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Liang Yin

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4 papers

2 author rows

ICLR Conference 2025 Conference Paper

ContraDiff: Planning Towards High Return States via Contrastive Learning

Yixiang Shan
Zhengbang Zhu
Ting Long
Qifan Liang
Yi Chang 0001
Weinan Zhang 0001
Liang Yin

The performance of offline reinforcement learning (RL) is sensitive to the proportion of high-return trajectories in the offline dataset. However, in many simulation environments and real-world scenarios, there are large ratios of low-return trajectories rather than high-return trajectories, which makes learning an efficient policy challenging. In this paper, we propose a method called Contrastive Diffuser (ContraDiff) to make full use of low-return trajectories and improve the performance of offline RL algorithms. Specifically, ContraDiff groups the states of trajectories in the offline dataset into high-return states and low-return states and treats them as positive and negative samples correspondingly. Then, it designs a contrastive mechanism to pull the planned trajectory of an agent toward high-return states and push them away from low-return states. Through the contrast mechanism, trajectories with low returns can serve as negative examples for policy learning, guiding the agent to avoid areas associated with low returns and achieve better performance. Through the contrast mechanism, trajectories with low returns provide a ``counteracting force'' guides the agent to avoid areas associated with low returns and achieve better performance. Experiments on 27 sub-optimal datasets demonstrate the effectiveness of our proposed method. Our code is publicly available at https://github.com/Looomo/contradiff.

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NeurIPS Conference 2025 Conference Paper

MSTAR: Box-free Multi-query Scene Text Retrieval with Attention Recycling

Liang Yin
Xudong Xie
Zhang Li
Xiang Bai
Yuliang Liu

Scene text retrieval has made significant progress with the assistance of accurate text localization. However, existing approaches typically require costly bounding box annotations for training. Besides, they mostly adopt a customized retrieval strategy but struggle to unify various types of queries to meet diverse retrieval needs. To address these issues, we introduce Multi-query Scene Text retrieval with Attention Recycling (MSTAR), a box-free approach for scene text retrieval. It incorporates progressive vision embedding to dynamically capture the multi-grained representation of texts and harmonizes free-style text queries with style-aware instructions. Additionally, a multi-instance matching module is integrated to enhance vision-language alignment. Furthermore, we build the Multi-Query Text Retrieval (MQTR) dataset, the first benchmark designed to evaluate the multi-query scene text retrieval capability of models, comprising four query types and $16k$ images. Extensive experiments demonstrate the superiority of our method across seven public datasets and the MQTR dataset. Notably, MSTAR marginally surpasses the previous state-of-the-art model by 6. 4\% in MAP on Total-Text while eliminating box annotation costs. Moreover, on the MQTR benchmark, MSTAR significantly outperforms the previous models by an average of 8. 5\%. The code and datasets are available at \href{https: //github. com/yingift/MSTAR}{https: //github. com/yingift/MSTAR}.

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AAAI Conference 2013 Conference Paper

A Maximum K-Min Approach for Classification

Mingzhi Dong
Liang Yin

In this paper, a general Maximum K-Min approach for classification is proposed, which focuses on maximizing the gain obtained by the K worst-classified instances while ignoring the remaining ones. To make the original optimization problem with combinational constraints computationally tractable, the optimization techniques are adopted and a general compact representation lemma is summarized. Based on the lemma, a Nonlinear Maximum K-Min (NMKM) classifier is presented and the experiment results demonstrate the superior performance of the Maximum K-Min Approach.

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AAAI Conference 2013 Conference Paper

A Maximum K-Min Approach for Classification

Mingzhi Dong
Liang Yin
Weihong Deng
Li Shang
Jun Guo
Honggang Zhang

In this paper, a general Maximum K-Min approach for classiﬁcation is proposed. With the physical meaning of optimizing the classiﬁcation conﬁdence of the K worst instances, Maximum K-Min Gain/Minimum K- Max Loss (MKM) criterion is introduced. To make the original optimization problem with combinational number of constraints computationally tractable, the optimization techniques are adopted and a general compact representation lemma for MKM Criterion is summarized. Based on the lemma, a Nonlinear Maximum K- Min (NMKM) classiﬁer and a Semi-supervised Maximum K-Min (SMKM) classiﬁer are presented for traditional classiﬁcation task and semi-supervised classi- ﬁcation task respectively. Based on the experiment results of publicly available datasets, our Maximum K- Min methods have achieved competitive performance when comparing against Hinge Loss classiﬁers.

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