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Xiaolong Zhou

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4 papers

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TIST Journal 2025 Journal Article

Adaptive Target-Oriented Tracking

Sixian Chan
Xianpeng Zeng
Zhoujian Wu
Yu Wang
Xiaolong Zhou
Tinglong Tang
Jie Hu

The current one-stream tracking pipelines are early relation modeling in feature extraction. However, insufficient discrimination may result in ambiguous relation modeling during early feature extraction. Moreover, the non-target information occupies most of the search image, rendering most relation modeling futile. To tackle the above issues, we propose tracking via learning adaptive target-oriented representation, named ATOTrack. We design an Untied positional encoding to mark the template token and the search region token separately, which reduces the confused relationship between the template and the search region. Besides, we introduce an Auto-Mask Learner to decouple the target and non-target information in the search region. Interestingly, the Auto-Mask Learner can self-learn and mask the ineffective information to interpret adaptive target-oriented representation. Extensive experiments demonstrate that ATOTrack is superior to existing methods, which achieves the state-of-the-art performance on six tracking benchmarks. In particular, ATOTrack establishes a new record on AViST with 57% AO. The code and models will be released as soon.

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IJCAI Conference 2025 Conference Paper

Enhancing Nighttime Semantic Segmentation with Visual-Linguistic Priors and Wavelet Transform

Jianhou Zhou
Xiaolong Zhou
Sixian Chan
Zhaomin Chen
Xiaoqin Zhang

Nighttime semantic segmentation is a critical yet challenging task in autonomous driving. Most existing methods are designed for daytime scenarios, resulting in poor nighttime performance due to texture loss and decreased object visibility. Low-light enhancement was applied before segmentation but failed to recover nighttime-specific details, introducing noise or losing delicate structures. Recent work shows that large-scale image-text pairs can effectively leverage natural language priors to guide visual representation, achieving remarkable performance across various downstream visual tasks. However, effectively employing visual-linguistic priors for nighttime semantic segmentation remains underexplored. To address these issues, we propose Text-WaveletFormer, a novel end-to-end framework that integrates text prompts and wavelet-based texture enhancement. Specifically, to compensate for the low recognizability of objects in nighttime scenes, we design a Text-Image Fusion Module (TIFM) to incorporate textual priors to improve nighttime object recognition. In addition, to alleviate the lack of texture details in nighttime conditions, we introduce a Wavelet Guided Texture Amplifier Module (WTAM) to fuse wavelet and raw image features via cross-attention, restoring low-light details. Finally, extensive experiments on benchmarks including NightCity, NightCity-fine, BDD100K, and CityScapes demonstrate our method’s superior performance over existing approaches.

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AAAI Conference 2025 Conference Paper

SGFormer: Semantic-Geometry Fusion Transformer for Multi-modal 3D Panoptic Segmentation

Hongqi Yu
Sixian Chan
Xiaolong Zhou
Xiaoqin Zhang

Modern methods for autonomous driving perception widely adopt multi-modal fusion to enhance 3D scene understanding. However, existing methods suffer from inferior semantic extraction in image encoders that treat all pixels equally, ignoring contextual differences. The generated multi-modal representations also typically lack comprehensive semantic and spatial geometry information, which is crucial for the 3D panoptic segmentation task. In this paper, we propose a novel Semantic-Geometry Fusion Transformer (SGFormer) that extracts adaptive semantic contexts, aggregates geometric information and captures the semantic-geometry fusion. First, in the Image Branch, we tailor semantic contexts for each pixel with context-guided attention and spatial context alignment to refine semantic details. Second, we transform image and voxel features into point-pixel geometry representations, simultaneously learning semantic category priors as embeddings to better represent scene geometry and semantics. Finally, to aggregate semantic information with related geometry, we design a semantic-geometry fusion that combines the transformer, effectively capturing semantic-geometry relationships into multi-modal panoptic representations. Notably, SGFormer achieves the state-of-the-art (SOTA) results on the nuScenes and SemanticPOSS, as well as yielding competitive performance on the SemanticKITTI. Moreover, SGFormer exhibits superior robustness compared to leading methods, marking an improvement of 2% to 10%.

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NeurIPS Conference 2025 Conference Paper

Unveiling the Spatial-temporal Effective Receptive Fields of Spiking Neural Networks

Jieyuan (Eric) Zhang
Xiaolong Zhou
Shuai Wang
Wenjie Wei
Hanwen Liu
Qian Sun
Malu Zhang
Yang Yang

Spiking Neural Networks (SNNs) demonstrate significant potential for energy-efficient neuromorphic computing through an event-driven paradigm. While training methods and computational models have greatly advanced, SNNs struggle to achieve competitive performance in visual long-sequence modeling tasks. In artificial neural networks, the effective receptive field (ERF) serves as a valuable tool for analyzing feature extraction capabilities in visual long-sequence modeling. Inspired by this, we introduce the Spatio-Temporal Effective Receptive Field (ST-ERF) to analyze the ERF distributions across various Transformer-based SNNs. Based on the proposed ST-ERF, we reveal that these models suffer from establishing a robust global ST-ERF, thereby limiting their visual feature modeling capabilities. To overcome this issue, we propose two novel channel-mixer architectures: \underline{m}ulti-\underline{l}ayer-\underline{p}erceptron-based m\underline{ixer} (MLPixer) and \underline{s}plash-and-\underline{r}econstruct \underline{b}lock (SRB). These architectures enhance global spatial ERF through all timesteps in early network stages of Transformer-based SNNs, improving performance on challenging visual long-sequence modeling tasks. Extensive experiments conducted on the Meta-SDT variants and across object detection and semantic segmentation tasks further validate the effectiveness of our proposed method. Beyond these specific applications, we believe the proposed ST-ERF framework can provide valuable insights for designing and optimizing SNN architectures across a broader range of tasks. The code is available at \href{https: //github. com/EricZhang1412/Spatial-temporal-ERF}{\faGithub~EricZhang1412/Spatial-temporal-ERF}.

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