pwc-archive/papers-with-abstracts huggingface dataset:
Exploring Intrinsic Normal Prototypes within a Single Image for Universal Anomaly Detection
Authors: Wei Luo, Yunkang Cao, Haiming Yao, Xiaotian Zhang, Jianan Lou, Yuqi Cheng, Weiming Shen, Wenyong Yu
Date: 2025-03-04 00:00:00
Proceeding: CVPR 2025 1
Tasks: Anomaly Detection, Multi-class Anomaly Detection

Anomaly detection (AD) is essential for industrial inspection, yet existing methods typically rely on ``comparing'' test images to normal references from a training set. However, variations in appearance and positioning often complicate the alignment of these references with the test image, limiting detection accuracy. We observe that most anomalies manifest as local variations, meaning that even within anomalous images, valuable normal information remains. We argue that this information is useful and may be more aligned with the anomalies since both the anomalies and the normal information originate from the same image. Therefore, rather than relying on external normality from the training set, we propose INP-Former, a novel method that extracts Intrinsic Normal Prototypes (INPs) directly from the test image. Specifically, we introduce the INP Extractor, which linearly combines normal tokens to represent INPs. We further propose an INP Coherence Loss to ensure INPs can faithfully represent normality for the testing image. These INPs then guide the INP-Guided Decoder to reconstruct only normal tokens, with reconstruction errors serving as anomaly scores. Additionally, we propose a Soft Mining Loss to prioritize hard-to-optimize samples during training. INP-Former achieves state-of-the-art performance in single-class, multi-class, and few-shot AD tasks across MVTec-AD, VisA, and Real-IAD, positioning it as a versatile and universal solution for AD. Remarkably, INP-Former also demonstrates some zero-shot AD capability. Code is available at:https://github.com/luow23/INP-Former.
Adversarial Tokenization
Authors: Renato Lui Geh, Zilei Shao, Guy Van Den Broeck
Date: 2025-03-04 00:00:00
Tasks: valid

Current LLM pipelines account for only one possible tokenization for a given string, ignoring exponentially many alternative tokenizations during training and inference. For example, the standard Llama3 tokenization of penguin is [p,enguin], yet [peng,uin] is another perfectly valid alternative. In this paper, we show that despite LLMs being trained solely on one tokenization, they still retain semantic understanding of other tokenizations, raising questions about their implications in LLM safety. Put succinctly, we answer the following question: can we adversarially tokenize an obviously malicious string to evade safety and alignment restrictions? We show that not only is adversarial tokenization an effective yet previously neglected axis of attack, but it is also competitive against existing state-of-the-art adversarial approaches without changing the text of the harmful request. We empirically validate this exploit across three state-of-the-art LLMs and adversarial datasets, revealing a previously unknown vulnerability in subword models.
Output Length Effect on DeepSeek-R1's Safety in Forced Thinking
Authors: Xuying Li, Zhuo Li, Yuji Kosuga, Victor Bian
Date: 2025-03-02 00:00:00

Large Language Models (LLMs) have demonstrated strong reasoning capabilities, but their safety under adversarial conditions remains a challenge. This study examines the impact of output length on the robustness of DeepSeek-R1, particularly in Forced Thinking scenarios. We analyze responses across various adversarial prompts and find that while longer outputs can improve safety through self-correction, certain attack types exploit extended generations. Our findings suggest that output length should be dynamically controlled to balance reasoning effectiveness and security. We propose reinforcement learning-based policy adjustments and adaptive token length regulation to enhance LLM safety.
Language Models can Self-Improve at State-Value Estimation for Better Search
Authors: Ethan Mendes, Alan Ritter
Date: 2025-03-04 00:00:00
Tasks: Language Modeling, Language Modelling

