Pre-trained vision-language models enhance single-modal person re-identification by leveraging high-level semantic knowledge through prompt learning. However, in visible-in-frared person re-identification, existing prompt learning frameworks primarily emphasize the spatial encoding of modality-shared pedestrian features, inevitably entangling identity-specific characteristics with modality-specific details. Consequently, they struggle to fully disentangle and exploit critical modality-specific cues from the entangled information and fail to construct robust modality-invariant discriminative representations by effectively integrating identity-specific information with features from both modalities, thereby hindering effective cross-modality alignment. To address these limitations, we propose a novel Frequency-Guided Multi-Perspective Prompt Learning (FGMP) framework for visible-infrared person re-identification, which decouples identity-specific and modality-specific information via frequency-guided feature disentanglement, establishes discriminative pedestrian semantics and global modality prototypes through multi-perspective prompts, and performs cross-modality feature fusion, collectively reducing modality discrepancies and facilitating robust pedestrian representation. Specifically, FGMP introduces the Frequency Domain Gated Decoupler (FDGD), which leverages the inherent discreteness of frequency representations to decompose pedestrian features into modality-shared and modality-specific components, overcoming the limitations of prior spatial-centric approaches that entangle identity-specific cues with modality-specific information. Building on this, a heterogeneous prompt learning strategy is employed to generate ID-specific and modality-specific prompts, enriching instance-discriminative information and global modality prototypes by activating CLIP’s inherent multi-modal knowledge and forming complementary high-level semantic interactions. Finally, a multi-modal feature fusion module is designed to seamlessly integrate representations from different modalities and identity-specific characteristics, yielding unified and discriminative pedestrian representations. Extensive experiments conducted on three widely used datasets demonstrate the effectiveness of the proposed FGMP.

Text-based person retrieval (TBPR) aims to retrieve pedestrian images based on textual descriptions, facing challenges due to the inherent heterogeneity and uncertainty between visual and textual modalities. Most existing methods focus on addressing heterogeneity while neglecting the issue of uncertainty. To tackle the uncertainty arising from the diverse textual expressions, including both structural and semantic content variations, we propose a novel Dual Uncertainty-Guided Feature Alignment Learning (DUAL) approach, utilizing instance-level and identity-level uncertainty estimations to mitigate these impacts. Specifically, for the uncertainty caused by textual structure variations, DUAL first introduces an uncertainty Gaussian modeling module that represents image and text features as Gaussian distributions in a learnable manner, and estimates instance-level uncertainty coefficients to quantify structural differences within the text. Subsequently, DUAL leverages ShareGPT4V to standardize the text structure, dynamically aligning the original text features with structure-invariant generated text features through adaptive knowledge distillation guided by the instance-level uncertainty coefficients, effectively reducing structural diversity's impact while minimizing noise. Moreover, for the uncertainty caused by the diversity of textual semantic content, DUAL designs an alignment loss that utilizes identity-level uncertainty coefficients, estimated via a Gaussian Mixture Model based on the distances between image and text features of the same identity, effectively mitigating the impact of semantic content diversity. Experimental results demonstrate that DUAL outperforms existing methods on TBPR benchmarks, highlighting its superiority in multimodal person retrieval.

Unsupervised domain adaptive person re-identification aims at adapting the re-identification model trained on a labeled source domain to an unlabeled target domain. The mainstream pipeline alternates between clustering-based pseudo-label prediction and representation learning, but the imperfect interaction between these steps generates noisy pseudo labels that diminish the model’s effectiveness. Previous methods reduce noisy pseudo labels impact by assessing consistency only at a single granularity level, overlooking multi-level confidence analysis for better feature representation. To address the issue, we propose a novel multi-granularity consistency network (MGCN) to perform the noise-resistance learning across different granularity consistency perspectives, including prototype-wise consistency, triplet-wise consistency and list-wise consistency, to suppress the contribution of noisy samples simultaneously. Specifically, the prototype-wise consistency leverages the prototypical output affinity between teacher and student networks to evaluate the reliability of pseudo label of a target sample, thus reducing their negative impact on identity classification loss. Triplet-wise consistency focuses on the triplet distance discrepancies between the teacher and student networks to retain the reliable and informative samples that satisfy triplet distance constraint in the triplet loss, thereby facilitating more effective model training and improved performance in the target domain. Furthermore, the list-wise consistency uses accurate list-wise similarity rankings from the teacher’s memory bank to select more dependable neighboring samples in the student’s memory bank, pulling these closer in feature space to alleviate the detrimental effects of noisy labels in contrastive loss. Based on the multi-granularity consistency, MGCN evaluates the credibility of pseudo labels and adjusts their impact across three re-ID losses for effective domain adaptation. Experimental results demonstrate that our proposed method achieves significant improvements over the existing methods on multiple benchmarks.

We propose a method that allows a user to create folded surfaces with a good sense of reality from simply sketches directly drawing curves on the surface of a cloth 3D model. The user can obtain a one-shot image of a simple cloth fold simulation result with a good sense of reality through our method in a short time, even for the one who has little or no knowledge of the cloth apparel cutting structure and the physical properties of the fabric. The method consists of a simulation method that allows the user to make a preliminary adjustment to their cloth model, a reconstruction method to design the folded surface by sketching on the surface, and a refinement method to modify the shape of the folded surface.

A polycube is a face-connected set of cubical cells on ℤ3. To-date, no formulae enumerating polycubes by volume (number of cubes) or perimeter (number of empty cubes neighboring the polycube) are known. We present a few formulae enumerating polycubes with a fixed deviation from the maximum possible perimeter.