pwc-archive/papers-with-abstracts huggingface dataset:
Learning Robust Stereo Matching in the Wild with Selective Mixture-of-Experts
Authors: Yun Wang, Longguang Wang, Chenghao Zhang, Yongjian Zhang, Zhanjie Zhang, Ao Ma, Chenyou Fan, Tin Lun Lam, Junjie Hu
Date: 2025-07-07 00:00:00
Tasks: Inductive Bias, Mixture-of-Experts, Stereo Matching

Recently, learning-based stereo matching networks have advanced significantly. However, they often lack robustness and struggle to achieve impressive cross-domain performance due to domain shifts and imbalanced disparity distributions among diverse datasets. Leveraging Vision Foundation Models (VFMs) can intuitively enhance the model's robustness, but integrating such a model into stereo matching cost-effectively to fully realize their robustness remains a key challenge. To address this, we propose SMoEStereo, a novel framework that adapts VFMs for stereo matching through a tailored, scene-specific fusion of Low-Rank Adaptation (LoRA) and Mixture-of-Experts (MoE) modules. SMoEStereo introduces MoE-LoRA with adaptive ranks and MoE-Adapter with adaptive kernel sizes. The former dynamically selects optimal experts within MoE to adapt varying scenes across domains, while the latter injects inductive bias into frozen VFMs to improve geometric feature extraction. Importantly, to mitigate computational overhead, we further propose a lightweight decision network that selectively activates MoE modules based on input complexity, balancing efficiency with accuracy. Extensive experiments demonstrate that our method exhibits state-of-the-art cross-domain and joint generalization across multiple benchmarks without dataset-specific adaptation. The code is available at \textcolor{red}{https://github.com/cocowy1/SMoE-Stereo}.
Taming Anomalies with Down-Up Sampling Networks: Group Center Preserving Reconstruction for 3D Anomaly Detection
Authors: Hanzhe Liang, Jie Zhang, Tao Dai, Linlin Shen, Jinbao Wang, Can Gao
Date: 2025-07-05 00:00:00
Tasks: 3D Anomaly Detection, Anomaly Detection

Reconstruction-based methods have demonstrated very promising results for 3D anomaly detection. However, these methods face great challenges in handling high-precision point clouds due to the large scale and complex structure. In this study, a Down-Up Sampling Network (DUS-Net) is proposed to reconstruct high-precision point clouds for 3D anomaly detection by preserving the group center geometric structure. The DUS-Net first introduces a Noise Generation module to generate noisy patches, which facilitates the diversity of training data and strengthens the feature representation for reconstruction. Then, a Down-sampling Network~(Down-Net) is developed to learn an anomaly-free center point cloud from patches with noise injection. Subsequently, an Up-sampling Network (Up-Net) is designed to reconstruct high-precision point clouds by fusing multi-scale up-sampling features. Our method leverages group centers for construction, enabling the preservation of geometric structure and providing a more precise point cloud. Extensive experiments demonstrate the effectiveness of our proposed method, achieving state-of-the-art (SOTA) performance with an Object-level AUROC of 79.9% and 79.5%, and a Point-level AUROC of 71.2% and 84.7% on the Real3D-AD and Anomaly-ShapeNet datasets, respectively.
Unsupervised Vehicle Re-Identification Based on Cross-Style Semi-Supervised Pre-Training and Feature Cross-Division
Authors: Zhan G, Wang Q, Min W, Han Q, Zhao H, Wei Z
Date: 2023-07-03 00:00:00
Proceeding: Electronics 2023 7
Tasks: Domain Adaptation, Pseudo Label, Style Transfer, Unsupervised Domain Adaptation, Unsupervised Vehicle Re-Identification, Vehicle Re-Identification

