pwc-archive/papers-with-abstracts huggingface dataset:
Item Level Exploration Traffic Allocation in Large-scale Recommendation Systems
Authors: Dong Wang, Junyi Jiao, Arnab Bhadury, Yaping Zhang, Mingyan Gao
Date: 2025-05-14 00:00:00
Tasks: Recommendation Systems

This paper contributes to addressing the item cold start problem in large-scale recommender systems, focusing on how to efficiently gain initial visibility for newly ingested content. We propose an exploration system designed to efficiently allocate impressions to these fresh items. Our approach leverages a learned probabilistic model to predict an item's discoverability, which then informs a scalable and adaptive traffic allocation strategy. This system intelligently distributes exploration budgets, optimizing for the long-term benefit of the recommendation platform. The impact is a demonstrably more efficient cold-start process, leading to a significant increase in the discoverability of new content and ultimately enriching the item corpus available for exploitation, as evidenced by its successful deployment in a large-scale production environment.
Aligning Anime Video Generation with Human Feedback
Authors: Bingwen Zhu, Yudong Jiang, Baohan Xu, Siqian Yang, Mingyu Yin, Yidi Wu, Huyang Sun, Zuxuan Wu
Date: 2025-04-14 00:00:00
Tasks: Video Generation

Anime video generation faces significant challenges due to the scarcity of anime data and unusual motion patterns, leading to issues such as motion distortion and flickering artifacts, which result in misalignment with human preferences. Existing reward models, designed primarily for real-world videos, fail to capture the unique appearance and consistency requirements of anime. In this work, we propose a pipeline to enhance anime video generation by leveraging human feedback for better alignment. Specifically, we construct the first multi-dimensional reward dataset for anime videos, comprising 30k human-annotated samples that incorporating human preferences for both visual appearance and visual consistency. Based on this, we develop AnimeReward, a powerful reward model that employs specialized vision-language models for different evaluation dimensions to guide preference alignment. Furthermore, we introduce Gap-Aware Preference Optimization (GAPO), a novel training method that explicitly incorporates preference gaps into the optimization process, enhancing alignment performance and efficiency. Extensive experiment results show that AnimeReward outperforms existing reward models, and the inclusion of GAPO leads to superior alignment in both quantitative benchmarks and human evaluations, demonstrating the effectiveness of our pipeline in enhancing anime video quality. Our dataset and code will be publicly available.
Mitigating the Modality Gap: Few-Shot Out-of-Distribution Detection with Multi-modal Prototypes and Image Bias Estimation
Authors: Yimu Wang, Evelien Riddell, Adrian Chow, Sean Sedwards, Krzysztof Czarnecki
Date: 2025-02-02 00:00:00
Tasks: Out-of-Distribution Detection, Out of Distribution (OOD) Detection

Existing vision-language model (VLM)-based methods for out-of-distribution (OOD) detection typically rely on similarity scores between input images and in-distribution (ID) text prototypes. However, the modality gap between image and text often results in high false positive rates, as OOD samples can exhibit high similarity to ID text prototypes. To mitigate the impact of this modality gap, we propose incorporating ID image prototypes along with ID text prototypes. We present theoretical analysis and empirical evidence indicating that this approach enhances VLM-based OOD detection performance without any additional training. To further reduce the gap between image and text, we introduce a novel few-shot tuning framework, SUPREME, comprising biased prompts generation (BPG) and image-text consistency (ITC) modules. BPG enhances image-text fusion and improves generalization by conditioning ID text prototypes on the Gaussian-based estimated image domain bias; ITC reduces the modality gap by minimizing intra- and inter-modal distances. Moreover, inspired by our theoretical and empirical findings, we introduce a novel OOD score $S_{\textit{GMP}}$, leveraging uni- and cross-modal similarities. Finally, we present extensive experiments to demonstrate that SUPREME consistently outperforms existing VLM-based OOD detection methods.
FedC4: Graph Condensation Meets Client-Client Collaboration for Efficient and Private Federated Graph Learning
Authors: Zekai Chen, Xunkai Li, Yinlin Zhu, Rong-Hua Li, Guoren Wang
Date: 2025-04-19 00:00:00
Tasks: Graph Learning

