Author name cluster

Shicai Yang

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

6 papers

2 author rows

NeurIPS Conference 2025 Conference Paper

Training-Free Test-Time Adaptation via Shape and Style Guidance for Vision-Language Models

Shenglong Zhou
Manjiang Yin
Leiyu Sun
Shicai Yang
Di Xie
Jiang Zhu

Test-time adaptation with pre-trained vision-language models shows impressive zero-shot classification abilities, and training-free methods further improve the performance without any optimization burden. However, existing training-free test-time adaptation methods typically rely on entropy criteria to select the visual features and update the visual caches, while ignoring the generalizable factors, such as shape-sensitive and style-insensitive factors. In this paper, we propose a novel shape and style guidance method (SSG) for training-free test-time adaptation in vision-language models, aiming to highlight the shape-sensitive (SHS) and style-insensitive (STI) factors in addition to entropy criteria. Specifically, SSG perturbs the raw test image with shape and style corruption operations, and measures the prediction difference between the raw and corrupted one as perturbed prediction difference (PPD). Based on the PPD measurement, SSG reweights the high-confidence visual features and corresponding predictions, aiming to highlight the effect of SHS and STI factors during the test-time procedure. Furthermore, SSG takes both PPD and entropy into consideration to update the visual cache, aiming to maintain the stored sample with high entropy and generalizable factors. Extensive experimental results on out-of-distribution and cross-domain benchmark datasets demonstrate that our proposed SSG consistently outperforms previous state-of-the-art methods while also exhibiting promising computational efficiency.

PDF Details

IJCAI Conference 2025 Conference Paper

VQCounter: Designing Visual Prompt Queue for Accurate Open-World Counting

Fanfan Ye
Yiqi Fan
Qiaoyong Zhong
Shicai Yang
Di Xie
Jie Song
Mingli Song

Class-agnostic counting enables enumerating arbitrary object classes beyond those seen during training. Recent studies attempted to exploit the potential of visual foundation models such as GroundingDINO. Despite the considerable progress, we observe certain shortcomings, including the limited diversity of visual prompts and suboptimal training regimen. To address these issues, we introduce VQCounter, which incorporates a visual prompt queue mechanism designed to enrich the diversity of visual prompts. A random modality switching strategy is proposed during training to strengthen both textual and visual modalities. Besides, in light of weak point supervision, a Voronoi diagram-based cost (VoronoiCost) is designed to improve Hungarian matching, leading to more stable and faster convergence. Building upon the Voronoi diagram, we also propose a novel set of more stringent evaluation metrics, which take point localization into account. Extensive experiments on the FSC-147 and CARPK datasets demonstrate that VQCounter achieves state-of-the-art performance in both zero-shot and few-shot settings, significantly outperforming existing methods across nearly all evaluations.

PDF Details DOI

IJCAI Conference 2022 Conference Paper

Dynamic Domain Generalization

Zhishu Sun
Zhifeng Shen
Luojun Lin
Yuanlong Yu
Zhifeng Yang
Shicai Yang
Weijie Chen

Domain generalization (DG) is a fundamental yet very challenging research topic in machine learning. The existing arts mainly focus on learning domain-invariant features with limited source domains in a static model. Unfortunately, there is a lack of training-free mechanism to adjust the model when generalized to the agnostic target domains. To tackle this problem, we develop a brand-new DG variant, namely Dynamic Domain Generalization (DDG), in which the model learns to twist the network parameters to adapt to the data from different domains. Specifically, we leverage a meta-adjuster to twist the network parameters based on the static model with respect to different data from different domains. In this way, the static model is optimized to learn domain-shared features, while the meta-adjuster is designed to learn domain-specific features. To enable this process, DomainMix is exploited to simulate data from diverse domains during teaching the meta-adjuster to adapt to the agnostic target domains. This learning mechanism urges the model to generalize to different agnostic target domains via adjusting the model without training. Extensive experiments demonstrate the effectiveness of our proposed method. Code is available: https: //github. com/MetaVisionLab/DDG

