77 citations · 83 across the 14 of their papers we have counts for
16 papers
Synergistic Dual-Branch Adaptation for Multi-modal Generalized Category Discovery
Yuxun Qu, Minyu Zhou, Yongqiang Tang +2
Generalized Category Discovery (GCD) aims to classify old categories and discover new ones from unlabeled data. Recent multi-modal approaches introduce retrieved or synthesized tex…
GradMAP: Faster Layer Pruning with Gradient Metric and Projection Compensation
Hao Liu, Guangyan Li, Wensheng Zhang +1
Large Language Models (LLMs) exhibit strong reasoning abilities, but their high computational costs limit their practical deployment. Recent studies reveal significant redundancy i…
Towards Open-World Retrieval-Augmented Generation on Knowledge Graph: A Multi-Agent Collaboration Framework
Jiasheng Xu, Mingda Li, Yongqiang Tang +2
Large Language Models (LLMs) have demonstrated strong capabilities in web search and reasoning. However, their dependence on static training corpora makes them prone to factual err…
MGAA: Multi-Granular Adaptive Allocation fof Low-Rank Compression of LLMs
Guangyan Li, Yongqiang Tang, Wensheng Zhang
The enormous parameter scale of large language models (LLMs) has made model compression a research hotspot, which aims to alleviate computational resource demands during deployment…
AdaptGCD: Multi-Expert Adapter Tuning for Generalized Category Discovery
Yuxun Qu, Yongqiang Tang, Chenyang Zhang +1
Different from the traditional semi-supervised learning paradigm that is constrained by the close-world assumption, Generalized Category Discovery (GCD) presumes that the unlabeled…
LoRAP: Transformer Sub-Layers Deserve Differentiated Structured Compression for Large Language Models
Guangyan Li, Yongqiang Tang, Wensheng Zhang
Large language models (LLMs) show excellent performance in difficult tasks, but they often require massive memories and computational resources. How to reduce the parameter scale o…
Cross-Stream Contrastive Learning for Self-Supervised Skeleton-Based Action Recognition
Ding Li, Yongqiang Tang, Zhizhong Zhang +1
Self-supervised skeleton-based action recognition enjoys a rapid growth along with the development of contrastive learning. The existing methods rely on imposing invariance to augm…
Cross-Domain Label Propagation for Domain Adaptation with Discriminative Graph Self-Learning
Lei Tian, Yongqiang Tang, Liangchen Hu +1
Domain adaptation manages to transfer the knowledge of well-labeled source data to unlabeled target data. Many recent efforts focus on improving the prediction accuracy of target p…
Constrained Maximum Cross-Domain Likelihood for Domain Generalization
Jianxin Lin, Yongqiang Tang, Junping Wang +1
As a recent noticeable topic, domain generalization aims to learn a generalizable model on multiple source domains, which is expected to perform well on unseen test domains. Great…
Mitigating Both Covariate and Conditional Shift for Domain Generalization
Jianxin Lin, Yongqiang Tang, Junping Wang +1
Domain generalization (DG) aims to learn a model on several source domains, hoping that the model can generalize well to unseen target domains. The distribution shift between domai…
Attentive pooling for Group Activity Recognition
Ding Li, Yuan Xie, Wensheng Zhang +2
In group activity recognition, hierarchical framework is widely adopted to represent the relationships between individuals and their corresponding group, and has achieved promising…
Active Learning with Effective Scoring Functions for Semi-Supervised Temporal Action Localization
Ding Li, Xuebing Yang, Yongqiang Tang +2
Temporal Action Localization (TAL) aims to predict both action category and temporal boundary of action instances in untrimmed videos, i.e., start and end time. Fully-supervised so…
Deep Multi-View Semi-Supervised Clustering with Sample Pairwise Constraints
Rui Chen, Yongqiang Tang, Wensheng Zhang +1
Multi-view clustering has attracted much attention thanks to the capacity of multi-source information integration. Although numerous advanced methods have been proposed in past dec…
Learnable Faster Kernel-PCA for Nonlinear Fault Detection: Deep Autoencoder-Based Realization
Zelin Ren, Xuebing Yang, Yuchen Jiang +1
Kernel principal component analysis (KPCA) is a well-recognized nonlinear dimensionality reduction method that has been widely used in nonlinear fault detection tasks. As a kernel…
Multi-scale 2D Representation Learning for weakly-supervised moment retrieval
Ding Li, Rui Wu, Yongqiang Tang +2
Video moment retrieval aims to search the moment most relevant to a given language query. However, most existing methods in this community often require temporal boundary annotatio…
Domain Adaptation by Class Centroid Matching and Local Manifold Self-Learning
Lei Tian, Yongqiang Tang, Liangchen Hu +2
Domain adaptation has been a fundamental technology for transferring knowledge from a source domain to a target domain. The key issue of domain adaptation is how to reduce the dist…