pwc-archive/papers-with-abstracts huggingface dataset:
From Images to Point Clouds: An Efficient Solution for Cross-media Blind Quality Assessment without Annotated Training
Authors: Yipeng Liu, Qi Yang, Yujie Zhang, Yiling Xu, Le Yang, Zhu Li
Date: 2025-01-23 00:00:00
Tasks: Disentanglement, Domain Adaptation, Point Cloud Quality Assessment

We present a novel quality assessment method which can predict the perceptual quality of point clouds from new scenes without available annotations by leveraging the rich prior knowledge in images, called the Distribution-Weighted Image-Transferred Point Cloud Quality Assessment (DWIT-PCQA). Recognizing the human visual system (HVS) as the decision-maker in quality assessment regardless of media types, we can emulate the evaluation criteria for human perception via neural networks and further transfer the capability of quality prediction from images to point clouds by leveraging the prior knowledge in the images. Specifically, domain adaptation (DA) can be leveraged to bridge the images and point clouds by aligning feature distributions of the two media in the same feature space. However, the different manifestations of distortions in images and point clouds make feature alignment a difficult task. To reduce the alignment difficulty and consider the different distortion distribution during alignment, we have derived formulas to decompose the optimization objective of the conventional DA into two suboptimization functions with distortion as a transition. Specifically, through network implementation, we propose the distortion-guided biased feature alignment which integrates existing/estimated distortion distribution into the adversarial DA framework, emphasizing common distortion patterns during feature alignment. Besides, we propose the quality-aware feature disentanglement to mitigate the destruction of the mapping from features to quality during alignment with biased distortions. Experimental results demonstrate that our proposed method exhibits reliable performance compared to general blind PCQA methods without needing point cloud annotations.
Scalable Evaluation Framework for Foundation Models in Musculoskeletal MRI Bridging Computational Innovation with Clinical Utility
Authors: Gabrielle Hoyer, Michelle W Tong, Rupsa Bhattacharjee, Valentina Pedoia, Sharmila Majumdar
Date: 2025-01-23 00:00:00

Foundation models hold transformative potential for medical imaging, but their clinical utility requires rigorous evaluation to address their strengths and limitations. This study introduces an evaluation framework for assessing the clinical impact and translatability of SAM, MedSAM, and SAM2, using musculoskeletal MRI as a case study. We tested these models across zero-shot and finetuned paradigms to assess their ability to process diverse anatomical structures and effectuate clinically reliable biomarkers, including cartilage thickness, muscle volume, and disc height. We engineered a modular pipeline emphasizing scalability, clinical relevance, and workflow integration, reducing manual effort and aligning validation with end-user expectations. Hierarchical modeling revealed how dataset mixing, anatomical complexity, and MRI acquisition parameters influence performance, providing insights into the role of imaging refinements in improving segmentation accuracy. This work demonstrates how clinically focused evaluations can connect computational advancements with tangible applications, creating a pathway for foundation models to address medical challenges. By emphasizing interdisciplinary collaboration and aligning technical innovation with clinical priorities, our framework provides a roadmap for advancing machine learning technologies into scalable and impactful biomedical solutions.
Unraveling Normal Anatomy via Fluid-Driven Anomaly Randomization
Authors: Peirong Liu, Ana Lawry Aguila, Juan E. Iglesias
Date: 2025-01-23 00:00:00
Proceeding: CVPR 2025 1
Tasks: Anatomy, Anomaly Detection, Medical Image Analysis

Data-driven machine learning has made significant strides in medical image analysis. However, most existing methods are tailored to specific modalities and assume a particular resolution (often isotropic). This limits their generalizability in clinical settings, where variations in scan appearance arise from differences in sequence parameters, resolution, and orientation. Furthermore, most general-purpose models are designed for healthy subjects and suffer from performance degradation when pathology is present. We introduce UNA (Unraveling Normal Anatomy), the first modality-agnostic learning approach for normal brain anatomy reconstruction that can handle both healthy scans and cases with pathology. We propose a fluid-driven anomaly randomization method that generates an unlimited number of realistic pathology profiles on-the-fly. UNA is trained on a combination of synthetic and real data, and can be applied directly to real images with potential pathology without the need for fine-tuning. We demonstrate UNA's effectiveness in reconstructing healthy brain anatomy and showcase its direct application to anomaly detection, using both simulated and real images from 3D healthy and stroke datasets, including CT and MRI scans. By bridging the gap between healthy and diseased images, UNA enables the use of general-purpose models on diseased images, opening up new opportunities for large-scale analysis of uncurated clinical images in the presence of pathology. Code is available at https://github.com/peirong26/UNA.
Meta-Feature Adapter: Integrating Environmental Metadata for Enhanced Animal Re-identification
Authors: Yuzhuo Li, Di Zhao, Yihao Wu, Yun Sing Koh
Date: 2025-01-23 00:00:00

