170 citations · 596 across the 62 of their papers we have counts for
12 papers · 1 filter
LamRA: Large Multimodal Model as Your Advanced Retrieval Assistant
Yikun Liu, Pingan Chen, Jiayin Cai +5
With the rapid advancement of multimodal information retrieval, increasingly complex retrieval tasks have emerged. Existing methods predominately rely on task-specific fine-tuning…
LoRKD: Low-Rank Knowledge Decomposition for Medical Foundation Models
Haolin Li, Yuhang Zhou, Ziheng Zhao +5
The widespread adoption of large-scale pre-training techniques has significantly advanced the development of medical foundation models, enabling them to serve as versatile tools ac…
Reprogramming Distillation for Medical Foundation Models
Yuhang Zhou, Siyuan Du, Haolin Li +3
Medical foundation models pre-trained on large-scale datasets have demonstrated powerful versatile capabilities for various tasks. However, due to the gap between pre-training task…
Exploring Training on Heterogeneous Data with Mixture of Low-rank Adapters
Yuhang Zhou, Zihua Zhao, Haolin Li +4
Training a unified model to take multiple targets into account is a trend towards artificial general intelligence. However, how to efficiently mitigate the training conflicts among…
Diversified Batch Selection for Training Acceleration
Feng Hong, Yueming Lyu, Jiangchao Yao +3
The remarkable success of modern machine learning models on large datasets often demands extensive training time and resource consumption. To save cost, a prevalent research line,…
Federated Learning under Partially Class-Disjoint Data via Manifold Reshaping
Ziqing Fan, Jiangchao Yao, Ruipeng Zhang +3
Statistical heterogeneity severely limits the performance of federated learning (FL), motivating several explorations e.g., FedProx, MOON and FedDyn, to alleviate this problem. Des…
Federated Learning with Bilateral Curation for Partially Class-Disjoint Data
Ziqing Fan, Ruipeng Zhang, Jiangchao Yao +3
Partially class-disjoint data (PCDD), a common yet under-explored data formation where each client contributes a part of classes (instead of all classes) of samples, severely chall…
Locally Estimated Global Perturbations are Better than Local Perturbations for Federated Sharpness-aware Minimization
Ziqing Fan, Shengchao Hu, Jiangchao Yao +4
In federated learning (FL), the multi-step update and data heterogeneity among clients often lead to a loss landscape with sharper minima, degenerating the performance of the resul…
Domain-Inspired Sharpness-Aware Minimization Under Domain Shifts
Ruipeng Zhang, Ziqing Fan, Jiangchao Yao +2
This paper presents a Domain-Inspired Sharpness-Aware Minimization (DISAM) algorithm for optimization under domain shifts. It is motivated by the inconsistent convergence degree of…
Mitigating Noisy Correspondence by Geometrical Structure Consistency Learning
Zihua Zhao, Mengxi Chen, Tianjie Dai +4
Noisy correspondence that refers to mismatches in cross-modal data pairs, is prevalent on human-annotated or web-crawled datasets. Prior approaches to leverage such data mainly con…
Low-Rank Knowledge Decomposition for Medical Foundation Models
Yuhang Zhou, Haolin Li, Siyuan Du +3
The popularity of large-scale pre-training has promoted the development of medical foundation models. However, some studies have shown that although foundation models exhibit stron…
ReMamber: Referring Image Segmentation with Mamba Twister
Yuhuan Yang, Chaofan Ma, Jiangchao Yao +3
Referring Image Segmentation~(RIS) leveraging transformers has achieved great success on the interpretation of complex visual-language tasks. However, the quadratic computation cos…