pwc-archive/papers-with-abstracts huggingface dataset:
Revitalizing Reconstruction Models for Multi-class Anomaly Detection via Class-Aware Contrastive Learning
Authors: Lei Fan, JunJie Huang, Donglin Di, Anyang Su, Maurice Pagnucco, Yang song
Date: 2024-12-06 00:00:00
Tasks: Anomaly Detection, Contrastive Learning, Multi-class Anomaly Detection

For anomaly detection (AD), early approaches often train separate models for individual classes, yielding high performance but posing challenges in scalability and resource management. Recent efforts have shifted toward training a single model capable of handling multiple classes. However, directly extending early AD methods to multi-class settings often results in degraded performance. In this paper, we analyze this degradation observed in reconstruction-based methods, identifying two key issues: catastrophic forgetting and inter-class confusion. To this end, we propose a plug-and-play modification by incorporating class-aware contrastive learning (CL). By explicitly leveraging raw object category information (e.g., carpet or wood) as supervised signals, we apply local CL to fine-tune multiscale features and global CL to learn more compact feature representations of normal patterns, thereby effectively adapting the models to multi-class settings. Experiments across four datasets (over 60 categories) verify the effectiveness of our approach, yielding significant improvements and superior performance compared to advanced methods. Notably, ablation studies show that even using pseudo-class labels can achieve comparable performance.
Latent Space Characterization of Autoencoder Variants
Authors: Anika Shrivastava, Renu Rameshan, Samar Agnihotri
Date: 2024-12-06 00:00:00
Tasks: Denoising, Representation Learning

Understanding the latent spaces learned by deep learning models is crucial in exploring how they represent and generate complex data. Autoencoders (AEs) have played a key role in the area of representation learning, with numerous regularization techniques and training principles developed not only to enhance their ability to learn compact and robust representations, but also to reveal how different architectures influence the structure and smoothness of the lower-dimensional non-linear manifold. We strive to characterize the structure of the latent spaces learned by different autoencoders including convolutional autoencoders (CAEs), denoising autoencoders (DAEs), and variational autoencoders (VAEs) and how they change with the perturbations in the input. By characterizing the matrix manifolds corresponding to the latent spaces, we provide an explanation for the well-known observation that the latent spaces of CAE and DAE form non-smooth manifolds, while that of VAE forms a smooth manifold. We also map the points of the matrix manifold to a Hilbert space using distance preserving transforms and provide an alternate view in terms of the subspaces generated in the Hilbert space as a function of the distortion in the input. The results show that the latent manifolds of CAE and DAE are stratified with each stratum being a smooth product manifold, while the manifold of VAE is a smooth product manifold of two symmetric positive definite matrices and a symmetric positive semi-definite matrix.
Machine learning algorithms to predict the risk of rupture of intracranial aneurysms: a systematic review
Authors: Karan Daga, Siddharth Agarwal, Zaeem Moti, Matthew BK Lee, Munaib Din, David Wood, Marc Modat, Thomas C Booth
Date: 2024-12-06 00:00:00

Purpose: Subarachnoid haemorrhage is a potentially fatal consequence of intracranial aneurysm rupture, however, it is difficult to predict if aneurysms will rupture. Prophylactic treatment of an intracranial aneurysm also involves risk, hence identifying rupture-prone aneurysms is of substantial clinical importance. This systematic review aims to evaluate the performance of machine learning algorithms for predicting intracranial aneurysm rupture risk. Methods: MEDLINE, Embase, Cochrane Library and Web of Science were searched until December 2023. Studies incorporating any machine learning algorithm to predict the risk of rupture of an intracranial aneurysm were included. Risk of bias was assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). PROSPERO registration: CRD42023452509. Results: Out of 10,307 records screened, 20 studies met the eligibility criteria for this review incorporating a total of 20,286 aneurysm cases. The machine learning models gave a 0.66-0.90 range for performance accuracy. The models were compared to current clinical standards in six studies and gave mixed results. Most studies posed high or unclear risks of bias and concerns for applicability, limiting the inferences that can be drawn from them. There was insufficient homogenous data for a meta-analysis. Conclusions: Machine learning can be applied to predict the risk of rupture for intracranial aneurysms. However, the evidence does not comprehensively demonstrate superiority to existing practice, limiting its role as a clinical adjunct. Further prospective multicentre studies of recent machine learning tools are needed to prove clinical validation before they are implemented in the clinic.
Decomposed Distribution Matching in Dataset Condensation
Authors: Sahar Rahimi Malakshan, Mohammad Saeed Ebrahimi Saadabadi, Ali Dabouei, Nasser M. Nasrabadi
Date: 2024-12-06 00:00:00
Tasks: Continual Learning, Dataset Condensation, Diversity

