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Jinhai Xiang

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AAAI Conference 2026 Conference Paper

Towards Understanding In-Context Learning of Transformers Under Non-I.I.D. Scenarios

Qilu Shen
Yingjie Wang
Jinhai Xiang

Understanding the generalization behavior of in-context learning (ICL) in Transformers remains a fundamental challenge, as most existing theoretical analyses are based on the assumption that data are independently and identically distributed (i.i.d.), an assumption that often does not hold in practice. Motivated by the theoretical insight that ICL operates similarly to gradient-based optimization, we leverage the concept of gradient stability to establish generalization error bounds for ICL under a general non-i.i.d. setting. Our analysis shows that two factors play a central role in ICL generalization: the number of demonstrations in the prompt and their distributional alignment with the query. In particular, increasing the number of demonstrations and improving their alignment with the query distribution lead to better generalization, even without any parameter tuning. Under mild conditions, we further prove that the generalization error can achieve the optimal convergence rate of O(N^(-1/2)), where N is the number of demonstrations. Our empirical evaluations validate the effectiveness of our theoretical findings.

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AAAI Conference 2017 Conference Paper

Robust Visual Tracking via Local-Global Correlation Filter

Heng Fan
Jinhai Xiang

Correlation filter has drawn increasing interest in visual tracking due to its high efficiency, however, it is sensitive to partial occlusion, which may result in tracking failure. To address this problem, we propose a novel local-global correlation filter (LGCF) for object tracking. Our LGCF model utilizes both local-based and global-based strategies, and effectively combines these two strategies by exploiting the relationship of circular shifts among local object parts and global target for their motion models to preserve the structure of object. In specific, our proposed model has two advantages: (1) Owing to the benefits of local-based mechanism, our method is robust to partial occlusion by leveraging visible parts. (2) Taking into account the relationship of motion models among local parts and global target, our LGCF model is able to capture the inner structure of object, which further improves its robustness to occlusion. In addition, to alleviate the issue of drift away from object, we incorporate temporal consistencies of both local parts and global target in our LGCF model. Besides, we adopt an adaptive method to accurately estimate the scale of object. Extensive experiments on OTB15 with 100 videos demonstrate that our tracking algorithm performs favorably against state-of-the-art methods.

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