Vehicle Re-Identification (Re-ID) based on Unsupervised Domain Adaptation (UDA) has shown promising performance. However, two main issues still exist: (1) existing methods that use Generative Adversarial Networks (GANs) for domain gap alleviation combine supervised learning with hard labels of the source domain, resulting in a mismatch between style transfer data and hard labels; (2) pseudo label assignment in the fine-tuning stage is solely determined by similarity measures of global features using clustering algorithms, leading to inevitable label noise in generated pseudo labels. To tackle these issues, this paper proposes an unsupervised vehicle re-identification framework based on cross-style semi-supervised pre-training and feature cross-division. The framework consists of two parts: cross-style semi-supervised pre-training (CSP) and feature cross-division (FCD) for model fine-tuning. The CSP module generates style transfer data containing source domain content and target domain style using a style transfer network, and then pre-trains the model in a semi-supervised manner using both source domain and style transfer data. A pseudo-label reassignment strategy is designed to generate soft labels assigned to the style transfer data. The FCD module obtains feature partitions through a novel interactive division to reduce the dependence of pseudo-labels on global features, and the final similarity measurement combines the results of partition features and global features. Experimental results on the VehicleID and VeRi-776 datasets show that the proposed method outperforms existing unsupervised vehicle re-identification methods. Compared with the last best method on each dataset, the method proposed in this paper improves the mAP by 0.63% and the Rank-1 by 0.73% on the three sub-datasets of VehicleID on average, and it improves mAP by 0.9% and Rank-1 by 1% on VeRi-776 dataset.
Latent Thermodynamic Flows: Unified Representation Learning and Generative Modeling of Temperature-Dependent Behaviors from Limited Data
Authors: Yunrui Qiu, Richard John, Lukas Herron, Pratyush Tiwary
Date: 2025-07-03 00:00:00
Tasks: Benchmarking, Representation Learning

Accurate characterization of the equilibrium distributions of complex molecular systems and their dependence on environmental factors such as temperature is essential for understanding thermodynamic properties and transition mechanisms. Projecting these distributions onto meaningful low-dimensional representations enables interpretability and downstream analysis. Recent advances in generative AI, particularly flow models such as Normalizing Flows (NFs), have shown promise in modeling such distributions, but their scope is limited without tailored representation learning. In this work, we introduce Latent Thermodynamic Flows (LaTF), an end-to-end framework that tightly integrates representation learning and generative modeling. LaTF unifies the State Predictive Information Bottleneck (SPIB) with NFs to simultaneously learn low-dimensional latent representations, referred to as Collective Variables (CVs), classify metastable states, and generate equilibrium distributions across temperatures beyond the training data. The two components of representation learning and generative modeling are optimized jointly, ensuring that the learned latent features capture the system's slow, important degrees of freedom while the generative model accurately reproduces the system's equilibrium behavior. We demonstrate LaTF's effectiveness across diverse systems, including a model potential, the Chignolin protein, and cluster of Lennard Jones particles, with thorough evaluations and benchmarking using multiple metrics and extensive simulations. Finally, we apply LaTF to a RNA tetraloop system, where despite using simulation data from only two temperatures, LaTF reconstructs the temperature-dependent structural ensemble and melting behavior, consistent with experimental and prior extensive computational results.
Exploring Remote Physiological Signal Measurement under Dynamic Lighting Conditions at Night: Dataset, Experiment, and Analysis
Authors: Zhipeng Li, Kegang Wang, Hanguang Xiao, Xingyue Liu, Feizhong Zhou, Jiaxin Jiang, Tianqi Liu
Date: 2025-07-06 00:00:00
Tasks: Emotion Recognition

Remote photoplethysmography (rPPG) is a non-contact technique for measuring human physiological signals. Due to its convenience and non-invasiveness, it has demonstrated broad application potential in areas such as health monitoring and emotion recognition. In recent years, the release of numerous public datasets has significantly advanced the performance of rPPG algorithms under ideal lighting conditions. However, the effectiveness of current rPPG methods in realistic nighttime scenarios with dynamic lighting variations remains largely unknown. Moreover, there is a severe lack of datasets specifically designed for such challenging environments, which has substantially hindered progress in this area of research. To address this gap, we present and release a large-scale rPPG dataset collected under dynamic lighting conditions at night, named DLCN. The dataset comprises approximately 13 hours of video data and corresponding synchronized physiological signals from 98 participants, covering four representative nighttime lighting scenarios. DLCN offers high diversity and realism, making it a valuable resource for evaluating algorithm robustness in complex conditions. Built upon the proposed Happy-rPPG Toolkit, we conduct extensive experiments and provide a comprehensive analysis of the challenges faced by state-of-the-art rPPG methods when applied to DLCN. The dataset and code are publicly available at https://github.com/dalaoplan/Happp-rPPG-Toolkit.
Hasaan Raza
Authors: Hasaan Raza
Date: 2025-07-08 00:00:00
Proceeding: July 8 2025 7

