most citedMultimodal Federated Learning via Contrastive Representation Ensemble

36 citations · 42 across the 6 of their papers we have counts for

collaborators

6 papers

cs.LG20231 cited

Efficient Algorithms for Generalized Linear Bandits with Heavy-tailed Rewards

Bo Xue, Yimu Wang, Yuanyu Wan +2

This paper investigates the problem of generalized linear bandits with heavy-tailed rewards, whose -th moment is bounded for some . Although there exist methods…

cs.CV20231 cited

InvGC: Robust Cross-Modal Retrieval by Inverse Graph Convolution

Xiangru Jian, Yimu Wang

Over recent decades, significant advancements in cross-modal retrieval are mainly driven by breakthroughs in visual and linguistic modeling. However, a recent study shows that mult…

cs.LG20231 cited

Balance Act: Mitigating Hubness in Cross-Modal Retrieval with Query and Gallery Banks

Yimu Wang, Xiangru Jian, Bo Xue

In this work, we present a post-processing solution to address the hubness problem in cross-modal retrieval, a phenomenon where a small number of gallery data points are frequently…

cs.CV20233 cited

NICE: CVPR 2023 Challenge on Zero-shot Image Captioning

Taehoon Kim, Pyunghwan Ahn, Sangyun Kim +39

In this report, we introduce NICE (New frontiers for zero-shot Image Captioning Evaluation) project and share the results and outcomes of 2023 challenge. This project is designed t…

cs.CL2023

Gradient-Based Word Substitution for Obstinate Adversarial Examples Generation in Language Models

Yimu Wang, Peng Shi, Hongyang Zhang

In this paper, we study the problem of generating obstinate (over-stability) adversarial examples by word substitution in NLP, where input text is meaningfully changed but the mode…

cs.LG202336 cited

Multimodal Federated Learning via Contrastive Representation Ensemble

Qiying Yu, Yang Liu, Yimu Wang +2

With the increasing amount of multimedia data on modern mobile systems and IoT infrastructures, harnessing these rich multimodal data without breaching user privacy becomes a criti…