Accurate segmentation of 3D tubular structures in medical images is critical for clinical diagnosis and interventional planning. Although deep learning methods have advanced significantly, most existing approaches exhibit limited generalizability due to their reliance on structure-specific morphological priors. Consequently, these models are often constrained to particular anatomical regions-such as the colorectal tract, vasculature, or arteries-leading to suboptimal performance when applied across varied organ systems. Moreover, the joint modeling of global and local morphological characteristics remains underexplored in the context of tubular structure segmentation. To address these limitations, we propose MCDNet, a Morphological-Conditional Dual-view Network that integrates both contextual and morphological information through a target-adaptive Morphological-Conditional Convolution (MCConv). The network is composed of three sequential modules: (1) a morphological feature extraction stage leveraging MCConv to enhance structural sensitivity across diverse tubular geometries; (2) a contextual feature learning stage employing a cross-fusion mechanism that synergistically combines convolutional and attention-based representations; and (3) a residual self-attention fusion module that reinforces feature integration from decoupled morphological and contextual branches. We evaluate MCDNet on four benchmark datasets encompassing diverse tubular segmentation tasks. Experimental results demonstrate that MCDNet achieves superior performance over state-of-the-art methods, with an average Dice coefficient improvement of 6.93% and a 10.61% reduction in Hausdorff distance relative to a strong baseline. The source code is publicly available at https://github.com/wzydcg/MCDNet.

Person re-identification (ReID) is crucial in video surveillance, aiming to match individuals across different camera views while cloth-changing person re-identification (CC-ReID) focuses on pedestrians changing attire. Many existing CC-ReID methods overlook generalization, crucial for universality across cloth-consistent and cloth-changing scenarios. This paper pioneers exploring the cloth-generalized person re-identification (CG-ReID) task and introduces the Cloth-aware Augmentation (CaAug) strategy. Comprising domain augmentation and feature augmentation, CaAug aims to learn identity-relevant features adaptable to both scenarios. Domain augmentation involves creating diverse fictitious domains and simulating various clothing scenarios. Supervising features from different cloth domains enhances robustness and generalization against clothing changes. Additionally, for feature augmentation, element exchange introduces diversity concerning clothing changes. Regularizing the model with these augmented features strengthens resilience against clothing change uncertainty. Extensive experiments on cloth-changing datasets demonstrate the efficacy of our approach, consistently outperforming state-of-the-art methods.

For the long-term person re-identification (ReID) task, pedestrians are likely to change clothes, which poses a key challenge in overcoming drastic appearance variations caused by these cloth changes. However, analyzing how cloth changes influence identity-invariant representation learning is difficult. In this context, varying cloth-changed samples are not adaptively utilized, and their effects on the resulting features are overshadowed. To address these limitations, this paper aims to estimate the effect of cloth-changing patterns at both the image and feature levels, presenting a Dual-Level Adaptive Weighting (DLAW) solution. Specifically, at the image level, we propose an adaptive mining strategy to locate the cloth-changed regions for each identity. This strategy highlights the informative areas that have undergone changes, enhancing robustness against cloth variations. At the feature level, we estimate the degree of cloth-changing by modeling the correlation of part-level features and re-weighting identity-invariant feature components. This further eliminates the effects of cloth variations at the semantic body part level. Extensive experiments demonstrate that our method achieves promising performance on several cloth-changing datasets. Code and models are available at https: //github.com/fountaindream/DLAW.

Nowadays, research on temporal membership queries is indispensable. Generally, temporal membership queries exist in two modalities: fixed windows and sliding windows, the latter having obvious advantages. The first sketch that implements temporal membership queries is the persistent Bloom filter (PBF). PBF has two shortcomings: it does not support sliding windows nor frequency queries. Here, we propose HoppingSketch to promote the original PBF. It is the first sketch that implements temporal membership queries for sliding windows. HoppingSketch is a general and efficient data stream processing framework, able to implement different tasks thanks to different atomic sketches. When the atomic sketches are Bloom filters and we apply them to PBF, HoppingSketch can achieve significantly higher temporal membership query accuracy than the original PBF. When the atomic sketches are sketches of Count-Min, Conservative Update, and Count, HoppingSketch can achieve more accurate frequency query than by applying PBF on the corresponding sketches. Our experimental results demonstrate the advantages of HoppingSketch compared with the state-of-the-art.

The performances of person re-identification tasks can be seriously degraded because of variations caused by view changes. In recent years, there are many methods focusing on how to solve cross view challenges which can be roughly divided into two categories: 1) learning view-invariant features without the help of view information. 2) combining view-wise features with the guide of view information. However, these methods are neither perfect enough. Methods of the first category are not roust enough for different kinds of view-invariants while methods of the other category can not generalize well in real-world applications. In this paper, we aim to learn view-invariant features with the help of view information. We proposed an end-to-end trainable framework, called View Confusion Feature Learning (VCFL), to learn view-invariant features by getting rid of view specific information. To the best of our knowledge, VCFL is originally proposed to learn view-invariant identity-wise features, and it is a kind of combination of view-generic and view-specific methods. The whole view confusion learning mechanism consists of three parts: 1) adversarial learning between feature extractor and the view classifier; 2) drawing the features with the same ID close to centers; 3) the guidance of SIFT, for seamlessly integration of hand-crafted features and deep features. In order to make the whole confusion mechanism work better, we further propose a VCFL+ model, which improves the fusion process in the feature map level through the thoughts of attention mechanism. Experiments on three benchmark datasets including Market1501, CUHK03, and DukeMTMC prove the superiority of our method over state-of-the-art approaches.

Person re-identification is an important task in video surveillance that aims to associate people across camera views at different locations and time. View variability is always a challenging problem seriously degrading person re-identification performance. Most of the existing methods either focus on how to learn view invariant feature or how to combine viewwise features. In this paper, we mainly focus on how to learn view-independent features by getting rid of view specific information through a view confusion learning mechanism. Specifically, we propose an end-to-end trainable framework, called View Confusion Feature Learning (VCFL), for person Re-ID across cameras. To the best of our knowledge, VCFL is originally proposed to learn view-independent identity-wise features, and it's a kind of combination of view-generic and view-specific methods. Furthermore, we extract sift-guided features by using bag-of-words model to help supervise the training of deep networks and enhance the view invariance of features. In experiments, our approach is validated on three benchmark datasets including CUHK01, CUHK03, and MARKET1501, which show the superiority of the proposed method over several state-of-the-art approaches.