Entrepreneur and founder of Civsec Group, an investment firm focused on acquiring and growing companies in the security sector. Passionate about innovation, strategy, and building a strong portfolio, shaping the future of safety and protection.
Differential Attention for Multimodal Crisis Event Analysis
Authors: Nusrat Munia, Junfeng Zhu, Olfa Nasraoui, Abdullah-Al-Zubaer Imran
Date: 2025-07-07 00:00:00
Tasks: Disaster Response, Humanitarian

Social networks can be a valuable source of information during crisis events. In particular, users can post a stream of multimodal data that can be critical for real-time humanitarian response. However, effectively extracting meaningful information from this large and noisy data stream and effectively integrating heterogeneous data remains a formidable challenge. In this work, we explore vision language models (VLMs) and advanced fusion strategies to enhance the classification of crisis data in three different tasks. We incorporate LLaVA-generated text to improve text-image alignment. Additionally, we leverage Contrastive Language-Image Pretraining (CLIP)-based vision and text embeddings, which, without task-specific fine-tuning, outperform traditional models. To further refine multimodal fusion, we employ Guided Cross Attention (Guided CA) and combine it with the Differential Attention mechanism to enhance feature alignment by emphasizing critical information while filtering out irrelevant content. Our results show that while Differential Attention improves classification performance, Guided CA remains highly effective in aligning multimodal features. Extensive experiments on the CrisisMMD benchmark data set demonstrate that the combination of pretrained VLMs, enriched textual descriptions, and adaptive fusion strategies consistently outperforms state-of-the-art models in classification accuracy, contributing to more reliable and interpretable models for three different tasks that are crucial for disaster response. Our code is available at https://github.com/Munia03/Multimodal_Crisis_Event.
Image-to-image domain adaptation for vehicle re-identification
Authors: Fukai Zhang, Lulu Zhang, Haiyan Zhang, Yongqiang Ma
Date: 2023-03-30 00:00:00
Proceeding: Multimed Tools Appl 2023 3
Tasks: Domain Adaptation, Generative Adversarial Network, Image Super-Resolution, Super-Resolution, Unsupervised Domain Adaptation, Vehicle Re-Identification

Cross-domain vehicle re-identification (ReID) is an interesting but challenging task in computer vision. A ReID model well-trained on one dataset often experiences a severe performance drop when applied to another dataset due to the domain discrepancy between the different datasets. This is especially true for low-resolution images. In this paper, we present a vehicle image domain adaptation framework (VDAF) which contains a single-image super resolution network (SISR) and a vehicle transfer generative adversarial network (VTGAN). SISR is an enhancement task for mapping low-resolution (LR) images to high-resolution (HR) images. Based on the reconstructed HR images, VTGAN can translate vehicle images from a source domain to a target domain with consistent styles and identities. VTGAN is an unsupervised approach designed for source-target translation for vehicle ReID and is composed of two adversarial networks and one Siamese network. Based on the translated images, we can infer an enhanced vehicle representation free of influences from style variations, allowing distance metrics for vehicle ReID to be learned. Through extensive experiments on the VeRi, VehicleID, and VRIC datasets, we show that images translated by VTGAN are effective for domain adaptation and are superior at promoting the accuracy of vehicle ReID.
Progressive learning with multi-scale attention network for cross-domain vehicle re-identification
Authors: Yang Wang, Jinjia Peng, Huibing Wang, Meng Wang
Date: 2021-11-19 00:00:00
Proceeding: Sci. China Inf. Sci 2021 11
Tasks: Domain Adaptation, Unsupervised Domain Adaptation, Vehicle Re-Identification