Identifying individual animals within large wildlife populations is essential for effective wildlife monitoring and conservation efforts. Recent advancements in computer vision have shown promise in animal re-identification (Animal ReID) by leveraging data from camera traps. However, existing methods rely exclusively on visual data, neglecting environmental metadata that ecologists have identified as highly correlated with animal behavior and identity, such as temperature and circadian rhythms. To bridge this gap, we propose the Meta-Feature Adapter (MFA), a lightweight module designed to integrate environmental metadata into vision-language foundation models, such as CLIP, to enhance Animal ReID performance. Our approach translates environmental metadata into natural language descriptions, encodes them into metadata-aware text embeddings, and incorporates these embeddings into image features through a cross-attention mechanism. Furthermore, we introduce a Gated Cross-Attention mechanism that dynamically adjusts the weights of metadata contributions, further improving performance. To validate our approach, we constructed the Metadata Augmented Animal Re-identification (MAAR) dataset, encompassing six species from New Zealand and featuring paired image data and environmental metadata. Extensive experiments demonstrate that MFA consistently improves Animal ReID performance across multiple baseline models.
A light-weight model to generate NDWI from Sentinel-1
Authors: Saleh Sakib Ahmed, Saifur Rahman Jony, Md. Toufikuzzaman, Saifullah Sayed, Rashed Uz Zzaman, Sara Nowreen, M. Sohel Rahman
Date: 2025-01-23 00:00:00

The use of Sentinel-2 images to compute Normalized Difference Water Index (NDWI) has many applications, including water body area detection. However, cloud cover poses significant challenges in this regard, which hampers the effectiveness of Sentinel-2 images in this context. In this paper, we present a deep learning model that can generate NDWI given Sentinel-1 images, thereby overcoming this cloud barrier. We show the effectiveness of our model, where it demonstrates a high accuracy of 0.9134 and an AUC of 0.8656 to predict the NDWI. Additionally, we observe promising results with an R2 score of 0.4984 (for regressing the NDWI values) and a Mean IoU of 0.4139 (for the underlying segmentation task). In conclusion, our model offers a first and robust solution for generating NDWI images directly from Sentinel-1 images and subsequent use for various applications even under challenging conditions such as cloud cover and nighttime.
NUDT4MSTAR: A Large Dataset and Benchmark Towards Remote Sensing Object Recognition in the Wild
Authors: Yongxiang Liu, Weijie Li, Li Liu, Jie zhou, Xuying Xiong, Bowen Peng, Yafei Song, Wei Yang, Tianpeng Liu, Zhen Liu, Xiang Li
Date: 2025-01-23 00:00:00
Tasks: Earth Observation, Object Recognition, Transfer Learning

As an indispensable sensor for Remote sensing, Synthetic Aperture Radar (SAR) has a unique capability for all-day imaging. Nevertheless, in a data-driven era, the scarcity of large-scale datasets poses a significant bottleneck to advancing SAR automatic target recognition (ATR) technology. This paper introduces NUDT4MSTAR, a large-scale SAR dataset for remote sensing target recognition in the wild, including 40 vehicle target types and various imaging conditions across 5 realistic scenes. NUDT4MSTAR represents a significant leap forward in dataset scale, containing over 190,000 images-tenfold the size of its predecessors. We meticulously annotate each image with detailed target information and imaging conditions. Besides, data in both processed magnitude images and original complex formats are provided. Then, we construct a comprehensive benchmark consisting of 7 experiments with 15 recognition methods focusing on the stable and effective ATR issues. Besides, we conduct transfer learning experiments utilizing various models training on NUDT4MSTAR and apply them to three other target datasets, demonstrating its substantial potential for the broader field of ground objects ATR. Finally, we discuss this dataset's application value and ATR's significant challenges. To the best of our knowledge, this work marks the first-ever endeavor to create a large-scale dataset benchmark for fine-grained SAR recognition in the wild, featuring an extensive collection of exhaustively annotated vehicle images. We expect that the open source of NUDT4MSTAR will facilitate the development of SAR ATR and attract a wider community of researchers.
Contrast: A Hybrid Architecture of Transformers and State Space Models for Low-Level Vision
Authors: Aman Urumbekov, Zheng Chen
Date: 2025-01-23 00:00:00
Tasks: Image Super-Resolution, Mamba, State Space Models, Super-Resolution

Transformers have become increasingly popular for image super-resolution (SR) tasks due to their strong global context modeling capabilities. However, their quadratic computational complexity necessitates the use of window-based attention mechanisms, which restricts the receptive field and limits effective context expansion. Recently, the Mamba architecture has emerged as a promising alternative with linear computational complexity, allowing it to avoid window mechanisms and maintain a large receptive field. Nevertheless, Mamba faces challenges in handling long-context dependencies when high pixel-level precision is required, as in SR tasks. This is due to its hidden state mechanism, which can compress and store a substantial amount of context but only in an approximate manner, leading to inaccuracies that transformers do not suffer from. In this paper, we propose \textbf{Contrast}, a hybrid SR model that combines \textbf{Con}volutional, \textbf{Tra}nsformer, and \textbf{St}ate Space components, effectively blending the strengths of transformers and Mamba to address their individual limitations. By integrating transformer and state space mechanisms, \textbf{Contrast} compensates for the shortcomings of each approach, enhancing both global context modeling and pixel-level accuracy. We demonstrate that combining these two architectures allows us to mitigate the problems inherent in each, resulting in improved performance on image super-resolution tasks.
Polyhedra Encoding Transformers: Enhancing Diffusion MRI Analysis Beyond Voxel and Volumetric Embedding
Authors: Tianyuan Yao, Zhiyuan Li, Praitayini Kanakaraj, Derek B. Archer, Kurt Schilling, Lori Beason-Held, Susan Resnick, Bennett A. Landman, Yuankai Huo
Date: 2025-01-23 00:00:00
Tasks: Diffusion  MRI