Federated Graph Learning (FGL) is an emerging distributed learning paradigm that enables collaborative model training over decentralized graph-structured data while preserving local privacy. Existing FGL methods can be categorized into two optimization architectures: (1) the Server-Client (S-C) paradigm, where clients upload local models for server-side aggregation; and (2) the Client-Client (C-C) paradigm, which allows direct information exchange among clients to support personalized training. Compared to S-C, the C-C architecture better captures global graph knowledge and enables fine-grained optimization through customized peer-to-peer communication. However, current C-C methods often broadcast identical and redundant node embeddings, incurring high communication costs and privacy risks. To address this, we propose FedC4, a novel framework that combines graph Condensation with Client-Client Collaboration. Instead of transmitting raw node-level features, FedC4 distills each client's private graph into a compact set of synthetic node embeddings, reducing communication overhead and enhancing privacy. In addition, FedC4 introduces three modules that allow source clients to send distinct node representations tailored to target clients'graph structures, enabling personalized optimization with global guidance. Extensive experiments on eight real-world datasets show that FedC4 outperforms state-of-the-art baselines in both performance and communication efficiency.
STCOcc: Sparse Spatial-Temporal Cascade Renovation for 3D Occupancy and Scene Flow Prediction
Authors: Zhimin Liao, Ping Wei, Shuaijia Chen, Haoxuan Wang, Ziyang Ren
Date: 2025-04-28 00:00:00
Proceeding: CVPR 2025 1
Tasks: GPU

3D occupancy and scene flow offer a detailed and dynamic representation of 3D scene. Recognizing the sparsity and complexity of 3D space, previous vision-centric methods have employed implicit learning-based approaches to model spatial and temporal information. However, these approaches struggle to capture local details and diminish the model's spatial discriminative ability. To address these challenges, we propose a novel explicit state-based modeling method designed to leverage the occupied state to renovate the 3D features. Specifically, we propose a sparse occlusion-aware attention mechanism, integrated with a cascade refinement strategy, which accurately renovates 3D features with the guidance of occupied state information. Additionally, we introduce a novel method for modeling long-term dynamic interactions, which reduces computational costs and preserves spatial information. Compared to the previous state-of-the-art methods, our efficient explicit renovation strategy not only delivers superior performance in terms of RayIoU and mAVE for occupancy and scene flow prediction but also markedly reduces GPU memory usage during training, bringing it down to 8.7GB. Our code is available on https://github.com/lzzzzzm/STCOcc
From Events to Enhancement: A Survey on Event-Based Imaging Technologies
Authors: Yunfan Lu, Xiaogang Xu, Pengteng Li, Yusheng Wang, Yi Cui, Huizai Yao, Hui Xiong
Date: 2025-04-30 00:00:00
Tasks: Video Enhancement

Event cameras offering high dynamic range and low latency have emerged as disruptive technologies in imaging. Despite growing research on leveraging these benefits for different imaging tasks, a comprehensive study of recently advances and challenges are still lacking. This limits the broader understanding of how to utilize events in universal imaging applications. In this survey, we first introduce a physical model and the characteristics of different event sensors as the foundation. Following this, we highlight the advancement and interaction of image/video enhancement tasks with events. Additionally, we explore advanced tasks, which capture richer light information with events, \eg~light field estimation, multi-view generation, and photometric. Finally, we discuss new challenges and open questions offering a perspective for this rapidly evolving field. More continuously updated resources are at this link: https://github.com/yunfanLu/Awesome-Event-Imaging
Real-time object detection and tracking using flash LiDAR imagery
Authors: Daniel Carvalho, Art Lompado, Riccardo Consolo, Abhijit Bhattacharjee, Jarrod Brown
Date: 2024-06-07 00:00:00
Proceeding: Proc. SPIE, Automatic Target Recognition XXXIV 2024 6
Tasks: 3D Object Classification, Object, object-detection, Object Detection, Real-Time Object Detection, vehicle detection

In this study, we present a real-time vehicle detection program that combines the You-Only-Look-Once-X (YOLOX) object detection algorithm with a multi-object Kalman filter tracker, specifically designed for analyzing 3D light detection and ranging (LiDAR) data. The use of an active imager, such as LiDAR, offers significant advantages over conventional passive 2D imagery. By providing its illumination source, LiDAR eliminates color fluctuations caused by shadowing or diurnal cycling, resulting in improved precision and accuracy for object detection and classification. Our approach involves capturing videos of 8 vehicles using an Advanced Scientific Concepts TigerCub 3D Flash LiDAR camera, which provides intensity and range data sequences. These sequences are then converted into representative color images, which are used to train the YOLOX object detector neural network.

