Text-to-visible & infrared person retrieval aims to retrieve the corresponding visible (RGB) and thermal infrared (TIR) images given the text descriptions. Existing methods perform semantic decoupling by aligning RGB and TIR features separately to different attributes, thereby facilitating the alignment between the fused multimodal representation and the text. However, insufficient TIR representation ability and cross-view representation capabilities of RGB and TIR modalities limit the retrieval accuracy and robustness. To address these issues, we propose a novel Dual-teacher Interactive Knowledge Distillation Network called DIKDNet, which performs the interactive knowledge distillation between two modality-specific teachers with rich cross-view representation capabilities to enhance TIR representations and the collaborative knowledge distillation from both teachers to the corresponding students to enhance the cross-modal cross-view representations, for robust text-to-visible & infrared person retrieval. Specifically, to enhance the representation ability of the TIR backbone network while preserving modality-specific characteristics, we design an Interactive Knowledge Distillation Module (IKDM), which introduces a boundary-constrained distillation strategy between RGB and TIR backbones, to transfer the semantic features of RGB backbone to TIR one. To enhance the cross-modal cross-view representation capability, we design a Collaborative Knowledge Distillation Module (CKDM) to transfer the cross-modal similarity relations and the cross-view multimodal representations from teacher networks to student ones. Experimental results demonstrate that our method consistently achieves significant performance gains on both the RGBT-PEDES and RGBNT201-PEDES datasets. The code will be released upon the acceptance.

Existing text-image person retrieval methods are built upon fixed-point or distributional embeddings, but they typically perform single-point alignment across modalities, making it difficult to effectively capture the one-to-many cross-modal semantic associations. To address this, we propose a One-to-Many Relation modEling network (OMRE) that explicitly constructs one-to-many semantic matching structures across modalities, thereby modeling richer and more diverse semantic associations. Specifically, to achieve one-to-many matching modeling, we design a bidirectional one-to-many alignment module, which constructs cross-modal matching distributions by aggregating relations between the mean embedding and multiple sampled embeddings, and minimizes their discrepancy with the true distribution to capture complex semantic associations. To construct fine-grained one-to-many matching relationships, we propose a collaborative reconstruction-based similarity refinement module, which maximizes the semantic consistency between multiple reconstructed masked tokens and the original tokens, effectively achieving robust and precise one-to-many cross-modal fine-grained semantic alignment. Moreover, to enhance the discriminative capability of one-to-many semantic distributions, we introduce a Hard Negative Mining mechanism that focuses on semantically similar but mismatched samples, helping to refine distribution boundaries in the probabilistic space and suppress interference from hard negative samples. Extensive experiments on three public datasets demonstrate that our method not only achieves superior overall performance but also exhibits excellent generalization ability. The code will be released on https://github.com/Yifei-AHU/OMRE.

Existing research on text-to-image person retrieval primarily focuses on visible images, which are not suitable under low-light scenarios. Infrared imaging becomes necessary in many visual systems, and matching text with both visible and infrared images is required. However, visible and infrared images are heterogeneous with different visual characteristics, so matching text with them in a unified framework is very challenging. In this work, we design a new task called Text-Visible/Infrared person retrieval and contribute a novel approach and a unified benchmark to promote the research and development of this field. On one hand, we propose a novel Attribute-guided feature decoupling and Collaborative Alignment Network (ACANet) that pursues accurate alignment from the text modality to both visible and infrared modalities in a unified framework according to the texture and color attribute information of text descriptions. In particular, we decouple the color features of visible images supervised by the text labels and integrate them into the infrared features to eliminate the impact of the absence of color information in infrared images during cross-modal collaborative alignment. Moreover, we also decouple the texture information from visible images supervised by the text labels and perform the collaborative alignment of texture and infrared features with a fusion agent. In addition, we extend conventional masked language modeling to a cross-modal paradigm to help ACANet learn uniform fine-grained alignment in multiple image modalities. On the other hand, we contribute a unified high-quality MM01LLCM-Text dataset, which provides person images in both visible and infrared modalities paired with fine-grained text descriptions. Experimental results show that the proposed ACANet outperforms existing state-of-the-art methods on MM01LLCM-Text dataset.

Existing Text-Image Person Retrieval (TIPR) methods have made substantial progress in modeling cross-modal associations via contrastive learning frameworks, but usually ignore the fine-grained differences in semantic relevance among different samples, which limits retrieval accuracy. To address this problem, we propose a novel Hierarchical Cross-modal Association framework HCA, which leverages the intra-modal fine-grained semantic relations distilled by single-modal pretrained models to constrain hierarchical cross-modal association between image and text modalities, for accurate TIPR. Specifically, to model hierarchical cross-modal semantic relationships, we propose a Hierarchical Relevance Matching (HRM) module. It partitions the matching strength of image-text pairs by jointly considering identity labels and cross-modal similarity, collaborating with unimodal similarity to construct a hierarchical relevance distribution that serves as a soft supervision signal. HRM not only helps the model better capture varying levels of semantic consistency between image-text pairs but also enhances the overall accuracy of cross-modal association learning. To enhance the ability to capture fine-grained cross-modal semantic relationships, we introduce an Image-guided Ambiguous text Token Modeling (IATM) module. It replaces original tokens with semantically ambiguous ones and leverages image guidance to detect and correct these tokens. This process further improves the fine-grained semantic alignment between images and texts. Experimental results demonstrate that HCA achieves new state-of-the-art performance across multiple datasets, thoroughly validating its effectiveness and advancement in cross-modal retrieval tasks.