Collecting ground truth task completion rewards or human demonstrations for multi-step reasoning tasks is often cost-prohibitive and time-consuming, especially in interactive domains like web tasks. To address this bottleneck, we present self-taught lookahead, a self-supervised method that leverages state-transition dynamics to train a value model capable of effectively guiding language model-controlled search. We find that moderately sized (8 billion parameters) open-weight value models improved with self-taught lookahead can match the performance of using a frontier LLM such as gpt-4o as the value model. Furthermore, we find that self-taught lookahead improves performance by 20% while reducing costs 37x compared to previous LLM-based tree search, without relying on ground truth rewards.
The First Few Tokens Are All You Need: An Efficient and Effective Unsupervised Prefix Fine-Tuning Method for Reasoning Models
Authors: Ke Ji, Jiahao Xu, Tian Liang, Qiuzhi Liu, Zhiwei He, Xingyu Chen, Xiaoyuan Liu, Zhijie Wang, Junying Chen, Benyou Wang, Zhaopeng Tu, Haitao Mi, Dong Yu
Date: 2025-03-04 00:00:00

Improving the reasoning capabilities of large language models (LLMs) typically requires supervised fine-tuning with labeled data or computationally expensive sampling. We introduce Unsupervised Prefix Fine-Tuning (UPFT), which leverages the observation of Prefix Self-Consistency -- the shared initial reasoning steps across diverse solution trajectories -- to enhance LLM reasoning efficiency. By training exclusively on the initial prefix substrings (as few as 8 tokens), UPFT removes the need for labeled data or exhaustive sampling. Experiments on reasoning benchmarks show that UPFT matches the performance of supervised methods such as Rejection Sampling Fine-Tuning, while reducing training time by 75% and sampling cost by 99%. Further analysis reveals that errors tend to appear in later stages of the reasoning process and that prefix-based training preserves the model's structural knowledge. This work demonstrates how minimal unsupervised fine-tuning can unlock substantial reasoning gains in LLMs, offering a scalable and resource-efficient alternative to conventional approaches.
Multimodal Deep Learning for Subtype Classification in Breast Cancer Using Histopathological Images and Gene Expression Data
Authors: Amin Honarmandi Shandiz
Date: 2025-03-04 00:00:00
Tasks: Classification, Decision Making, Multimodal Deep Learning, Prognosis

Molecular subtyping of breast cancer is crucial for personalized treatment and prognosis. Traditional classification approaches rely on either histopathological images or gene expression profiling, limiting their predictive power. In this study, we propose a deep multimodal learning framework that integrates histopathological images and gene expression data to classify breast cancer into BRCA.Luminal and BRCA.Basal / Her2 subtypes. Our approach employs a ResNet-50 model for image feature extraction and fully connected layers for gene expression processing, with a cross-attention fusion mechanism to enhance modality interaction. We conduct extensive experiments using five-fold cross-validation, demonstrating that our multimodal integration outperforms unimodal approaches in terms of classification accuracy, precision-recall AUC, and F1-score. Our findings highlight the potential of deep learning for robust and interpretable breast cancer subtype classification, paving the way for improved clinical decision-making.
Beyond Cosine Decay: On the effectiveness of Infinite Learning Rate Schedule for Continual Pre-training
Authors: Vaibhav Singh, Paul Janson, Paria Mehrbod, Adam Ibrahim, Irina Rish, Eugene Belilovsky, Benjamin Thérien
Date: 2025-03-04 00:00:00
Tasks: Self-Supervised Learning

The ever-growing availability of unlabeled data presents both opportunities and challenges for training artificial intelligence systems. While self-supervised learning (SSL) has emerged as a powerful paradigm for extracting meaningful representations from vast amounts of unlabeled data, existing methods still struggle to adapt to the non-stationary, non-IID nature of real-world data streams without forgetting previously learned knowledge. Recent works have adopted a repeated cosine annealing schedule for large-scale continual pre-training; however, these schedules (1) inherently cause forgetting during the re-warming phase and (2) have not been systematically compared to existing continual SSL methods. In this work, we systematically compare the widely used cosine schedule with the recently proposed infinite learning rate schedule and empirically find the latter to be a more effective alternative. Our extensive empirical evaluation across diverse image and language datasets demonstrates that the infinite learning rate schedule consistently enhances continual pre-training performance compared to a repeated cosine decay without being restricted to a fixed iteration budget. For instance, in a small-scale MAE pre-training setup, it outperforms several strong baselines from the literature. We then scale up our experiments to larger MAE pre-training and autoregressive language model pre-training. Our results show that the infinite learning rate schedule remains effective at scale, surpassing repeated cosine decay for both MAE pre-training and zero-shot LM benchmarks.
Boltzmann Attention Sampling for Image Analysis with Small Objects
Authors: Theodore Zhao, Sid Kiblawi, Naoto Usuyama, Ho Hin Lee, Sam Preston, Hoifung Poon, Mu Wei
Date: 2025-03-04 00:00:00
Proceeding: CVPR 2025 1