To further enhance the detection accuracy for obscured vehicles and minimize the wrong detection rate, we integrate Kalman filter trackers into the detection algorithm. These trackers identify the vehicles and predict their future locations, effectively reducing both false positive and false negative detections. The resulting algorithm is lightweight and capable of producing highly accurate inference results in near real-time on a live-stream of LiDAR data.

To demonstrate the applicability of our approach on small, unmanned vehicles/drones, we deploy the application on NVIDIA's Jetson Orin Nano embedded processor for AI. By optimizing the code using TensorRT for real-time performance, we achieve object detection and classification of flash LiDAR data at an average precision exceeding 95% and a rate of 60 frames-per-second. MATLAB plays a crucial role in enabling rapid prototyping and algorithm testing, facilitating the smooth transfer and deployment of the complex deep learning logic to an edge device without compromising performance or accuracy.
I Know What I Don't Know: Improving Model Cascades Through Confidence Tuning
Authors: Stephan Rabanser, Nathalie Rauschmayr, Achin Kulshrestha, Petra Poklukar, Wittawat Jitkrittum, Sean Augenstein, Congchao Wang, Federico Tombari
Date: 2025-02-26 00:00:00
Tasks: Decoder, image-classification, Image Classification, Language Modeling, Language Modelling

Large-scale machine learning models deliver strong performance across a wide range of tasks but come with significant computational and resource constraints. To mitigate these challenges, local smaller models are often deployed alongside larger models, relying on routing and deferral mechanisms to offload complex tasks. However, existing approaches inadequately balance the capabilities of these models, often resulting in unnecessary deferrals or sub-optimal resource usage. In this work we introduce a novel loss function called Gatekeeper for calibrating smaller models in cascade setups. Our approach fine-tunes the smaller model to confidently handle tasks it can perform correctly while deferring complex tasks to the larger model. Moreover, it incorporates a mechanism for managing the trade-off between model performance and deferral accuracy, and is broadly applicable across various tasks and domains without any architectural changes. We evaluate our method on encoder-only, decoder-only, and encoder-decoder architectures. Experiments across image classification, language modeling, and vision-language tasks show that our approach substantially improves deferral performance.
STFM: A Spatio-Temporal Information Fusion Model Based on Phase Space Reconstruction for Sea Surface Temperature Prediction
Authors: Yin Wang, Chunlin Gong, Xiang Wu, Hanleran Zhang
Date: 2025-04-23 00:00:00
Tasks: Prediction

The sea surface temperature (SST), a key environmental parameter, is crucial to optimizing production planning, making its accurate prediction a vital research topic. However, the inherent nonlinearity of the marine dynamic system presents significant challenges. Current forecasting methods mainly include physics-based numerical simulations and data-driven machine learning approaches. The former, while describing SST evolution through differential equations, suffers from high computational complexity and limited applicability, whereas the latter, despite its computational benefits, requires large datasets and faces interpretability challenges. This study presents a prediction framework based solely on data-driven techniques. Using phase space reconstruction, we construct initial-delay attractor pairs with a mathematical homeomorphism and design a Spatio-Temporal Fusion Mapping (STFM) to uncover their intrinsic connections. Unlike conventional models, our method captures SST dynamics efficiently through phase space reconstruction and achieves high prediction accuracy with minimal training data in comparative tests
The Geography of Transportation Cybersecurity: Visitor Flows, Industry Clusters, and Spatial Dynamics
Authors: Yuhao Wang, Kailai Wang, Songhua Hu, Yunpeng, Zhang, Gino Lim, Pengyu Zhu
Date: 2025-05-12 00:00:00
Tasks: Clustering