Vehicle re-identification (reID) aims to identify vehicles across different cameras that have non-overlapping views. Most existing vehicle reID approaches train the reID model with well-labeled datasets via a supervised manner, which inevitably causes a severe drop in performance when tested in an unknown domain. Moreover, these supervised approaches require full annotations, which is limiting owing to the amount of unlabeled data. Therefore, with the aim of addressing the aforementioned problems, unsupervised vehicle reID models have attracted considerable attention. It always adopts domain adaptation to transfer discriminative information from supervised domains to unsupervised ones. In this paper, a novel progressive learning method with a multi-scale fusion network is proposed, named PLM, for vehicle reID in the unknown domain, which directly exploits inference from the available abundant data without any annotations. For PLM, a domain adaptation module is employed to smooth the domain bias, which generates images with similar data distribution to unlabeled target domain as “pseudo target samples”. Furthermore, to better exploit the distinct features of vehicle images in the unknown domain, a multi-scale attention network is proposed to train the reID model with the “pseudo target samples” and unlabeled samples; this network embeds low-layer texture features with high-level semantic features to train the reID model. Moreover, a weighted label smoothing (WLS) loss is proposed, which considers the distance between samples and different clusters to balance the confidence of pseudo labels in the feature learning module. Extensive experiments are carried out to verify that our proposed PLM achieves excellent performance on several benchmark datasets.
RAG-R1 : Incentivize the Search and Reasoning Capabilities of LLMs through Multi-query Parallelism
Authors: Zhiwen Tan, Jiaming Huang, Qintong Wu, Hongxuan Zhang, Chenyi Zhuang, Jinjie Gu
Date: 2025-06-30 00:00:00
Tasks: Question Answering, RAG, Reinforcement Learning (RL), Retrieval, Retrieval-augmented Generation

Large Language Models (LLMs) have demonstrated remarkable capabilities across various tasks, while they remain prone to generating hallucinated or outdated responses due to their static internal knowledge. Recent advancements in Retrieval-Augmented Generation (RAG) methods have explored enhancing models' search and reasoning capabilities through reinforcement learning (RL). Although these methods demonstrate promising results, they face challenges in training stability and encounter issues such as substantial inference time and restricted capabilities due to the single-query mode. In this paper, we propose RAG-R1, a novel training framework designed to enable LLMs to adaptively leverage internal and external knowledge during the reasoning process. We further expand the generation and retrieval processes within the framework from single-query mode to multi-query parallelism, aimed at reducing inference time and enhancing the model's capabilities. Extensive experiments on seven question-answering benchmarks demonstrate that our method outperforms the strongest baseline by up to 13.2% and decreases inference time by 11.1%.
Latent Chain-of-Thought? Decoding the Depth-Recurrent Transformer
Authors: Wenquan Lu, Yuechuan Yang, Kyle Lee, Yanshu Li, Enqi Liu
Date: 2025-07-02 00:00:00

Chain-of-thought (CoT) reasoning has enabled transformer-based language models to excel at complex mathematics and multi-step planning. However, in standard decoder-only architectures, these reasoning steps are externalized in natural language, improving interpretability at the cost of efficiency. To capture reasoning that is not easily represented in words, many works have explored recurrent architectures that aim to internalize reasoning in latent space, potentially supporting latent CoT. In this paper, we investigate whether such reasoning structures emerge in Huginn-3.5B, a depth-recurrent Transformer that reuses layers at inference time without increasing parameter count. We examine the model's internal behavior on arithmetic tasks using a suite of probing techniques including the Logit Lens and Coda Lens. Our findings reveal limited evidence of interpretable latent CoT by tracking rank trajectories of final and intermediate result tokens. Furthermore, we uncover significant probing inconsistencies across recurrent blocks, where the interpretability of hidden states depends heavily on both the layer index and the decoding method. Finally, we empirically show that increasing recurrence depth yields only marginal gains and falls well short of models that explicitly externalize reasoning steps. The code is available at https://github.com/wenquanlu/huginn-latent-cot.
CLIP-Guided Backdoor Defense through Entropy-Based Poisoned Dataset Separation
Authors: Binyan Xu, Fan Yang, Xilin Dai, Di Tang, Kehuan Zhang
Date: 2025-07-07 00:00:00
Tasks: backdoor defense

