Colorectal polyp segmentation is essential for the early detection and diagnosis of colorectal diseases, requiring precise identification of both polyp regions and their boundaries. In this paper, we propose a novel Boundary-Focused Polyp Segmentation model (BFPS) that integrates frequency domain separation with boundary-focused mechanisms. Specifically, we introduce a Frequency Domain Separator that decomposes feature maps extracted by the encoder into low- and high-frequency components. The low-frequency features are processed through a Boundary-Focused Enhancement module, which refines boundary predictions by leveraging higher-level contextual information, while the high-frequency features are processed by a High-Frequency Attention module to highlight fine boundary details. Experimental results on five widely used polyp segmentation datasets demonstrate that the proposed BFPS achieves state-of-the-art performance on particularly challenging datasets and shows competitive results on other datasets. The source code will be released at https://github.com/gentleMar/BFPS.git.

Neural network-based models for collaborative filtering have received widespread attention, among which variational autoencoder (VAE) has shown unique advantages in the task of item recommendation. However, most existing VAE-based models only focus on one type of user feedback, leading to their performance bottlenecks. To overcome this limitation, we propose a novel VAE-based recommendation model called VAE++, which can effectively utilize heterogeneous feedback to boost recommendation performance. Specifically, it combines three different types of signals, i.e., purchase feedback, examination feedback and their mixed feedback, via two well-designed modules, i.e., a target representation enhancement module and a target representation refinement module. The former exploits the mixed feedback to improve the learning of purchase representations, while the latter leverages the examination feedback to further refine them. In particular, purchase and examination preferences are jointly decoded in one decoder to ensure the high quality of the reconstructed samples. Extensive experiments on three public datasets show that our VAE++ achieves the best results compared with several state-of-the-art methods.

Recommender systems have been a valuable component in various online services such as e-commerce and entertainment. To provide an accurate top-N recommendation list of items for each target user, we have to answer a very basic question of how to model users’ feedback effectively. In this article, we focus on studying users’ explicit feedback, which is usually assumed to contain more preference information than the counterpart, i.e., implicit feedback. In particular, we follow two very recent transfer to rank algorithms by converting the original feedback to three different but related views of examinations, scores, and purchases, and then propose a novel solution called holistic transfer to rank (HoToR), which is able to address the uncertainty challenge and the inconvenience challenge in the existing works. More specifically, we take the rating scores as a weighting strategy to alleviate the uncertainty of the examinations, and we design a holistic one-stage solution to address the inconvenience of the two/three-stage training and prediction procedures in previous works. We then conduct extensive empirical studies in a direct comparison with the two closely related transfer learning algorithms and some very competitive factorization- and neighborhood-based methods on three public datasets and find that our HoToR performs significantly better than the other methods in terms of several ranking-oriented evaluation metrics.