Detecting and segmenting small objects, such as lung nodules and tumor lesions, remains a critical challenge in image analysis. These objects often occupy less than 0.1% of an image, making traditional transformer architectures inefficient and prone to performance degradation due to redundant attention computations on irrelevant regions. Existing sparse attention mechanisms rely on rigid hierarchical structures, which are poorly suited for detecting small, variable, and uncertain object locations. In this paper, we propose BoltzFormer, a novel transformer-based architecture designed to address these challenges through dynamic sparse attention. BoltzFormer identifies and focuses attention on relevant areas by modeling uncertainty using a Boltzmann distribution with an annealing schedule. Initially, a higher temperature allows broader area sampling in early layers, when object location uncertainty is greatest. As the temperature decreases in later layers, attention becomes more focused, enhancing efficiency and accuracy. BoltzFormer seamlessly integrates into existing transformer architectures via a modular Boltzmann attention sampling mechanism. Comprehensive evaluations on benchmark datasets demonstrate that BoltzFormer significantly improves segmentation performance for small objects while reducing attention computation by an order of magnitude compared to previous state-of-the-art methods.
Developing a PET/CT Foundation Model for Cross-Modal Anatomical and Functional Imaging
Authors: Yujin Oh, Robert Seifert, Yihan Cao, Christoph Clement, Justin Ferdinandus, Constantin Lapa, Alessandro Liebich, Michelle Amon, Johanna Enke, Sifan Song, Runqi Meng, Fang Zeng, Ning Guo, Xiang Li, Pedram Heidari, Axel Rominger, Kuangyu Shi, Quanzheng Li
Date: 2025-03-04 00:00:00
Tasks: Representation Learning

In oncology, Positron Emission Tomography-Computed Tomography (PET/CT) is widely used in cancer diagnosis, staging, and treatment monitoring, as it combines anatomical details from CT with functional metabolic activity and molecular marker expression information from PET. However, existing artificial intelligence-driven PET/CT analyses rely predominantly on task-specific models trained from scratch or on limited datasets, limiting their generalizability and robustness. To address this, we propose a foundation model approach specifically designed for multimodal PET/CT imaging. We introduce the Cross-Fraternal Twin Masked Autoencoder (FratMAE), a novel framework that effectively integrates whole-body anatomical and functional or molecular information. FratMAE employs separate Vision Transformer (ViT) encoders for PET and CT scans, along with cross-attention decoders that enable synergistic interactions between modalities during masked autoencoder training. Additionally, it incorporates textual metadata to enhance PET representation learning. By pre-training on PET/CT datasets, FratMAE captures intricate cross-modal relationships and global uptake patterns, achieving superior performance on downstream tasks and demonstrating its potential as a generalizable foundation model.
Feynman-Kac Correctors in Diffusion: Annealing, Guidance, and Product of Experts
Authors: Marta Skreta, Tara Akhound-Sadegh, Viktor Ohanesian, Roberto Bondesan, Alán Aspuru-Guzik, Arnaud Doucet, Rob Brekelmans, Alexander Tong, Kirill Neklyudov
Date: 2025-03-04 00:00:00
Tasks: Image Generation, Text to Image Generation, Text-to-Image Generation

While score-based generative models are the model of choice across diverse domains, there are limited tools available for controlling inference-time behavior in a principled manner, e.g. for composing multiple pretrained models. Existing classifier-free guidance methods use a simple heuristic to mix conditional and unconditional scores to approximately sample from conditional distributions. However, such methods do not approximate the intermediate distributions, necessitating additional 'corrector' steps. In this work, we provide an efficient and principled method for sampling from a sequence of annealed, geometric-averaged, or product distributions derived from pretrained score-based models. We derive a weighted simulation scheme which we call Feynman-Kac Correctors (FKCs) based on the celebrated Feynman-Kac formula by carefully accounting for terms in the appropriate partial differential equations (PDEs). To simulate these PDEs, we propose Sequential Monte Carlo (SMC) resampling algorithms that leverage inference-time scaling to improve sampling quality. We empirically demonstrate the utility of our methods by proposing amortized sampling via inference-time temperature annealing, improving multi-objective molecule generation using pretrained models, and improving classifier-free guidance for text-to-image generation. Our code is available at https://github.com/martaskrt/fkc-diffusion.
A Causal Framework for Aligning Image Quality Metrics and Deep Neural Network Robustness
Authors: Nathan Drenkow, Mathias Unberath
Date: 2025-03-04 00:00:00
Tasks: Image Quality Assessment