The rapid evolution of the transportation cybersecurity ecosystem, encompassing cybersecurity, automotive, and transportation and logistics sectors, will lead to the formation of distinct spatial clusters and visitor flow patterns across the US. This study examines the spatiotemporal dynamics of visitor flows, analyzing how socioeconomic factors shape industry clustering and workforce distribution within these evolving sectors. To model and predict visitor flow patterns, we develop a BiTransGCN framework, integrating an attention-based Transformer architecture with a Graph Convolutional Network backbone. By integrating AI-enabled forecasting techniques with spatial analysis, this study improves our ability to track, interpret, and anticipate changes in industry clustering and mobility trends, thereby supporting strategic planning for a secure and resilient transportation network. It offers a data-driven foundation for economic planning, workforce development, and targeted investments in the transportation cybersecurity ecosystem.
Self-Supervised Transformer-based Contrastive Learning for Intrusion Detection Systems
Authors: Ippokratis Koukoulis, Ilias Syrigos, Thanasis Korakis
Date: 2025-05-12 00:00:00
Tasks: Anomaly Detection, Contrastive Learning, Data Augmentation, Intrusion Detection

As the digital landscape becomes more interconnected, the frequency and severity of zero-day attacks, have significantly increased, leading to an urgent need for innovative Intrusion Detection Systems (IDS). Machine Learning-based IDS that learn from the network traffic characteristics and can discern attack patterns from benign traffic offer an advanced solution to traditional signature-based IDS. However, they heavily rely on labeled datasets, and their ability to generalize when encountering unseen traffic patterns remains a challenge. This paper proposes a novel self-supervised contrastive learning approach based on transformer encoders, specifically tailored for generalizable intrusion detection on raw packet sequences. Our proposed learning scheme employs a packet-level data augmentation strategy combined with a transformer-based architecture to extract and generate meaningful representations of traffic flows. Unlike traditional methods reliant on handcrafted statistical features (NetFlow), our approach automatically learns comprehensive packet sequence representations, significantly enhancing performance in anomaly identification tasks and supervised learning for intrusion detection. Our transformer-based framework exhibits better performance in comparison to existing NetFlow self-supervised methods. Specifically, we achieve up to a 3% higher AUC in anomaly detection for intra-dataset evaluation and up to 20% higher AUC scores in inter-dataset evaluation. Moreover, our model provides a strong baseline for supervised intrusion detection with limited labeled data, exhibiting an improvement over self-supervised NetFlow models of up to 1.5% AUC when pretrained and evaluated on the same dataset. Additionally, we show the adaptability of our pretrained model when fine-tuned across different datasets, demonstrating strong performance even when lacking benign data from the target domain.
AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection
Authors: Bin-Bin Gao, Yue Zhou, Jiangtao Yan, Yuezhi Cai, Weixi Zhang, Meng Wang, Jun Liu, Yong liu, Lei Wang, Chengjie Wang
Date: 2025-05-15 00:00:00
Tasks: Anomaly Detection

Universal visual anomaly detection aims to identify anomalies from novel or unseen vision domains without additional fine-tuning, which is critical in open scenarios. Recent studies have demonstrated that pre-trained vision-language models like CLIP exhibit strong generalization with just zero or a few normal images. However, existing methods struggle with designing prompt templates, complex token interactions, or requiring additional fine-tuning, resulting in limited flexibility. In this work, we present a simple yet effective method called AdaptCLIP based on two key insights. First, adaptive visual and textual representations should be learned alternately rather than jointly. Second, comparative learning between query and normal image prompt should incorporate both contextual and aligned residual features, rather than relying solely on residual features. AdaptCLIP treats CLIP models as a foundational service, adding only three simple adapters, visual adapter, textual adapter, and prompt-query adapter, at its input or output ends. AdaptCLIP supports zero-/few-shot generalization across domains and possesses a training-free manner on target domains once trained on a base dataset. AdaptCLIP achieves state-of-the-art performance on 12 anomaly detection benchmarks from industrial and medical domains, significantly outperforming existing competitive methods. We will make the code and model of AdaptCLIP available at https://github.com/gaobb/AdaptCLIP.
System Prompt Optimization with Meta-Learning
Authors: Yumin Choi, Jinheon Baek, Sung Ju Hwang
Date: 2025-05-14 00:00:00
Tasks: Meta-Learning