A typical supply chain consists of suppliers, manufacturers, distributors, and customers, with supply, production, and distribution being the key links. Coordinating and optimizing these stages to reduce waste and shorten delivery times poses a significant challenge. However, most existing research relies on heuristic algorithms and focuses primarily on the production and distribution stages. In complex large-scale scenarios, the computational efficiency and adaptability of heuristic algorithms often fall short. This paper investigates a combined scheduling problem and a heterogeneous vehicle routing problem. Unlike traditional heuristic approaches, we propose a novel deep-reinforcement learning model. Finally, a Multi-Rollout algorithm is employed for collaborative training. Experimental results demonstrate that the proposed algorithm delivers competitive performance compared to heuristic methods while significantly outperforming them in computation time for individual instances.

Deep reinforcement learning (DRL) has been widely applied to address inventory management problems. To tackle the challenges posed by factors such as backlog, multisource replenishment, and demand priority in multi-echelon inventory systems, this paper proposes a value decomposition-based multi-agent reinforcement learning (MARL) framework. The framework utilizes independent DQN networks, embedded with self-attention and GRU modules, to facilitate distributed learning of local action value functions, thus simplifying the complexity of the action space. Additionally, a hybrid network based on the multi-head attention mechanism is constructed to approximate the joint action value function, aiming to optimize the overall system cost. Experiments have been conducted on various types of supply chain networks, and the results demonstrate the effectiveness and scalability of the proposed framework.

With the increasing complexity of the hardware design, conducting verification before the tapeout is of utmost importance. Simulation-based verification remains the primary method owing to its scalability and flexibility. A comprehensive verification of modern processors usually requires numerous effective tests to cover all possible conditions and use cases, leading to significant time, resource, and manual effort even with the EDA. Moreover, novel domain specific architecture (DSA), such as neuromorphic processors, will exacerbate the challenge of verification. Fortunately, emerging large language models (LLMs) have been demonstrating a powerful ability to complete specific tasks assigned by human instructions. In this paper, we explore the challenges and opportunities encountered when using the LLMs to accelerate the DSA verification using the proposed LLM-based workflow consisting of test generation, compilation&simulation, and result collection&processing. By verifying a RISC-V core and a neuromorphic processor, we examine the capabilities and limitations of the LLMs when using them for the function verification of traditional processors and emerging DSA. In the experiment, 36$C$programs and 128 assembly snippets for the RISC-V core and the neuromorphic processor are generated using an advanced LLM to demonstrate our claim. The experimental results show that the code coverage based on the LLM test generation can reach 89% and 91% for the above two architectures respectively, showing a promising research direction for the future processor verification in the new golden age for computer architecture.

Design verification (DV) has existed for decades and is crucial for identifying potential bugs before chip tape- out. Hand-crafting test cases is time-consuming and error-prone, even for experienced verification engineers. Prior work has attempted to lighten this burden by rule-guided random test case generation. However, this approach does not eliminate the manual effort required to write rules that describe detailed hardware behavior. Motivated by advances in large language models (LLMs), we explore their potential to capture register transfer level (RTL) behavior and construct prompts for test case generation based on RTL behavior. First, we introduce a prompt framework, LLM - Driven Test Generation (LLM - TG), to generate test cases, thereby enhancing LLMs' test generation capabilities. Additionally, we provide an open-source prompt library that offers a set of standardized prompts for processor verification, aiming to improve test generation efficiency. Lastly, we use an LLM to verify a 12-stage, multi-issue, out-of-order RV64GC processor, achieving at least an 8.34 % increase in block coverage and at least a 5.8 % increase in expression coverage compared to the state-of-the-art (SOTA) methods, LLM4DV and RISCV- DV. The prompt library is available at https://github.com/LLM-TGIPrompt_Library.

Since the standard license plate of large vehicle is easily affected by occlusion and stain, the traffic management department introduces the enlarged license plate at the rear of the large vehicle to assist license plate recognition. However, current researches regards standard license plate recognition and enlarged license plate recognition as independent tasks, and do not take advantage of the complementary benefits from the two types of license plates. In this work, we propose a new computer vision task called collaborative license plate recognition, aiming to leverage the complementary advantages of standard and enlarged license plates for achieving more accurate license plate recognition. To achieve this goal, we propose an Association Enhancement Network (AENet), which achieves robust collaborative licence plate recognition by capturing the correlations between characters within a single licence plate and enhancing the associations between two license plates. In particular, we design an association enhancement branch, which supervises the fusion of two licence plate information using the complete licence plate number to mine the association between them. To enhance the representation ability of each type of licence plates, we design an auxiliary learning branch in the training stage, which supervises the learning of individual license plates in the association enhancement between two license plates. In addition, we contribute a comprehensive benchmark dataset called CLPR, which consists of a total of 19,782 standard and enlarged licence plates from 24 provinces in China and covers most of the challenges in real scenarios, for collaborative license plate recognition. Extensive experiments on the proposed CLPR dataset demonstrate the effectiveness of the proposed AENet against several state-of-the-art methods.