Image quality plays an important role in the performance of deep neural networks (DNNs) and DNNs have been widely shown to exhibit sensitivity to changes in imaging conditions. Large-scale datasets often contain images under a wide range of conditions prompting a need to quantify and understand their underlying quality distribution in order to better characterize DNN performance and robustness. Aligning the sensitivities of image quality metrics and DNNs ensures that estimates of quality can act as proxies for image/dataset difficulty independent of the task models trained/evaluated on the data. Conventional image quality assessment (IQA) seeks to measure and align quality relative to human perceptual judgments, but here we seek a quality measure that is not only sensitive to imaging conditions but also well-aligned with DNN sensitivities. We first ask whether conventional IQA metrics are also informative of DNN performance. In order to answer this question, we reframe IQA from a causal perspective and examine conditions under which quality metrics are predictive of DNN performance. We show theoretically and empirically that current IQA metrics are weak predictors of DNN performance in the context of classification. We then use our causal framework to provide an alternative formulation and a new image quality metric that is more strongly correlated with DNN performance and can act as a prior on performance without training new task models. Our approach provides a means to directly estimate the quality distribution of large-scale image datasets towards characterizing the relationship between dataset composition and DNN performance.
Calibrating LLM Confidence with Semantic Steering: A Multi-Prompt Aggregation Framework
Authors: Ziang Zhou, Tianyuan Jin, Jieming Shi, Qing Li
Date: 2025-03-04 00:00:00

Large Language Models (LLMs) often exhibit misaligned confidence scores, usually overestimating the reliability of their predictions. While verbalized confidence in Large Language Models (LLMs) has gained attention, prior work remains divided on whether confidence scores can be systematically steered through prompting. Recent studies even argue that such prompt-induced confidence shifts are negligible, suggesting LLMs' confidence calibration is rigid to linguistic interventions. Contrary to these claims, we first rigorously confirm the existence of directional confidence shifts by probing three models (including GPT3.5, LLAMA3-70b, GPT4) across 7 benchmarks, demonstrating that explicit instructions can inflate or deflate confidence scores in a regulated manner. Based on this observation, we propose a novel framework containing three components: confidence steering, steered confidence aggregation and steered answers selection, named SteeringConf. Our method, SteeringConf, leverages a confidence manipulation mechanism to steer the confidence scores of LLMs in several desired directions, followed by a summarization module that aggregates the steered confidence scores to produce a final prediction. We evaluate our method on 7 benchmarks and it consistently outperforms the baselines in terms of calibration metrics in task of confidence calibration and failure detection.
In-Depth Analysis of Automated Acne Disease Recognition and Classification
Authors: Afsana Ahsan Jeny, Masum Shah Junayed, Md Robel Mia, Md Baharul Islam
Date: 2025-03-04 00:00:00
Tasks: Clustering

Facial acne is a common disease, especially among adolescents, negatively affecting both physically and psychologically. Classifying acne is vital to providing the appropriate treatment. Traditional visual inspection or expert scanning is time-consuming and difficult to differentiate acne types. This paper introduces an automated expert system for acne recognition and classification. The proposed method employs a machine learning-based technique to classify and evaluate six types of acne diseases to facilitate the diagnosis of dermatologists. The pre-processing phase includes contrast improvement, smoothing filter, and RGB to L*a*b color conversion to eliminate noise and improve the classification accuracy. Then, a clustering-based segmentation method, k-means clustering, is applied for segmenting the disease-affected regions that pass through the feature extraction step. Characteristics of these disease-affected regions are extracted based on a combination of gray-level co-occurrence matrix (GLCM) and Statistical features. Finally, five different machine learning classifiers are employed to classify acne diseases. Experimental results show that the Random Forest (RF) achieves the highest accuracy of 98.50%, which is promising compared to the state-of-the-art methods.
Catheter Detection and Segmentation in X-ray Images via Multi-task Learning
Authors: Lin Xi, YingLiang Ma, Ethan Koland, Sandra Howell, Aldo Rinaldi, Kawal S. Rhode
Date: 2025-03-04 00:00:00
Tasks: Multi-Task Learning, Segmentation

