Radiography imaging protocols focus on particular body regions, therefore producing images of great similarity and yielding recurrent anatomical structures across patients. Exploiting this structured information could potentially ease the detection of anomalies from radiography images. To this end, we propose a Sim ple S pace-Aware Memory Matrix for I n-painting and D etecting anomalies from radiography images (abbreviated as SimSID). We formulate anomaly detection as an image reconstruction task, consisting of a space-aware memory matrix and an in-painting block in the feature space. During the training, SimSID can taxonomize the ingrained anatomical structures into recurrent visual patterns, and in the inference, it can identify anomalies (unseen/modified visual patterns) from the test image. Our SimSID surpasses the state of the arts in unsupervised anomaly detection by +8.0%, +5.0%, and +9.9% AUC scores on ZhangLab, COVIDx, and CheXpert benchmark datasets, respectively.

Multi-label recognition with partial labels (MLR-PL), in which only some labels are known while others are unknown for each image, is a practical task in computer vision, since collecting large-scale and complete multi-label datasets is difficult in real application scenarios. Recently, vision language models (e.g., CLIP) have demonstrated impressive transferability to downstream tasks in data limited or label limited settings. However, current CLIP-based methods suffer from semantic confusion in MLR task due to the lack of fine-grained information in the single global visual and textual representation for all categories. In this work, we address this problem by introducing a semantic decoupling module and a category-specific prompt optimization method in CLIP-based framework. Specifically, the semantic decoupling module following the visual encoder learns category-specific feature maps by utilizing the semantic-guided spatial attention mechanism. Moreover, the category-specific prompt optimization method is introduced to learn text representations aligned with category semantics. Therefore, the prediction of each category is independent, which alleviate the semantic confusion problem. Extensive experiments on Microsoft COCO 2014 and Pascal VOC 2007 datasets demonstrate that the proposed framework significantly outperforms current state-of-art methods with a simpler model structure. Additionally, visual analysis shows that our method effectively separates information from different categories and achieves better performance compared to CLIP-based baseline method.

Along with the concern of similarity measures in linear space, supervised online hash methods have been applied to the retrieval task. However, they ignored multi-dimensional space semantic mining and association characteristics will cause quantization errors of information hash code: 1) The similarity relation of discretized data needs to be considered in different spaces; 2) Latent semantic features need to be continuously embedded into hash code learning; 3) The correlation between the structure similarity and discrete hash matrices needs to be continuously optimized. To tackle these challenges, this paper proposes a novel Extensible Max-min Collaborative Retention Online Hash retrieval method based on mini-batch training data (EMCROH). It mainly includes the Max-min Bayesian Similarity Sparse Latent Hash module (MBSSLH), and the Repetition Collaborative Projection Learning module (RCPL). Specifically, MBSSLH is a max-min optimization model. Firstly, to explore the semantic similarity of multi-dimensional space, we propose a novel liner and nonlinear semantic similarity discrimination mechanism based on the log maximum likelihood similarity estimation with Euclidean space and minimize the input batch data features with a common projection matrix. Moreover, to further mine the potential semantic information of the discretization, we also propose a robust sparse discrete latent semantic information extraction submodule based on double latent factors. RCPL can extend the data externally using the repetition collaborative projection matrix with robustness regularization constraint. Finally, a novel max-min embedding iterative step is proposed to solve the batch discrete optimization problem based on Augmented Lagrange Multipliers (ALM) with Alternating Direction Minimization (ADM). Extensive experiments on several well-known large databases demonstrate that EMCROH outperforms the state-of-the-art hash methods. Code and datasets have been publicly available at https://github.com/Tjeep-Tan/EMCROH .