Search Results for author: Wenbin An

Found 7 papers, 5 papers with code

DreamSalon: A Staged Diffusion Framework for Preserving Identity-Context in Editable Face Generation

no code implementations28 Mar 2024 Haonan Lin, Mengmeng Wang, Yan Chen, Wenbin An, Yuzhe Yao, Guang Dai, Qianying Wang, Yong liu, Jingdong Wang

While large-scale pre-trained text-to-image models can synthesize diverse and high-quality human-centered images, novel challenges arise with a nuanced task of "identity fine editing": precisely modifying specific features of a subject while maintaining its inherent identity and context.

Transfer and Alignment Network for Generalized Category Discovery

1 code implementation27 Dec 2023 Wenbin An, Feng Tian, Wenkai Shi, Yan Chen, Yaqiang Wu, Qianying Wang, Ping Chen

On the other hand, we use these prototypes to calibrate noisy prototypes estimated from unlabeled data based on category similarities, which allows for more accurate estimation of prototypes for novel categories that can be used as reliable learning targets later.

Attribute Representation Learning +1

Generalized Category Discovery with Large Language Models in the Loop

no code implementations18 Dec 2023 Wenbin An, Wenkai Shi, Feng Tian, Haonan Lin, Qianying Wang, Yaqiang Wu, Mingxiang Cai, Luyan Wang, Yan Chen, Haiping Zhu, Ping Chen

Then we propose a Scalable Query strategy to allow LLMs to choose true neighbors of the selected samples from multiple candidate samples.

Active Learning Contrastive Learning

A Diffusion Weighted Graph Framework for New Intent Discovery

1 code implementation24 Oct 2023 Wenkai Shi, Wenbin An, Feng Tian, Qinghua Zheng, Qianying Wang, Ping Chen

New Intent Discovery (NID) aims to recognize both new and known intents from unlabeled data with the aid of limited labeled data containing only known intents.

Contrastive Learning Intent Discovery

DNA: Denoised Neighborhood Aggregation for Fine-grained Category Discovery

1 code implementation16 Oct 2023 Wenbin An, Feng Tian, Wenkai Shi, Yan Chen, Qinghua Zheng, Qianying Wang, Ping Chen

Specifically, we retrieve k-nearest neighbors of a query as its positive keys to capture semantic similarities between data and then aggregate information from the neighbors to learn compact cluster representations, which can make fine-grained categories more separatable.

Representation Learning

Generalized Category Discovery with Decoupled Prototypical Network

2 code implementations28 Nov 2022 Wenbin An, Feng Tian, Qinghua Zheng, Wei Ding, Qianying Wang, Ping Chen

Furthermore, the coupled training approach prevents these models transferring category-specific knowledge explicitly from labeled data to unlabeled data, which can lose high-level semantic information and impair model performance.

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