175 citations · 295 across the 13 of their papers we have counts for
14 papers
Breaking Information Islands in Sparse Tuning via Small-World Connectivity
Xianchao Guan, Zijun Xiong, Yifeng Wang +5
Sparse tuning is widely used to adapt large language models due to its parameter efficiency. However, its effectiveness depends not only on the number of trainable parameters, but…
Denser Better: Limits of On-Policy Self-Distillation for Continual Post-Training
Meng Wang, Haohan Zhao, Wenzhuo Liu +7
Continual post-training enables foundation models to acquire new knowledge while preserving existing capabilities. Recent work suggests that on-policy learning can mitigate forgett…
Self-Consolidation for Self-Evolving Agents
Hongzhuo Yu, Fei Zhu, Guo-Sen Xie +1
While large language model (LLM) agents have demonstrated impressive problem-solving capabilities, they typically operate as static systems, lacking the ability to evolve through l…
CDIMC-net: Cognitive Deep Incomplete Multi-view Clustering Network
Jie Wen, Zheng Zhang, Yong Xu +3
In recent years, incomplete multi-view clustering, which studies the challenging multi-view clustering problem on missing views, has received growing research interests. Although a…
Attack-Augmentation Mixing-Contrastive Skeletal Representation Learning
Binqian Xu, Xiangbo Shu, Jiachao Zhang +2
Contrastive learning, relying on effective positive and negative sample pairs, is beneficial to learn informative skeleton representations in unsupervised skeleton-based action rec…
Dynamic Prototype Convolution Network for Few-Shot Semantic Segmentation
Jie Liu, Yanqi Bao, Guo-Sen Xie +3
The key challenge for few-shot semantic segmentation (FSS) is how to tailor a desirable interaction among support and query features and/or their prototypes, under the episodic tra…
MSDN: Mutually Semantic Distillation Network for Zero-Shot Learning
Shiming Chen, Ziming Hong, Guo-Sen Xie +5
The key challenge of zero-shot learning (ZSL) is how to infer the latent semantic knowledge between visual and attribute features on seen classes, and thus achieving a desirable kn…
TransZero: Attribute-guided Transformer for Zero-Shot Learning
Shiming Chen, Ziming Hong, Yang Liu +6
Zero-shot learning (ZSL) aims to recognize novel classes by transferring semantic knowledge from seen classes to unseen ones. Semantic knowledge is learned from attribute descripti…
TransZero++: Cross Attribute-Guided Transformer for Zero-Shot Learning
Shiming Chen, Ziming Hong, Wenjin Hou +6
Zero-shot learning (ZSL) tackles the novel class recognition problem by transferring semantic knowledge from seen classes to unseen ones. Existing attention-based models have strug…
HSVA: Hierarchical Semantic-Visual Adaptation for Zero-Shot Learning
Shiming Chen, Guo-Sen Xie, Yang Liu +5
Zero-shot learning (ZSL) tackles the unseen class recognition problem, transferring semantic knowledge from seen classes to unseen ones. Typically, to guarantee desirable knowledge…
Non-Salient Region Object Mining for Weakly Supervised Semantic Segmentation
Yazhou Yao, Tao Chen, Guosen Xie +5
Semantic segmentation aims to classify every pixel of an input image. Considering the difficulty of acquiring dense labels, researchers have recently been resorting to weak labels…
Kernelized Similarity Learning and Embedding for Dynamic Texture Synthesis
Shiming Chen, Peng Zhang, Guo-Sen Xie +4
Dynamic texture (DT) exhibits statistical stationarity in the spatial domain and stochastic repetitiveness in the temporal dimension, indicating that different frames of DT possess…
SADIH: Semantic-Aware DIscrete Hashing
Zheng Zhang, Guo-sen Xie, Yang Li +2
Due to its low storage cost and fast query speed, hashing has been recognized to accomplish similarity search in large-scale multimedia retrieval applications. Particularly supervi…
Hybrid CNN and Dictionary-Based Models for Scene Recognition and Domain Adaptation
Guo-Sen Xie, Xu-Yao Zhang, Shuicheng Yan +1
Convolutional neural network (CNN) has achieved state-of-the-art performance in many different visual tasks. Learned from a large-scale training dataset, CNN features are much more…