12 citations · 66 across the 39 of their papers we have counts for
14 papers · 1 filter
Progressive Feature Self-reinforcement for Weakly Supervised Semantic Segmentation
Jingxuan He, Lechao Cheng, Chaowei Fang +3
Compared to conventional semantic segmentation with pixel-level supervision, Weakly Supervised Semantic Segmentation (WSSS) with image-level labels poses the challenge that it alwa…
GP-NeRF: Generalized Perception NeRF for Context-Aware 3D Scene Understanding
Hao Li, Dingwen Zhang, Yalun Dai +5
Applying NeRF to downstream perception tasks for scene understanding and representation is becoming increasingly popular. Most existing methods treat semantic prediction as an addi…
ScrollTimes: Tracing the Provenance of Paintings as a Window into History
Wei Zhang, Wong Kam-Kwai, Yitian Chen +6
The study of cultural artifact provenance, tracing ownership and preservation, holds significant importance in archaeology and art history. Modern technology has advanced this fiel…
Improving Knowledge Distillation via Regularizing Feature Norm and Direction
Yuzhu Wang, Lechao Cheng, Manni Duan +3
Knowledge distillation (KD) exploits a large well-trained model (i.e., teacher) to train a small student model on the same dataset for the same task. Treating teacher features as k…
ViT-Calibrator: Decision Stream Calibration for Vision Transformer
Lin Chen, Zhijie Jia, Tian Qiu +4
A surge of interest has emerged in utilizing Transformers in diverse vision tasks owing to its formidable performance. However, existing approaches primarily focus on optimizing in…
Masked Collaborative Contrast for Weakly Supervised Semantic Segmentation
Fangwen Wu, Jingxuan He, Yufei Yin +3
This study introduces an efficacious approach, Masked Collaborative Contrast (MCC), to highlight semantic regions in weakly supervised semantic segmentation. MCC adroitly draws ins…
Calibrating Undisciplined Over-Smoothing in Transformer for Weakly Supervised Semantic Segmentation
Lechao Cheng, Zerun Liu, Jingxuan He +3
Weakly supervised semantic segmentation (WSSS) has recently attracted considerable attention because it requires fewer annotations than fully supervised approaches, making it espec…
Life Regression based Patch Slimming for Vision Transformers
Jiawei Chen, Lin Chen, Jiang Yang +4
Vision transformers have achieved remarkable success in computer vision tasks by using multi-head self-attention modules to capture long-range dependencies within images. However,…
Propheter: Prophetic Teacher Guided Long-Tailed Distribution Learning
Wenxiang Xu, Yongcheng Jing, Linyun Zhou +4
The problem of deep long-tailed learning, a prevalent challenge in the realm of generic visual recognition, persists in a multitude of real-world applications. To tackle the heavil…
Generalization Matters: Loss Minima Flattening via Parameter Hybridization for Efficient Online Knowledge Distillation
Tianli Zhang, Mengqi Xue, Jiangtao Zhang +5
Most existing online knowledge distillation(OKD) techniques typically require sophisticated modules to produce diverse knowledge for improving students' generalization ability. In…
Team DETR: Guide Queries as a Professional Team in Detection Transformers
Tian Qiu, Linyun Zhou, Wenxiang Xu +3
Recent proposed DETR variants have made tremendous progress in various scenarios due to their streamlined processes and remarkable performance. However, the learned queries usually…
Model Doctor for Diagnosing and Treating Segmentation Error
Zhijie Jia, Lin Chen, Kaiwen Hu +3
Despite the remarkable progress in semantic segmentation tasks with the advancement of deep neural networks, existing U-shaped hierarchical typical segmentation networks still suff…
Revisiting Long-tailed Image Classification: Survey and Benchmarks with New Evaluation Metrics
Chaowei Fang, Dingwen Zhang, Wen Zheng +4
Recently, long-tailed image classification harvests lots of research attention, since the data distribution is long-tailed in many real-world situations. Piles of algorithms are de…
Boosting Low-Data Instance Segmentation by Unsupervised Pre-training with Saliency Prompt
Hao Li, Dingwen Zhang, Nian Liu +5
Recently, inspired by DETR variants, query-based end-to-end instance segmentation (QEIS) methods have outperformed CNN-based models on large-scale datasets. Yet they would lose eff…