Automated detection and segmentation of surgical devices, such as catheters or wires, in X-ray fluoroscopic images have the potential to enhance image guidance in minimally invasive heart surgeries. In this paper, we present a convolutional neural network model that integrates a resnet architecture with multiple prediction heads to achieve real-time, accurate localization of electrodes on catheters and catheter segmentation in an end-to-end deep learning framework. We also propose a multi-task learning strategy in which our model is trained to perform both accurate electrode detection and catheter segmentation simultaneously. A key challenge with this approach is achieving optimal performance for both tasks. To address this, we introduce a novel multi-level dynamic resource prioritization method. This method dynamically adjusts sample and task weights during training to effectively prioritize more challenging tasks, where task difficulty is inversely proportional to performance and evolves throughout the training process. Experiments on both public and private datasets have demonstrated that the accuracy of our method surpasses the existing state-of-the-art methods in both single segmentation task and in the detection and segmentation multi-task. Our approach achieves a good trade-off between accuracy and efficiency, making it well-suited for real-time surgical guidance applications.
10K is Enough: An Ultra-Lightweight Binarized Network for Infrared Small-Target Detection
Authors: Biqiao Xin, Qianchen Mao, Bingshu Wang, Jiangbin Zheng, Yong Zhao, C. L. Philip Chen
Date: 2025-03-04 00:00:00
Tasks: Binarization, Model Compression

The widespread deployment of InfRared Small-Target Detection(IRSTD) algorithms on edge devices necessitates the exploration of model compression techniques. Binary neural networks (BNNs) are distinguished by their exceptional efficiency in model compression. However, the small size of infrared targets introduces stringent precision requirements for the IRSTD task, while the inherent precision loss during binarization presents a significant challenge. To address this, we propose the Binarized Infrared Small-Target Detection Network (BiisNet), which preserves the core operations of binarized convolutions while integrating full-precision features into the network's information flow. Specifically, we propose the Dot-Binary Convolution, which retains fine-grained semantic information in feature maps while still leveraging the binarized convolution operations. In addition, we introduce a smooth and adaptive Dynamic Softsign function, which provides more comprehensive and progressively finer gradient during back-propagation, enhancing model stability and promoting an optimal weight distribution.Experimental results demonstrate that BiisNet not only significantly outperforms other binary architectures but also demonstrates strong competitiveness among state-of-the-art full-precision models.
The Effectiveness of Large Language Models in Transforming Unstructured Text to Standardized Formats
Authors: William Brach, Kristián Košťál, Michal Ries
Date: 2025-03-04 00:00:00
Tasks: Information Retrieval

The exponential growth of unstructured text data presents a fundamental challenge in modern data management and information retrieval. While Large Language Models (LLMs) have shown remarkable capabilities in natural language processing, their potential to transform unstructured text into standardized, structured formats remains largely unexplored - a capability that could revolutionize data processing workflows across industries. This study breaks new ground by systematically evaluating LLMs' ability to convert unstructured recipe text into the structured Cooklang format. Through comprehensive testing of four models (GPT-4o, GPT-4o-mini, Llama3.1:70b, and Llama3.1:8b), an innovative evaluation approach is introduced that combines traditional metrics (WER, ROUGE-L, TER) with specialized metrics for semantic element identification. Our experiments reveal that GPT-4o with few-shot prompting achieves breakthrough performance (ROUGE-L: 0.9722, WER: 0.0730), demonstrating for the first time that LLMs can reliably transform domain-specific unstructured text into structured formats without extensive training. Although model performance generally scales with size, we uncover surprising potential in smaller models like Llama3.1:8b for optimization through targeted fine-tuning. These findings open new possibilities for automated structured data generation across various domains, from medical records to technical documentation, potentially transforming the way organizations process and utilize unstructured information.