The goal of re-identification (re-ID) is to find an object ( e.g. , person or vehicle) of interest across cameras. In re-ID, designing suitable and effective loss functions plays an essential and imperative role in learning identifiable features. Regardless of the significant success achieved by using retrieval- or verification-based loss functions due to re-ID can be formulated as a retrieval or verification task, the model performance might be degraded owing to the inconsistency between the loss functions and evaluation metrics. Moreover, current hand-designed loss functions based on evaluation metrics require great expertise and a significant workforce, which is often sub-optimal and laborious. To this end, we propose to automatically design loss functions with specific evaluation metrics for re-ID. Specifically, we propose Parameterized Retrieval & Verification (RV) Loss, which jointly optimizes RV tasks while introducing parameterized functions to replace non-differentiable operations in RV evaluation metrics. Different evaluation metric approximations are thus represented in a single formula by a family of parameterized functions. Then, an automated parameter search algorithm is used to conduct the parameter search. Experimental results indicate that the proposed Parameterized RV Loss can improve the performance of the state-of- the-art re-ID methods, thus demonstrating its effectiveness and superiority over other relevant loss functions on the public person re-ID and vehicle re-ID benchmarks.

Person re-identification (re-ID) is commonly investigated as a ranking problem. However, the performance of existing re-ID models drops dramatically, when they encounter extreme positive-negative class imbalance (e.g., very small ratio of positive and negative samples) during training. To alleviate this problem, this article designs a rank-in-rank loss to optimize the distribution of feature embeddings. Specifically, we propose a Differentiable Retrieval-Sort Loss (DRSL) to optimize the re-ID model by ranking each positive sample ahead of the negative samples according to the distance and sorting the positive samples according to the angle (e.g., similarity score). The key idea of the proposed DRSL lies in minimizing the distance between samples of the same category along with the angle between them. Considering that the ranking and sorting operations are non-differentiable and non-convex, the DRSL also performs the optimization of automatic derivation and backpropagation. In addition, the analysis of the proposed DRSL is provided to illustrate that the DRSL not only maintains the inter-class distance distribution but also preserves the intra-class similarity structure in terms of angle constraints. Extensive experimental results indicate that the proposed DRSL can improve the performance of the state-of-the-art re-ID models, thus demonstrating its effectiveness and superiority in the re-ID task.

In existing remote sensing image retrieval (RSIR) datasets, the number of images among different classes varies dramatically, which leads to a severe class imbalance problem. Some studies propose to train the model with the ranking‐based metric (e.g., average precision [AP]), because AP is robust to class imbalance. However, current AP‐based methods overlook an important issue: only optimising samples ranking before each positive sample, which is limited by the definition of AP and is prone to local optimum. To achieve global optimisation of AP, a novel method, namely Optimising Samples after positive ones & AP loss (OSAP‐Loss) is proposed in this study. Specifically, a novel superior ranking function is designed to make the AP loss differentiable while providing a tighter upper bound. Then, a novel loss called Optimising Samples after Positive ones (OSP) loss is proposed to involve all positive and negative samples ranking after each positive one and to provide a more flexible optimisation strategy for each sample. Finally, a graphics processing unit memory‐free mechanism is developed to thoroughly address the non‐decomposability of AP optimisation. Extensive experimental results on RSIR as well as conventional image retrieval datasets show the superiority and competitive performance of OSAP‐Loss compared to the state‐of‐the‐art.

Current visible-infrared cross-modality person re-identification research has only focused on exploring the bi-modality mutual retrieval paradigm, and we propose a new and more practical mix-modality retrieval paradigm. Existing Visible-Infrared Person Re-Identification (VI-ReID) methods have achieved some results in the bi-modality mutual retrieval paradigm by learning the correspondence between visible and infrared modalities. However, significant performance degradation occurs due to the modality confusion problem when these methods are applied to the new mix-modality paradigm. Therefore, this article proposes a Mix-Modality Person Re-Identification (MM-ReID) task, explores the influence of modality mixing ratio on performance, and constructs mix-modality test sets for existing datasets according to the new mix-modality testing paradigm. To solve the modality confusion problem in MM-ReID, we propose a Cross-Identity Discrimination Harmonization Loss (CIDHL) adjusting the distribution of samples in the hyperspherical feature space, pulling the centers of samples with the same identity closer, and pushing away the centers of samples with different identities while aggregating samples with the same modality and the same identity. Furthermore, we propose a Modality Bridge Similarity Optimization Strategy (MBSOS) to optimize the cross-modality similarity between the query and queried samples with the help of the similar bridge sample in the gallery. Extensive experiments demonstrate that compared to the original performance of existing cross-modality methods on MM-ReID, the addition of our CIDHL and MBSOS demonstrates a general improvement.

Camouflaged Object Detection (COD) aims to segment camouflaged objects hidden within their environment. Existing COD models, aside from image features, mostly focus on a single coarse-grained spatial structure, such as depth information, texture information, or edge information. However, when faced with complex scenes where the target and background textures are similar and overlapping, or when subjected to noise interference, this design often leads to insufficient detection accuracy and robustness. To address these issues, we proposed a strategy for multiple spatial explorations and designed Spatial Bi-Exploration Network (SPNet). SPNet conducts a comprehensive analysis of complex camouflage scenarios by jointly exploring depth spatial, contour spatial, and image feature information, thereby enhancing detection performance and maintaining robustness. Unlike existing methods, SPNet leverages dual exploration of depth and contour spaces to mitigate the vulnerability of coarse structures to noise. Depth spatial information aids the model in recognizing the deep relationships between objects and the background, reducing the impact of noise on object boundaries, while contour spatial information improves edge detection accuracy. This dual approach significantly enhances robustness, especially in the face of adversarial attacks. Extensive experiments on benchmark datasets demonstrate that our model not only outperforms existing methods in detection performance but also exhibits superior robustness against adversarial attacks. The code address is https://github.com/pdsu-zjl/SPNet.