Dataset Condensation (DC) aims to reduce deep neural networks training efforts by synthesizing a small dataset such that it will be as effective as the original large dataset. Conventionally, DC relies on a costly bi-level optimization which prohibits its practicality. Recent research formulates DC as a distribution matching problem which circumvents the costly bi-level optimization. However, this efficiency sacrifices the DC performance. To investigate this performance degradation, we decomposed the dataset distribution into content and style. Our observations indicate two major shortcomings of: 1) style discrepancy between original and condensed data, and 2) limited intra-class diversity of condensed dataset. We present a simple yet effective method to match the style information between original and condensed data, employing statistical moments of feature maps as well-established style indicators. Moreover, we enhance the intra-class diversity by maximizing the Kullback-Leibler divergence within each synthetic class, i.e., content. We demonstrate the efficacy of our method through experiments on diverse datasets of varying size and resolution, achieving improvements of up to 4.1% on CIFAR10, 4.2% on CIFAR100, 4.3% on TinyImageNet, 2.0% on ImageNet-1K, 3.3% on ImageWoof, 2.5% on ImageNette, and 5.5% in continual learning accuracy.
Fair Diagnosis: Leveraging Causal Modeling to Mitigate Medical Bias
Authors: Bowei Tian, Yexiao He, Meng Liu, Yucong Dai, Ziyao Wang, Shwai He, Guoheng Sun, Zheyu Shen, Wanghao Ye, Yongkai Wu, Ang Li
Date: 2024-12-06 00:00:00
Tasks: Diagnostic, Fairness, Medical Image Analysis

In medical image analysis, model predictions can be affected by sensitive attributes, such as race and gender, leading to fairness concerns and potential biases in diagnostic outcomes. To mitigate this, we present a causal modeling framework, which aims to reduce the impact of sensitive attributes on diagnostic predictions. Our approach introduces a novel fairness criterion, \textbf{Diagnosis Fairness}, and a unique fairness metric, leveraging path-specific fairness to control the influence of demographic attributes, ensuring that predictions are primarily informed by clinically relevant features rather than sensitive attributes. By incorporating adversarial perturbation masks, our framework directs the model to focus on critical image regions, suppressing bias-inducing information. Experimental results across multiple datasets demonstrate that our framework effectively reduces bias directly associated with sensitive attributes while preserving diagnostic accuracy. Our findings suggest that causal modeling can enhance both fairness and interpretability in AI-powered clinical decision support systems.
Espresso: High Compression For Rich Extraction From Videos for Your Vision-Language Model
Authors: Keunwoo Peter Yu, Achal Dave, Rares Ambrus, Jean Mercat
Date: 2024-12-06 00:00:00
Tasks: EgoSchema, Language Modeling, Language Modelling, Video Understanding

Recent advances in vision-language models (VLMs) have shown great promise in connecting images and text, but extending these models to long videos remains challenging due to the rapid growth in token counts. Models that compress videos by local aggregation in time or space have become popular for handling long-form inputs; however, these pooling-based projectors sacrifice the benefits of fixed-length representations that are crucial for streaming and efficient video understanding. We introduce $\texttt{Espresso}$, a new architecture that separately compresses spatial and temporal features into fixed-length sequences. $\texttt{Espresso}$ enables efficient video encoding while maintaining strong long-form reasoning capabilities. Experiments show that fixed-length compression combined with segment-wise processing offers a scalable and competitive alternative to pooling-based approaches. Our results demonstrate that fixed-length projectors, when properly designed and trained, remain a viable foundation for video-language modeling.
Learning to Translate Noise for Robust Image Denoising
Authors: Inju Ha, Donghun Ryou, Seonguk Seo, Bohyung Han
Date: 2024-12-06 00:00:00
Tasks: Denoising, Image Denoising, Translation

