arXiv subjects

Baolong Liu

Publications and source records attributed to Baolong Liu.

5 recordsLinked to original sources

arXiv2026

SCSimulator: An Exploratory Visual Analytics Framework for Partner Selection in Supply Chains through LLM-driven Multi-Agent Simulation

Supply chains (SCs), complex networks spanning from raw material acquisition to product delivery, with enterprises as interconnected nodes, play a pivotal role in organizational success. However, optimizing SCs remains challenging, particularly in partner selection, a key bottleneck shaped by competitive and cooperative dynamics. This challenge constitutes a multi-objective dynamic game requiring a synergistic integration of Multi-Criteria Decision-Making and Game Theory. Traditional approaches, grounded in mathematical simplifications and managerial heuristics, fail to capture real-world intricacies and risk introducing subjective biases. Multi-agent simulation offers promise, but prior research has largely relied on fixed, uniform agent logic, limiting practical applicability. Recent advances in LLMs create opportunities to represent complex SC requirements and hybrid game logic. However, challenges persist in modeling dynamic SC relationships, ensuring interpretability, and balancing agent autonomy with expert control. We present SCSimulator, a visual analytics framework that integrates LLM-driven MAS with human-in-the-loop collaboration for SC partner selection. It simulates SC evolution via adaptive network structures and enterprise behaviors, which are visualized via interpretable interfaces. By combining CoT reasoning with XAI techniques, it generates multi-faceted, transparent explanations of decision trade-offs. Users can iteratively adjust simulation settings to explore outcomes aligned with their expectations and strategic priorities. Developed through iterative co-design with SC experts and industry managers, SCSimulator serves as a proof-of-concept, offering methodological contributions and practical insights for future research on SC decision-making and interactive AI-driven analytics. Usage scenarios and a user study demonstrate the system's effectiveness and usability.

cs.HC↗

arXiv2023

From Region to Patch: Attribute-Aware Foreground-Background Contrastive Learning for Fine-Grained Fashion Retrieval

Attribute-specific fashion retrieval (ASFR) is a challenging information retrieval task, which has attracted increasing attention in recent years. Different from traditional fashion retrieval which mainly focuses on optimizing holistic similarity, the ASFR task concentrates on attribute-specific similarity, resulting in more fine-grained and interpretable retrieval results. As the attribute-specific similarity typically corresponds to the specific subtle regions of images, we propose a Region-to-Patch Framework (RPF) that consists of a region-aware branch and a patch-aware branch to extract fine-grained attribute-related visual features for precise retrieval in a coarse-to-fine manner. In particular, the region-aware branch is first to be utilized to locate the potential regions related to the semantic of the given attribute. Then, considering that the located region is coarse and still contains the background visual contents, the patch-aware branch is proposed to capture patch-wise attribute-related details from the previous amplified region. Such a hybrid architecture strikes a proper balance between region localization and feature extraction. Besides, different from previous works that solely focus on discriminating the attribute-relevant foreground visual features, we argue that the attribute-irrelevant background features are also crucial for distinguishing the detailed visual contexts in a contrastive manner. Therefore, a novel E-InfoNCE loss based on the foreground and background representations is further proposed to improve the discrimination of attribute-specific representation. Extensive experiments on three datasets demonstrate the effectiveness of our proposed framework, and also show a decent generalization of our RPF on out-of-domain fashion images. Our source code is available at https://github.com/HuiGuanLab/RPF.

cs.CV↗

arXiv2022

Hierarchical Contrast for Unsupervised Skeleton-based Action Representation Learning

This paper targets unsupervised skeleton-based action representation learning and proposes a new Hierarchical Contrast (HiCo) framework. Different from the existing contrastive-based solutions that typically represent an input skeleton sequence into instance-level features and perform contrast holistically, our proposed HiCo represents the input into multiple-level features and performs contrast in a hierarchical manner. Specifically, given a human skeleton sequence, we represent it into multiple feature vectors of different granularities from both temporal and spatial domains via sequence-to-sequence (S2S) encoders and unified downsampling modules. Besides, the hierarchical contrast is conducted in terms of four levels: instance level, domain level, clip level, and part level. Moreover, HiCo is orthogonal to the S2S encoder, which allows us to flexibly embrace state-of-the-art S2S encoders. Extensive experiments on four datasets, i.e., NTU-60, NTU-120, PKU-MMD I and II, show that HiCo achieves a new state-of-the-art for unsupervised skeleton-based action representation learning in two downstream tasks including action recognition and retrieval, and its learned action representation is of good transferability. Besides, we also show that our framework is effective for semi-supervised skeleton-based action recognition. Our code is available at https://github.com/HuiGuanLab/HiCo.

cs.CV↗

arXiv2022

Progressive Localization Networks for Language-based Moment Localization

This paper targets the task of language-based video moment localization. The language-based setting of this task allows for an open set of target activities, resulting in a large variation of the temporal lengths of video moments. Most existing methods prefer to first sample sufficient candidate moments with various temporal lengths, and then match them with the given query to determine the target moment. However, candidate moments generated with a fixed temporal granularity may be suboptimal to handle the large variation in moment lengths. To this end, we propose a novel multi-stage Progressive Localization Network (PLN) which progressively localizes the target moment in a coarse-to-fine manner. Specifically, each stage of PLN has a localization branch, and focuses on candidate moments that are generated with a specific temporal granularity. The temporal granularities of candidate moments are different across the stages. Moreover, we devise a conditional feature manipulation module and an upsampling connection to bridge the multiple localization branches. In this fashion, the later stages are able to absorb the previously learned information, thus facilitating the more fine-grained localization. Extensive experiments on three public datasets demonstrate the effectiveness of our proposed PLN for language-based moment localization, especially for localizing short moments in long videos.

cs.CV↗

arXiv2009

XML Data Integrity Based on Concatenated Hash Function

Data integrity is the fundamental for data authentication. A major problem for XML data authentication is that signed XML data can be copied to another document but still keep signature valid. This is caused by XML data integrity protecting. Through investigation, the paper discovered that besides data content integrity, XML data integrity should also protect element location information, and context referential integrity under fine-grained security situation. The aim of this paper is to propose a model for XML data integrity considering XML data features. The paper presents an XML data integrity model named as CSR (content integrity, structure integrity, context referential integrity) based on a concatenated hash function. XML data content integrity is ensured using an iterative hash process, structure integrity is protected by hashing an absolute path string from root node, and context referential integrity is ensured by protecting context-related elements. Presented XML data integrity model can satisfy integrity requirements under situation of fine-grained security, and compatible with XML signature. Through evaluation, the integrity model presented has a higher efficiency on digest value-generation than the Merkle hash tree-based integrity model for XML data.

cs.SE↗