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Dongxu Zhang

5 papers indexed

arxivcs.NE2026-07-22

SpikingMOT: A Spike-Driven Multi-Object Tracker

Yiding Sun, Xiangyang Yang, Dongxu Zhang, Qirui Wang, Zijie Xu, Wenxuan Liu, et al.

Multi-object tracking (MOT) plays a fundamental role in visual perception, where accurate trajectory prediction is essential for reliable target association under complex motion patterns. Recent trackers have improved motion modeling with densely activated artificial neural netwo…

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arxivcs.AIcs.CL2026-07-11

SPARK: Susceptibility-Guided Profiling and Steering of Latent Reasoning States in Large Language Models

Dongxu Zhang, Yiding Sun, Zihao Guo, Xiangyang Yang, Kai Tang, Lin Chen, et al.

Reasoning failures in large language models (LLMs) are usually evaluated from final answers, but a wrong answer does not reveal why the model failed. The same incorrect output may reflect missing capability, an unstable reasoning trajectory, or a failure to activate a reasoning s…

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arxivcs.CV2026-07-07

GaussFusion: Towards Multimodal 3D Gaussian Pretraining

Zhixuan You, Jihua Zhu, Yiding Sun, Zihao Guo, Haozhe Cheng, Dongxu Zhang, et al.

3D Gaussian Splatting provides an explicit representation that jointly models geometry and appearance, serving as a scalable foundation for 3D representation learning. Existing pre-training methods for Gaussian representations, such as masked Gaussian reconstruction, primarily ca…

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arxivcs.CVcs.AI2026-07-05

SeeMe: Mitigating Hallucinations in Large Vision-Language Models through Effective Visual Token Engineering

Kai Tang, Jinhao You, Bohua Zhang, Yichen Guo, Yiding Sun, Dongxu Zhang, et al.

Large Vision-Language Models (LVLMs) have achieved remarkable progress in visual understanding tasks such as image captioning and visual question answering. However, they remain susceptible to hallucinations, generating content that is inconsistent with the actual visual input. E…

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arxivcs.AI2026-06-28

FADE: Mitigating Hallucinations by Reducing Language-Prior Dominance in Large Vision-Language Models

Yichen Guo, Kai Tang, Fenglai Lin, Yiding Sun, Dongxu Zhang, Wenya Wang, et al.

Despite the impressive capabilities of Large Vision-Language Models (LVLMs), they remain susceptible to hallucination, generating content inconsistent with the input image. Recent studies attribute this to the dominance of language priors over visual inputs and employ contrastive…

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