Deep learning-based image denoising techniques often struggle with poor generalization performance to out-of-distribution real-world noise. To tackle this challenge, we propose a novel noise translation framework that performs denoising on an image with translated noise rather than directly denoising an original noisy image. Specifically, our approach translates complex, unknown real-world noise into Gaussian noise, which is spatially uncorrelated and independent of image content, through a noise translation network. The translated noisy images are then processed by an image denoising network pretrained to effectively remove Gaussian noise, enabling robust and consistent denoising performance. We also design well-motivated loss functions and architectures for the noise translation network by leveraging the mathematical properties of Gaussian noise. Experimental results demonstrate that the proposed method substantially improves robustness and generalizability, outperforming state-of-the-art methods across diverse benchmarks. Visualized denoising results and the source code are available on our project page.
Mix-Modality Person Re-Identification: A New and Practical Paradigm
Authors: Wei Liu, Xin Xu, Hua Chang, Xin Yuan, Zheng Wang
Date: 2024-12-06 00:00:00
Tasks: Cross-Modality Person Re-identification, Person Re-Identification, Retrieval

Current visible-infrared cross-modality person re-identification research has only focused on exploring the bi-modality mutual retrieval paradigm, and we propose a new and more practical mix-modality retrieval paradigm. Existing Visible-Infrared person re-identification (VI-ReID) methods have achieved some results in the bi-modality mutual retrieval paradigm by learning the correspondence between visible and infrared modalities. However, significant performance degradation occurs due to the modality confusion problem when these methods are applied to the new mix-modality paradigm. Therefore, this paper proposes a Mix-Modality person re-identification (MM-ReID) task, explores the influence of modality mixing ratio on performance, and constructs mix-modality test sets for existing datasets according to the new mix-modality testing paradigm. To solve the modality confusion problem in MM-ReID, we propose a Cross-Identity Discrimination Harmonization Loss (CIDHL) adjusting the distribution of samples in the hyperspherical feature space, pulling the centers of samples with the same identity closer, and pushing away the centers of samples with different identities while aggregating samples with the same modality and the same identity. Furthermore, we propose a Modality Bridge Similarity Optimization Strategy (MBSOS) to optimize the cross-modality similarity between the query and queried samples with the help of the similar bridge sample in the gallery. Extensive experiments demonstrate that compared to the original performance of existing cross-modality methods on MM-ReID, the addition of our CIDHL and MBSOS demonstrates a general improvement.
Addressing Attribute Leakages in Diffusion-based Image Editing without Training
Authors: Sunung Mun, Jinhwan Nam, Sunghyun Cho, Jungseul Ok
Date: 2024-12-06 00:00:00
Tasks: Attribute, Object

Diffusion models have become a cornerstone in image editing, offering flexibility with language prompts and source images. However, a key challenge is attribute leakage, where unintended modifications occur in non-target regions or within target regions due to attribute interference. Existing methods often suffer from leakage due to naive text embeddings and inadequate handling of End-of-Sequence (EOS) token embeddings. To address this, we propose ALE-Edit (Attribute-leakage-free editing), a novel framework to minimize attribute leakage with three components: (1) Object-Restricted Embeddings (ORE) to localize object-specific attributes in text embeddings, (2) Region-Guided Blending for Cross-Attention Masking (RGB-CAM) to align attention with target regions, and (3) Background Blending (BB) to preserve non-edited regions. Additionally, we introduce ALE-Bench, a benchmark for evaluating attribute leakage with new metrics for target-external and target-internal leakage. Experiments demonstrate that our framework significantly reduces attribute leakage while maintaining high editing quality, providing an efficient and tuning-free solution for multi-object image editing.
Superpixel Tokenization for Vision Transformers: Preserving Semantic Integrity in Visual Tokens
Authors: Jaihyun Lew, Soohyuk Jang, Jaehoon Lee, Seungryong Yoo, Eunji Kim, Saehyung Lee, Jisoo Mok, Siwon Kim, Sungroh Yoon
Date: 2024-12-06 00:00:00
Tasks: Superpixels

