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Shenglan Li

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2 papers

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IJCAI Conference 2025 Conference Paper

Modality-Guided Dynamic Graph Fusion and Temporal Diffusion for Self-Supervised RGB-T Tracking

Shenglan Li
Rui Yao
Yong Zhou
Hancheng Zhu
Kunyang Sun
Bing Liu
Zhiwen Shao
Jiaqi Zhao

To reduce the reliance on large-scale annotations, self-supervised RGB-T tracking approaches have garnered significant attention. However, the omission of the object region by erroneous pseudo-label or the introduction of background noise affects the efficiency of modality fusion, while pseudo-label noise triggered by similar object noise can further affect the tracking performance. In this paper, we propose GDSTrack, a novel approach that introduces dynamic graph fusion and temporal diffusion to address the above challenges in self-supervised RGB-T tracking. GDSTrack dynamically fuses the modalities of neighboring frames, treats them as distractor noise, and leverages the denoising capability of a generative model. Specifically, by constructing an adjacency matrix via an Adjacency Matrix Generator (AMG), the proposed Modality-guided Dynamic Graph Fusion (MDGF) module uses a dynamic adjacency matrix to guide graph attention, focusing on and fusing the object’s coherent regions. Temporal Graph-Informed Diffusion (TGID) models MDGF features from neighboring frames as interference, and thus improving robustness against similar-object noise. Extensive experiments conducted on four public RGB-T tracking datasets demonstrate that GDSTrack outperforms the existing state-of-the-art methods. The source code is available at https: //github. com/LiShenglana/GDSTrack.

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AAAI Conference 2020 Conference Paper

New Efficient Multi-Spike Learning for Fast Processing and Robust Learning

Shenglan Li
Qiang Yu

Spiking neural networks (SNNs) are considered to be more biologically plausible and lower power consuming than traditional artiﬁcial neural networks (ANNs). SNNs use discrete spikes as input and output, but how to process and learn these discrete spikes efﬁciently and accurately still remains a challenging task. Moreover, most existing learning methods are inefﬁcient with complicated neuron dynamics and learning procedures being involved. In this paper, we propose efﬁcient alternatives by ﬁrstly introducing a simpliﬁed and efﬁcient neuron model. Based on it, we develop two new multi-spike learning rules together with an eventdriven scheme being presented to improve the processing ef- ﬁciency. We show that, with the as-proposed rules, a single neuron can be trained to successfully perform challenging tasks such as multi-category classiﬁcation and feature extraction. Our learning methods demonstrate a signiﬁcant robustness against various strong noises. Moreover, experimental results on some real-world classiﬁcation tasks show that our approaches yield higher efﬁciency with less requirement on computation resource, highlighting the advantages and potential of spike-based processing and driving more efforts towards neuromorphic computing.

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