PDF Details DOI

ICML Conference 2022 Conference Paper

Learning Domain Adaptive Object Detection with Probabilistic Teacher

Meilin Chen
Weijie Chen 0006
Shicai Yang
Jie Song 0011
Xinchao Wang
Lei Zhang 0038
Yunfeng Yan
Donglian Qi

Self-training for unsupervised domain adaptive object detection is a challenging task, of which the performance depends heavily on the quality of pseudo boxes. Despite the promising results, prior works have largely overlooked the uncertainty of pseudo boxes during self-training. In this paper, we present a simple yet effective framework, termed as Probabilistic Teacher (PT), which aims to capture the uncertainty of unlabeled target data from a gradually evolving teacher and guides the learning of a student in a mutually beneficial manner. Specifically, we propose to leverage the uncertainty-guided consistency training to promote classification adaptation and localization adaptation, rather than filtering pseudo boxes via an elaborate confidence threshold. In addition, we conduct anchor adaptation in parallel with localization adaptation, since anchor can be regarded as a learnable parameter. Together with this framework, we also present a novel Entropy Focal Loss (EFL) to further facilitate the uncertainty-guided self-training. Equipped with EFL, PT outperforms all previous baselines by a large margin and achieve new state-of-the-arts.

Details

IROS Conference 2022 Conference Paper

Self-Supervised Noisy Label Learning for Source-Free Unsupervised Domain Adaptation

Weijie Chen 0006
Luojun Lin
Shicai Yang
Di Xie
Shiliang Pu
Yueting Zhuang

Domain adaptation is an important property in robot vision, which enables the neural networks pre-trained on source domains to adapt target domains automatically without any annotation efforts. During this process, source data is not always accessible due to the constraints of expensive storage overhead and data privacy protection. Therefore, the source domain pre-trained model is expected to optimize with only unlabeled target data, termed as source-free unsupervised domain adaptation. In this paper, we view this problem as a special case of noisy label learning, since the given pre-trained model can generate noisy labels for unlabeled target data via network inference. The potential semantic cues for unsupervised domain adaptation exactly lie on these noisy labels. Inspired by this problem modeling, we propose a simple yet effective Self-Supervised Noisy Label Learning method, which injects self-supervised learning to impose the intrinsic data structure and facilitate label-denoising. Extensive experiments have been conducted on diverse benchmarks to validate the effectiveness. Our method achieves state-of-the-art performance.

Details

AAAI Conference 2021 Conference Paper

A Free Lunch for Unsupervised Domain Adaptive Object Detection without Source Data

Xianfeng Li
Weijie Chen
Di Xie
Shicai Yang
Peng Yuan
Shiliang Pu
Yueting Zhuang

Unsupervised domain adaptation (UDA) assumes that source and target domain data are freely available and trained together to reduce the domain gap. Considering the data privacy, it is impractical in real scenarios. Hence, it draws our eyes to optimize the network in the target domain without labeled source data. To explore this direction in object detection, for the first time, we propose a source data-free domain adaptive object detection (SFOD) framework via modeling it into learning with noisy labels. A straightforward method is to leverage the pre-trained network from the source domain to generate the pseudo labels for target domain. However, it is difficult to evaluate the quality of pseudo labels since no labels are available in target domain. In this paper, self-entropy descent (SED) is a metric proposed to search an appropriate confidence threshold for reliable pseudo label generation. Nonetheless, completely clean labels are still unattainable. After an experimental analysis, false negatives are found to dominate in the generated noisy labels. Undoubtedly, false negatives mining is helpful for improvement, and we ease it to false negatives simulation through data augmentation like Mosaic. Extensive experiments conducted in four adaptation tasks have demonstrated that the proposed framework can easily achieve state-of-the-art performance. From another view, it also reminds the UDA community that the labeled source data are not fully exploited in the existing methods.

PDF Details