3 citations · 5 across the 13 of their papers we have counts for
13 papers
Cut-ViT: Task-Specific Model Pruning via Gram Anchoring Subspace Consistency
Jianjian Yin, Liulei Li, Tao Chen +3
Pruning visual foundation models has attracted considerable attention. However, existing methods focus on rigid point-to-point token alignment on a single dataset for pruning, suff…
PEARL: Geometry Aligns Semantics for Training-Free Open-Vocabulary Semantic Segmentation
Gensheng Pei, Xiruo Jiang, Xinhao Cai +3
Training-free open-vocabulary semantic segmentation (OVSS) promises rapid adaptation to new label sets without retraining. Yet, many methods rely on heavy post-processing or handle…
Beyond Quadratic: Linear-Time Change Detection with RWKV
Zhenyu Yang, Gensheng Pei, Tao Chen +4
Existing paradigms for remote sensing change detection are caught in a trade-off: CNNs excel at efficiency but lack global context, while Transformers capture long-range dependenci…
A Formally Verified Procedure for Width Inference in FIRRTL
Keyin Wang, Xiaomu Shi, Jiaxiang Liu +4
FIRRTL is an intermediate representation language for Register Transfer Level (RTL) hardware designs. In FIRRTL programs, the bit widths of many components are not specified explic…
Unbiased Object Detection Beyond Frequency with Visually Prompted Image Synthesis
Xinhao Cai, Liulei Li, Gensheng Pei +4
This paper presents a generation-based debiasing framework for object detection. Prior debiasing methods are often limited by the representation diversity of samples, while naive g…
Semi-supervised Semantic Segmentation with Multi-Constraint Consistency Learning
Jianjian Yin, Tao Chen, Gensheng Pei +3
Consistency regularization has prevailed in semi-supervised semantic segmentation and achieved promising performance. However, existing methods typically concentrate on enhancing t…
Seeing What Matters: Empowering CLIP with Patch Generation-to-Selection
Gensheng Pei, Tao Chen, Yujia Wang +4
The CLIP model has demonstrated significant advancements in aligning visual and language modalities through large-scale pre-training on image-text pairs, enabling strong zero-shot…
Relating CNN-Transformer Fusion Network for Change Detection
Yuhao Gao, Gensheng Pei, Mengmeng Sheng +3
While deep learning, particularly convolutional neural networks (CNNs), has revolutionized remote sensing (RS) change detection (CD), existing approaches often miss crucial feature…
Foster Adaptivity and Balance in Learning with Noisy Labels
Mengmeng Sheng, Zeren Sun, Tao Chen +3
Label noise is ubiquitous in real-world scenarios, posing a practical challenge to supervised models due to its effect in hurting the generalization performance of deep neural netw…
Knowledge Transfer with Simulated Inter-Image Erasing for Weakly Supervised Semantic Segmentation
Tao Chen, XiRuo Jiang, Gensheng Pei +3
Though adversarial erasing has prevailed in weakly supervised semantic segmentation to help activate integral object regions, existing approaches still suffer from the dilemma of u…
VideoMAC: Video Masked Autoencoders Meet ConvNets
Gensheng Pei, Tao Chen, Xiruo Jiang +3
Recently, the advancement of self-supervised learning techniques, like masked autoencoders (MAE), has greatly influenced visual representation learning for images and videos. Never…
Spatial Structure Constraints for Weakly Supervised Semantic Segmentation
Tao Chen, Yazhou Yao, Xingguo Huang +3
The image-level label has prevailed in weakly supervised semantic segmentation tasks due to its easy availability. Since image-level labels can only indicate the existence or absen…
Adaptive Integration of Partial Label Learning and Negative Learning for Enhanced Noisy Label Learning
Mengmeng Sheng, Zeren Sun, Zhenhuang Cai +3
There has been significant attention devoted to the effectiveness of various domains, such as semi-supervised learning, contrastive learning, and meta-learning, in enhancing the pe…