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In this technical report, we briefly introduce our solution for the Zero/Few-shot Track of the Visual Anomaly and Novelty Detection (VAND) 2023 Challenge. For industrial visual inspection, building a single model that can be rapidly adapted to numerous categories without or with only a few normal reference images is a promising research direction. This is primarily because of the vast variety of the product types. For the zero-shot track, we propose a solution based on the CLIP model by adding extra linear layers. These layers are used to map the image features to the joint embedding space, so that they can compare with the text features to generate the anomaly maps. Besides, when the reference images are available, we utilize multiple memory banks to store their features and compare them with the features of the test images during the testing phase. In this challenge, our method achieved first place in the zero-shot track, especially excelling in segmentation with an impressive F1 score improvement of 0.0489 over the second-ranked participant. Furthermore, in the few-shot track, we secured the fourth position overall, with our classification F1 score of 0.8687 ranking first among all participating teams.

Most of the existing blind image Super-Resolution (SR) methods assume that the blur kernels are space-invariant. However, the blur involved in real applications are usually space-variant due to object motion, out-of-focus, etc., resulting in severe performance drop of the advanced SR methods. To address this problem, we firstly introduce two new datasets with out-of-focus blur, i.e., NYUv2-BSR and Cityscapes-BSR, to support further researches of blind SR with space-variant blur. Based on the datasets, we design a novel Cross-MOdal fuSion network (CMOS) that estimate both blur and semantics simultaneously, which leads to improved SR results. It involves a feature Grouping Interactive Attention (GIA) module to make the two modalities interact more effectively and avoid inconsistency. GIA can also be used for the interaction of other features because of the universality of its structure. Qualitative and quantitative experiments compared with state-of-the-art methods on above datasets and real-world images demonstrate the superiority of our method, e.g., obtaining PSNR/SSIM by +1.91/+0.0048 on NYUv2-BSR than MANet., Comment: Accepted by CVPR 2023

We examined whether people perceive prosocial actors' prosocial motivations differently when prosocial actors gain monetary gifts and goods gifts. Across five studies, 1351 participants read different prosocial scenarios that depicted prosocial actors who gained either monetary or goods gifts. Then, they evaluated the prosocial motivations of the prosocial actors. Studies 1-5 consistently found that people perceived prosocial motivations to be less authentic when prosocial actors chose to receive monetary gifts compared with goods gifts. In addition, moral disgust and moral character evaluation mediated this effect (Studies 3-4). Moreover, the negative effect of monetary gifts on people's perception of prosocial motivation further undermined their helping intention to prosocial actors (Study 5). Our research expanded the understanding of people's perception of material gifts in prosocial behavior by proposing the model of monetary benefits aggravated tainted altruism. Besides, our findings provide insights into public policy and charity rules.