Deep Neural Networks (DNNs) are susceptible to backdoor attacks, where adversaries poison training data to implant backdoor into the victim model. Current backdoor defenses on poisoned data often suffer from high computational costs or low effectiveness against advanced attacks like clean-label and clean-image backdoors. To address them, we introduce CLIP-Guided backdoor Defense (CGD), an efficient and effective method that mitigates various backdoor attacks. CGD utilizes a publicly accessible CLIP model to identify inputs that are likely to be clean or poisoned. It then retrains the model with these inputs, using CLIP's logits as a guidance to effectively neutralize the backdoor. Experiments on 4 datasets and 11 attack types demonstrate that CGD reduces attack success rates (ASRs) to below 1% while maintaining clean accuracy (CA) with a maximum drop of only 0.3%, outperforming existing defenses. Additionally, we show that clean-data-based defenses can be adapted to poisoned data using CGD. Also, CGD exhibits strong robustness, maintaining low ASRs even when employing a weaker CLIP model or when CLIP itself is compromised by a backdoor. These findings underscore CGD's exceptional efficiency, effectiveness, and applicability for real-world backdoor defense scenarios. Code: https://github.com/binyxu/CGD.
Stochastic Human Motion Prediction with Memory of Action Transition and Action Characteristic
Authors: Jianwei Tang, Hong Yang, Tengyue Chen, Jian-Fang Hu
Date: 2025-07-05 00:00:00
Proceeding: CVPR 2025 1
Tasks: Human motion prediction, motion prediction, Stochastic Human Motion Prediction

Action-driven stochastic human motion prediction aims to generate future motion sequences of a pre-defined target action based on given past observed sequences performing non-target actions. This task primarily presents two challenges. Firstly, generating smooth transition motions is hard due to the varying transition speeds of different actions. Secondly, the action characteristic is difficult to be learned because of the similarity of some actions. These issues cause the predicted results to be unreasonable and inconsistent. As a result, we propose two memory banks, the Soft-transition Action Bank (STAB) and Action Characteristic Bank (ACB), to tackle the problems above. The STAB stores the action transition information. It is equipped with the novel soft searching approach, which encourages the model to focus on multiple possible action categories of observed motions. The ACB records action characteristic, which produces more prior information for predicting certain actions. To fuse the features retrieved from the two banks better, we further propose the Adaptive Attention Adjustment (AAA) strategy. Extensive experiments on four motion prediction datasets demonstrate that our approach consistently outperforms the previous state-of-the-art. The demo and code are available at https://hyqlat.github.io/STABACB.github.io/.
Model Inversion Attacks on Llama 3: Extracting PII from Large Language Models
Authors: Sathesh P. Sivashanmugam
Date: 2025-07-06 00:00:00
Tasks: Privacy Preserving

Large language models (LLMs) have transformed natural language processing, but their ability to memorize training data poses significant privacy risks. This paper investigates model inversion attacks on the Llama 3.2 model, a multilingual LLM developed by Meta. By querying the model with carefully crafted prompts, we demonstrate the extraction of personally identifiable information (PII) such as passwords, email addresses, and account numbers. Our findings highlight the vulnerability of even smaller LLMs to privacy attacks and underscore the need for robust defenses. We discuss potential mitigation strategies, including differential privacy and data sanitization, and call for further research into privacy-preserving machine learning techniques.
Temporal Continual Learning with Prior Compensation for Human Motion Prediction
Authors: Jianwei Tang, Jiangxin Sun, Xiaotong LIN, Lifang Zhang, Wei-Shi Zheng, Jian-Fang Hu
Date: 2025-07-05 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Continual Learning, Human motion prediction, motion prediction

Human Motion Prediction (HMP) aims to predict future poses at different moments according to past motion sequences. Previous approaches have treated the prediction of various moments equally, resulting in two main limitations: the learning of short-term predictions is hindered by the focus on long-term predictions, and the incorporation of prior information from past predictions into subsequent predictions is limited. In this paper, we introduce a novel multi-stage training framework called Temporal Continual Learning (TCL) to address the above challenges. To better preserve prior information, we introduce the Prior Compensation Factor (PCF). We incorporate it into the model training to compensate for the lost prior information. Furthermore, we derive a more reasonable optimization objective through theoretical derivation. It is important to note that our TCL framework can be easily integrated with different HMP backbone models and adapted to various datasets and applications. Extensive experiments on four HMP benchmark datasets demonstrate the effectiveness and flexibility of TCL. The code is available at https://github.com/hyqlat/TCL.