Transformers, a groundbreaking architecture proposed for Natural Language Processing (NLP), have also achieved remarkable success in Computer Vision. A cornerstone of their success lies in the attention mechanism, which models relationships among tokens. While the tokenization process in NLP inherently ensures that a single token does not contain multiple semantics, the tokenization of Vision Transformer (ViT) utilizes tokens from uniformly partitioned square image patches, which may result in an arbitrary mixing of visual concepts in a token. In this work, we propose to substitute the grid-based tokenization in ViT with superpixel tokenization, which employs superpixels to generate a token that encapsulates a sole visual concept. Unfortunately, the diverse shapes, sizes, and locations of superpixels make integrating superpixels into ViT tokenization rather challenging. Our tokenization pipeline, comprised of pre-aggregate extraction and superpixel-aware aggregation, overcomes the challenges that arise in superpixel tokenization. Extensive experiments demonstrate that our approach, which exhibits strong compatibility with existing frameworks, enhances the accuracy and robustness of ViT on various downstream tasks.
Unsupervised Segmentation by Diffusing, Walking and Cutting
Authors: Daniela Ivanova, Marco Aversa, Paul Henderson, John Williamson
Date: 2024-12-06 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation, Unsupervised Image Segmentation

We propose an unsupervised image segmentation method using features from pre-trained text-to-image diffusion models. Inspired by classic spectral clustering approaches, we construct adjacency matrices from self-attention layers between image patches and recursively partition using Normalised Cuts. A key insight is that self-attention probability distributions, which capture semantic relations between patches, can be interpreted as a transition matrix for random walks across the image. We leverage this by first using Random Walk Normalized Cuts directly on these self-attention activations to partition the image, minimizing transition probabilities between clusters while maximizing coherence within clusters. Applied recursively, this yields a hierarchical segmentation that reflects the rich semantics in the pre-trained attention layers, without any additional training. Next, we explore other ways to build the NCuts adjacency matrix from features, and how we can use the random walk interpretation of self-attention to capture long-range relationships. Finally, we propose an approach to automatically determine the NCut cost criterion, avoiding the need to tune this manually. We quantitatively analyse the effect incorporating different features, a constant versus dynamic NCut threshold, and incorporating multi-node paths when constructing the NCuts adjacency matrix. We show that our approach surpasses all existing methods for zero-shot unsupervised segmentation, achieving state-of-the-art results on COCO-Stuff-27 and Cityscapes.
LAA-Net: A Physical-prior-knowledge Based Network for Robust Nighttime Depth Estimation
Authors: KeBin Peng, Haotang Li, Zhenyu Qi, Huashan Chen, Zi Wang, Wei zhang, Sen He
Date: 2024-12-05 00:00:00
Tasks: Depth Estimation, Monocular Depth Estimation

Existing self-supervised monocular depth estimation (MDE) models attempt to improve nighttime performance by using GANs to transfer nighttime images into their daytime versions. However, this can introduce inconsistencies due to the complexities of real-world daytime lighting variations, which may finally lead to inaccurate estimation results. To address this issue, we leverage physical-prior-knowledge about light wavelength and light attenuation during nighttime. Specifically, our model, Light-Attenuation-Aware Network (LAA-Net), incorporates physical insights from Rayleigh scattering theory for robust nighttime depth estimation: LAA-Net is trained based on red channel values because red light preserves more information under nighttime scenarios due to its longer wavelength. Additionally, based on Beer-Lambert law, we introduce Red Channel Attenuation (RCA) loss to guide LAA-Net's training. Experiments on the RobotCar-Night, nuScenes-Night, RobotCar-Day, and KITTI datasets demonstrate that our model outperforms SOTA models.
ProPLIKS: Probablistic 3D human body pose estimation
Authors: Karthik Shetty, Annette Birkhold, Bernhard Egger, Srikrishna Jaganathan, Norbert Strobel, Markus Kowarschik, Andreas Maier
Date: 2024-12-05 00:00:00
Tasks: 3D Human Pose Estimation, Pose Estimation

