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.

Exemplar-based face sketch synthesis has long been impeded by the difficulty of accurate neighbor selection. Given a test patch extracted from the test photograph, the K-nearest neighbor (K-NN) matching algorithm is generally performed by existing methods to find K-nearest photograph patches in the training data set, which contains some pairs of face sketches and photographs. Then, the training sketch patches corresponding to the selected nearest photograph patches are taken as the candidate to synthesize the target sketch patch. In the aforementioned neighbor selection process, training sketch patches is not taken into consideration in the process of K-nearest neighbor selection. In this paper, we proposed a simple yet effective neighbor selection algorithm, namely, anchored neighborhood index (ANI), to boost the synthesis performance by taking training sketch patches into the consideration. In addition, the proposed ANI can be conducted offline and, thus, it does not increase the computational complexity. Extensive experiments on public available database demonstrate that the proposed algorithm achieves superior performance compared with the state-of-the-art methods in terms of both objective image quality scores and face recognition accuracy.

Exemplar-based face sketch synthesis has been widely applied to both digital entertainment and law enforcement. In this paper, we propose a Bayesian framework for face sketch synthesis, which provides a systematic interpretation for understanding the common properties and intrinsic difference in different methods from the perspective of probabilistic graphical models. The proposed Bayesian framework consists of two parts: the neighbor selection model and the weight computation model. Within the proposed framework, we further propose a Bayesian face sketch synthesis method. The essential rationale behind the proposed Bayesian method is that we take the spatial neighboring constraint between adjacent image patches into consideration for both aforementioned models, while the state-of-the-art methods neglect the constraint either in the neighbor selection model or in the weight computation model. Extensive experiments on the Chinese University of Hong Kong face sketch database demonstrate that the proposed Bayesian method could achieve superior performance compared with the state-of-the-art methods in terms of both subjective perceptions and objective evaluations.