Large Language Models (LLMs) have shown remarkable capabilities, with optimizing their input prompts playing a pivotal role in maximizing their performance. However, while LLM prompts consist of both the task-agnostic system prompts and task-specific user prompts, existing work on prompt optimization has focused on user prompts specific to individual queries or tasks, and largely overlooked the system prompt that is, once optimized, applicable across different tasks and domains. Motivated by this, we introduce the novel problem of bilevel system prompt optimization, whose objective is to design system prompts that are robust to diverse user prompts and transferable to unseen tasks. To tackle this problem, we then propose a meta-learning framework, which meta-learns the system prompt by optimizing it over various user prompts across multiple datasets, while simultaneously updating the user prompts in an iterative manner to ensure synergy between them. We conduct experiments on 14 unseen datasets spanning 5 different domains, on which we show that our approach produces system prompts that generalize effectively to diverse user prompts. Also, our findings reveal that the optimized system prompt enables rapid adaptation even to unseen tasks, requiring fewer optimization steps for test-time user prompts while achieving improved performance.
Defect Detection in Photolithographic Patterns Using Deep Learning Models Trained on Synthetic Data
Authors: Prashant P. Shinde, Priyadarshini P. Pai, Shashishekar P. Adiga, K. Subramanya Mayya, Yongbeom Seo, Myungsoo Hwang, Heeyoung Go, Changmin Park
Date: 2025-05-15 00:00:00
Tasks: Defect Detection, object-detection, Object Detection

In the photolithographic process vital to semiconductor manufacturing, various types of defects appear during EUV pattering. Due to ever-shrinking pattern size, these defects are extremely small and cause false or missed detection during inspection. Specifically, the lack of defect-annotated quality data with good representation of smaller defects has prohibited deployment of deep learning based defect detection models in fabrication lines. To resolve the problem of data unavailability, we artificially generate scanning electron microscopy (SEM) images of line patterns with known distribution of defects and autonomously annotate them. We then employ state-of-the-art object detection models to investigate defect detection performance as a function of defect size, much smaller than the pitch width. We find that the real-time object detector YOLOv8 has the best mean average precision of 96% as compared to EfficientNet, 83%, and SSD, 77%, with the ability to detect smaller defects. We report the smallest defect size that can be detected reliably. When tested on real SEM data, the YOLOv8 model correctly detected 84.6% of Bridge defects and 78.3% of Break defects across all relevant instances. These promising results suggest that synthetic data can be used as an alternative to real-world data in order to develop robust machine-learning models.
Spatial-Temporal Bipartite Graph Attention Network for Traffic Forecasting
Authors: Dimuthu Lakmal, Kushani Perera, Renata Borovica-Gajic, Shanika Karunasekera
Date: 2024-04-25 00:00:00
Proceeding: Pacific-Asia Conference on Knowledge Discovery and Data Mining 2024 4
Tasks: Decoder, Graph Attention

Accurate traffic forecasting is pivotal for an efficient data-driven transportation system. The intricate nature of spatial-temporal dependencies and non-linearity present in traffic data has posed a significant challenge to the modeling of accurate traffic forecasting systems. Lately, there has been a significant effort to develop complex Spatial-Temporal Graph Neural Networks (STGNN) that predominantly utilize various Graph Neural Networks (GNN) and attention-based encoder-decoder architectures due to their ability to capture non-linear dependencies in spatial and temporal domains effectively. However, conventional GNNs limit explicit propagation of past information among nodes, while attention-based models such as transformers do not support finer-grained attention score distribution. In this study, we address the aforementioned issues and introduce a novel STGNN namely, Spatio-Temporal Bipartite Graph Attention Network (STBGAT) that allows explicit modeling of past information propagation among nodes. Further, we present a heterogeneous cross-attention mechanism in a transformer to compute finer-grained feature-wise attention distribution enabling the model to capture richer and more expressive temporal dependencies. Our experiments reveal that the proposed architecture outperforms the state-of-the-art approaches proposed in recent literature.