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Yan Gan

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4 papers

2 author rows

EAAI Journal 2026 Journal Article

Multi-exposure high dynamic range reconstruction by incorporating imaging knowledge

Hu Wang
Mao Ye
Dengyan Luo
Yan Gan

The existing photographic equipment is not able to capture scenes of the natural world very well. Thus, the problem of reconstructing high dynamic range (HDR) images from multi-exposure low dynamic range (LDR) images arises because these images have different details. The existing methods do not fully leverage imaging knowledge in the LDR image generation pipeline, resulting in design redundancy and inefficient resource utilization. We propose a new Multi-Exposure HDR reconstruction by incorporating Imaging Knowledge (MEIK) for efficient HDR image reconstruction. Our method consists of two parts: fusion of LDR features and reconstruction of HDR feature. Due to object motion and exposure time effects, LDR features with different exposures need to be fused. A Multi-Exposure Information Aggregation (MEIA) module is proposed to fuse LDR features based on Mamba. After that, an Inverse imaging Knowledge-Driven (IKD) cluster is employed to reconstruct the HDR feature, which is a cascade of IKD blocks at different scales. The IKD block consists of three parts: HDR information recovery, imaging parameter adjustment, and noise suppression, used to simulate the mathematical formula for multi-exposure HDR imaging. Experimental results demonstrate that the proposed MEIK model outperforms existing state-of-the-art models and exhibits strong scalability.

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ICLR Conference 2025 Conference Paper

Proxy Denoising for Source-Free Domain Adaptation

Song Tang 0001
Wenxin Su
Yan Gan
Mao Ye 0001
Jianwei Zhang 0001
Xiatian Zhu

Source-Free Domain Adaptation (SFDA) aims to adapt a pre-trained source model to an unlabeled target domain with no access to the source data. Inspired by the success of large Vision-Language (ViL) models in many applications, the latest research has validated ViL's benefit for SFDA by using their predictions as pseudo supervision. However, we observe that ViL's supervision could be noisy and inaccurate at an unknown rate, potentially introducing additional negative effects during adaption. To address this thus-far ignored challenge, we introduce a novel Proxy Denoising (__ProDe__) approach. The key idea is to leverage the ViL model as a proxy to facilitate the adaptation process towards the latent domain-invariant space. Concretely, we design a proxy denoising mechanism to correct ViL's predictions. This is grounded on a proxy confidence theory that models the dynamic effect of proxy's divergence against the domain-invariant space during adaptation. To capitalize the corrected proxy, we further derive a mutual knowledge distilling regularization. Extensive experiments show that ProDe significantly outperforms the current state-of-the-art alternatives under both conventional closed-set setting and the more challenging open-set, partial-set, generalized SFDA, multi-target, multi-source, and test-time settings. Our code and data are available at https://github.com/tntek/source-free-domain-adaptation.

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AAAI Conference 2025 Conference Paper

Self-Prompting Analogical Reasoning for UAV Object Detection

Nianxin Li
Mao Ye
Lihua Zhou
Song Tang
Yan Gan
Zizhuo Liang
Xiatian Zhu

Unmanned Aerial Vehicle Object Detection (UAVOD) presents unique challenges due to varying altitudes, dynamic backgrounds, and the small size of objects. Traditional detection methods often struggle with these challenges, as they typically rely on visual feature only and fail to extract the semantic relations between the objects. To address these limitations, we propose a novel approach named Self-Prompting Analogical Reasoning (SPAR). Our method utilizes the vision-language model (CLIP) to generate context-aware prompts based on image feature, providing rich semantic information that guides analogical reasoning. SPAR includes two main modules: self-prompting and analogical reasoning. Self-prompting module based on learnable description and CLIP-text encoder generates context-aware prompt by combining specific image feature; then an objectness prompt score map is produced by computing the similarity between pixel-level features and context-aware prompt. With this score map, multi-scale image features are enhanced and pixel-level features are chosen for graph construction. While for analogical reasoning module, graph nodes consists of category-level prompt nodes and pixel-level image feature nodes. Analogical inference is based graph convolution. Under the guidance of category-level nodes, different-scale object features have been enhanced, which helps achieve more accurate detection of challenging objects. Extensive experiments illustrate that SPAR outperforms traditional methods, offering a more robust and accurate solution for UAVOD.

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TIST Journal 2023 Journal Article

Towards Query-Efficient Black-Box Attacks: A Universal Dual Transferability-Based Framework

Tao Xiang
Hangcheng Liu
Shangwei Guo
Yan Gan
Wenjian He
Xiaofeng Liao

Adversarial attacks have threatened the application of deep neural networks in security-sensitive scenarios. Most existing black-box attacks fool the target model by interacting with it many times and producing global perturbations. However, all pixels are not equally crucial to the target model; thus, indiscriminately treating all pixels will increase query overhead inevitably. In addition, existing black-box attacks take clean samples as start points, which also limits query efficiency. In this article, we propose a novel black-box attack framework, constructed on a strategy of dual transferability (DT), to perturb the discriminative areas of clean examples within limited queries. The first kind of transferability is the transferability of model interpretations. Based on this property, we identify the discriminative areas of clean samples for generating local perturbations. The second is the transferability of adversarial examples, which helps us to produce local pre-perturbations for further improving query efficiency. We achieve the two kinds of transferability through an independent auxiliary model and do not incur extra query overhead. After identifying discriminative areas and generating pre-perturbations, we use the pre-perturbed samples as better start points and further perturb them locally in a black-box manner to search the corresponding adversarial examples. The DT strategy is general; thus, the proposed framework can be applied to different types of black-box attacks. We conduct extensive experiments to show that, under various system settings, our framework can significantly improve the query efficiency of existing black-box attacks and attack success rates.

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