As the feature dimension of data continues to expand, the task of selecting an optimal subset of features from a pool of limited labeled data and extensive unlabeled data becomes more and more challenging. In recent years, some semi-supervised feature selection methods (SSFS) have been proposed to select a subset of features, but they still have some drawbacks limiting their performance, for e.g., many SSFS methods underutilize the structural distribution information available within labeled and unlabeled data. To address this issue, we proposed a semi-supervised feature selection method based on an adaptive graph with global and local constraints (SFS-AGGL) in this paper. Specifically, we first designed an adaptive graph learning mechanism that can consider both the global and local information of samples to effectively learn and retain the geometric structural information of the original dataset. Secondly, we constructed a label propagation technique integrated with the adaptive graph learning in SFS-AGGL to fully utilize the structural distribution information of both labeled and unlabeled data. The proposed SFS-AGGL method is validated through classification and clustering tasks across various datasets. The experimental results demonstrate its superiority over existing benchmark methods, particularly in terms of clustering performance.

Weakly Supervised Object Detection (WSOD), which utilizes image-level annotated datasets to reduce labeling costs, has been gaining significant attention. Among the WSOD methods, multi-instance learning (MIL) has emerged as a popular solution. However, MIL suffers from reduced accuracy in both localization and classification, as it focuses solely on the most discriminative local regions. To address this limitation, we propose a novel end-to-end MIL-based WSOD method named DAR-ILL. Our approach leverages double-attention refining and iterative learning labeling to capture the region covering the entire object. The double-attention refining consists of two modules: the attention aggregation module (AAM) and the attention erasure module (AEM). AAM effectively combines spatial attention and channel attention to extract features with higher accuracy, while AEM selectively erases the most discriminative local region and subsequently expands the region to encompass the entire object. The iterative labeling learning procedure allows us to identify additional positive instances by introducing a threshold that identifies regions capable of covering the object. To further enhance supervisory information and optimize confidence maps, we introduce the dense optimization module, which facilitates more effective training of all branches. Extensive experimental evaluations demonstrate the superiority of DAR-ILL, as evidenced by its competitive performance in terms of mAP and CorLoc, with particular emphasis on CorLoc.

Video object segmentation uses the first annotated video mask to achieve consistent and precise segmentation in subsequent frames. Recently, memory-based methods have received significant attention owing to their substantial performance enhancements. However, these approaches rely on a fixed global memory strategy, which poses a challenge to segmentation accuracy and speed in the context of longer videos. To alleviate this limitation, we propose a novel semi-supervised video object segmentation model, founded on the principles of the adaptive memory network. Our proposed model adaptively extracts object features by focusing on the object area while effectively filtering out extraneous background noise. An identification mechanism is also thoughtfully applied to discern each object in multiobject scenarios. To further reduce storage consumption without compromising the saliency of object information, the outdated features residing in the memory pool are compressed into salient features through the employment of a self-attention mechanism. Furthermore, we introduce a local matching module, specifically devised to refine object features by fusing the contextual information from historical frames. We demonstrate the efficiency of our approach through experiments, substantially augmenting both the speed and precision of segmentation for long-term videos, while maintaining comparable performance for short videos. The source code is available at https://github.com/GQLi-cong/AMN .