We present a novel approach for 3D human pose estimation by employing probabilistic modeling. This approach leverages the advantages of normalizing flows in non-Euclidean geometries to address uncertain poses. Specifically, our method employs normalizing flow tailored to the SO(3) rotational group, incorporating a coupling mechanism based on the M\"obius transformation. This enables the framework to accurately represent any distribution on SO(3), effectively addressing issues related to discontinuities. Additionally, we reinterpret the challenge of reconstructing 3D human figures from 2D pixel-aligned inputs as the task of mapping these inputs to a range of probable poses. This perspective acknowledges the intrinsic ambiguity of the task and facilitates a straightforward integration method for multi-view scenarios. The combination of these strategies showcases the effectiveness of probabilistic models in complex scenarios for human pose estimation techniques. Our approach notably surpasses existing methods in the field of pose estimation. We also validate our methodology on human pose estimation from RGB images as well as medical X-Ray datasets.
Motion-Guided Deep Image Prior for Cardiac MRI
Authors: Marc Vornehm, Chong Chen, Muhammad Ahmad Sultan, Syed Murtaza Arshad, Yuchi Han, Florian Knoll, Rizwan Ahmad
Date: 2024-12-05 00:00:00
Tasks: Diagnostic

Cardiovascular magnetic resonance imaging is a powerful diagnostic tool for assessing cardiac structure and function. Traditional breath-held imaging protocols, however, pose challenges for patients with arrhythmias or limited breath-holding capacity. We introduce Motion-Guided Deep Image prior (M-DIP), a novel unsupervised reconstruction framework for accelerated real-time cardiac MRI. M-DIP employs a spatial dictionary to synthesize a time-dependent template image, which is further refined using time-dependent deformation fields that model cardiac and respiratory motion. Unlike prior DIP-based methods, M-DIP simultaneously captures physiological motion and frame-to-frame content variations, making it applicable to a wide range of dynamic applications. We validate M-DIP using simulated MRXCAT cine phantom data as well as free-breathing real-time cine and single-shot late gadolinium enhancement data from clinical patients. Comparative analyses against state-of-the-art supervised and unsupervised approaches demonstrate M-DIP's performance and versatility. M-DIP achieved better image quality metrics on phantom data, as well as higher reader scores for in-vivo patient data.
Using Diffusion Priors for Video Amodal Segmentation
Authors: Kaihua Chen, Deva Ramanan, Tarasha Khurana
Date: 2024-12-05 00:00:00
Proceeding: CVPR 2025 1
Tasks: Image Segmentation, Object, Segmentation, Semantic Segmentation

Object permanence in humans is a fundamental cue that helps in understanding persistence of objects, even when they are fully occluded in the scene. Present day methods in object segmentation do not account for this amodal nature of the world, and only work for segmentation of visible or modal objects. Few amodal methods exist; single-image segmentation methods cannot handle high-levels of occlusions which are better inferred using temporal information, and multi-frame methods have focused solely on segmenting rigid objects. To this end, we propose to tackle video amodal segmentation by formulating it as a conditional generation task, capitalizing on the foundational knowledge in video generative models. Our method is simple; we repurpose these models to condition on a sequence of modal mask frames of an object along with contextual pseudo-depth maps, to learn which object boundary may be occluded and therefore, extended to hallucinate the complete extent of an object. This is followed by a content completion stage which is able to inpaint the occluded regions of an object. We benchmark our approach alongside a wide array of state-of-the-art methods on four datasets and show a dramatic improvement of upto 13% for amodal segmentation in an object's occluded region.