activity
20232025
most citedBayesPrompt: Prompting Large-Scale Pre-Trained Language Models on Few-shot Inference via Debiased Domain Abstraction

3 citations · 13 across the 14 of their papers we have counts for

collaborators

14 papers

cs.CV2025

BayesTTA: Continual-Temporal Test-Time Adaptation for Vision-Language Models via Gaussian Discriminant Analysis

Shuang Cui, Jinglin Xu, Yi Li +6

Vision-language models (VLMs) such as CLIP achieve strong zero-shot recognition but degrade significantly under \textit{temporally evolving distribution shifts} common in real-worl…

cs.CV2025

On the Transferability and Discriminability of Repersentation Learning in Unsupervised Domain Adaptation

Wenwen Qiang, Ziyin Gu, Lingyu Si +4

In this paper, we addressed the limitation of relying solely on distribution alignment and source-domain empirical risk minimization in Unsupervised Domain Adaptation (UDA). Our in…

cs.AI2024★ 2 cited

Rethinking Generalizability and Discriminability of Self-Supervised Learning from Evolutionary Game Theory Perspective

Jiangmeng Li, Zehua Zang, Qirui Ji +6

Representations learned by self-supervised approaches are generally considered to possess sufficient generalizability and discriminability. However, we disclose a nontrivial mutual…

cs.CV2024★ 2 cited

DiffDesign: Controllable Diffusion with Meta Prior for Efficient Interior Design Generation

Yuxuan Yang, Tao Geng

Interior design is a complex and creative discipline involving aesthetics, functionality, ergonomics, and materials science. Effective solutions must meet diverse requirements, typ…

cs.CV2024

On the Generalization and Causal Explanation in Self-Supervised Learning

Wenwen Qiang, Zeen Song, Ziyin Gu +4

Self-supervised learning (SSL) methods learn from unlabeled data and achieve high generalization performance on downstream tasks. However, they may also suffer from overfitting to…

cs.CV2024★ 1 cited

On the Discriminability of Self-Supervised Representation Learning

Zeen Song, Wenwen Qiang, Changwen Zheng +2

Self-supervised learning (SSL) has recently shown notable success in various visual tasks. However, in terms of discriminability, SSL is still not on par with supervised learning (…