurs a heavier computation burden than a static vision transformer, as the former processes times longer sequence than the latter under the curren…
KE-RCNN: Unifying Knowledge based Reasoning into Part-level Attribute Parsing
Xuanhan Wang, Jingkuan Song, Xiaojia Chen +3
Part-level attribute parsing is a fundamental but challenging task, which requires the region-level visual understanding to provide explainable details of body parts. Most existing…
Cross-Modality High-Frequency Transformer for MR Image Super-Resolution
Chaowei Fang, Dingwen Zhang, Liang Wang +3
Improving the resolution of magnetic resonance (MR) image data is critical to computer-aided diagnosis and brain function analysis. Higher resolution helps to capture more detailed…
Model LEGO: Creating Models Like Disassembling and Assembling Building Blocks
Jiacong Hu, Jing Gao, Jingwen Ye +4
With the rapid development of deep learning, the increasing complexity and scale of parameters make training a new model increasingly resource-intensive. In this paper, we start fr…