Diffusion-weighted Magnetic Resonance Imaging (dMRI) is an essential tool in neuroimaging. It is arguably the sole noninvasive technique for examining the microstructural properties and structural connectivity of the brain. Recent years have seen the emergence of machine learning and data-driven approaches that enhance the speed, accuracy, and consistency of dMRI data analysis. However, traditional deep learning models often fell short, as they typically utilize pixel-level or volumetric patch-level embeddings similar to those used in structural MRI, and do not account for the unique distribution of various gradient encodings. In this paper, we propose a novel method called Polyhedra Encoding Transformer (PE-Transformer) for dMRI, designed specifically to handle spherical signals. Our approach involves projecting an icosahedral polygon onto a unit sphere to resample signals from predetermined directions. These resampled signals are then transformed into embeddings, which are processed by a transformer encoder that incorporates orientational information reflective of the icosahedral structure. Through experimental validation with various gradient encoding protocols, our method demonstrates superior accuracy in estimating multi-compartment models and Fiber Orientation Distributions (FOD), outperforming both conventional CNN architectures and standard transformers.
MSF: Efficient Diffusion Model Via Multi-Scale Latent Factorize
Authors: Haohang Xu, Longyu Chen, Shuangrui Ding, Yilin Gao, Dongsheng Jiang, Yin Li, Shugong Xu, Junqing Yu, Wei Yang
Date: 2025-01-23 00:00:00
Tasks: Image Generation

Diffusion-based generative models have achieved remarkable progress in visual content generation. However, traditional diffusion models directly denoise the entire image from noisy inputs, disregarding the hierarchical structure present in visual signals. This method is computationally intensive, especially for high-resolution image generation. Signal processing often leverages hierarchical decompositions; for instance, Fourier analysis decomposes signals by frequency, while wavelet analysis captures localized frequency components, reflecting both spatial and frequency information simultaneously. Inspired by these principles, we propose a multiscale diffusion framework that generates hierarchical visual representations, which are subsequently integrated to form the final output. The diffusion model target, whether raw RGB pixels or latent features from a Variational Autoencoder, s divided into multiple components that each capture distinct spatial levels. The low-resolution component contains the primary informative signal, while higher-resolution components add high-frequency details, such as texture. This approach divides image generation into two stages: producing a low-resolution base signal, followed by a high-resolution residual signal. Both stages can be effectively modeled using simpler, lightweight transformer architectures compared to full-resolution generation. This decomposition is conceptually similar to wavelet decomposition but offers a more streamlined and intuitive design. Our method, termed MSF(short for Multi-Scale Factorization), achieves an FID of 2.2 and an IS of 255.4 on the ImageNet 256x256 benchmark, reducing computational costs by 50% compared to baseline methods.
YOLOSCM: An improved YOLO algorithm for cars detection
Authors: Changhui Deng, Lieyang Chen, Shinan Liu
Date: 2025-01-23 00:00:00
Tasks: Clustering

Detecting objects in urban traffic images presents considerable difficulties because of the following reasons: 1) These images are typically immense in size, encompassing millions or even hundreds of millions of pixels, yet computational resources are constrained. 2) The small size of vehicles in certain scenarios leads to insufficient information for accurate detection. 3) The uneven distribution of vehicles causes inefficient use of computational resources. To address these issues, we propose YOLOSCM (You Only Look Once with Segmentation Clustering Module), an efficient and effective framework. To address the challenges of large-scale images and the non-uniform distribution of vehicles, we propose a Segmentation Clustering Module (SCM). This module adaptively identifies clustered regions, enabling the model to focus on these areas for more precise detection. Additionally, we propose a new training strategy to optimize the detection of small vehicles and densely packed targets in complex urban traffic scenes. We perform extensive experiments on urban traffic datasets to demonstrate the effectiveness and superiority of our proposed approach.
Multi-aspect Knowledge Distillation with Large Language Model
Authors: Taegyeong Lee, Jinsik Bang, Soyeong Kwon, Taehwan Kim
Date: 2025-01-23 00:00:00
Tasks: image-classification, Image Classification, Knowledge Distillation, Language Modeling, Language Modelling, Large Language Model

Recent advancements in deep learning have significantly improved performance on computer vision tasks. Previous image classification methods primarily modify model architectures or add features, and they optimize models using cross-entropy loss on class logits. Since they focus on classifying images with considering class labels, these methods may struggle to learn various \emph{aspects} of classes (e.g., natural positions and shape changes). Rethinking the previous approach from a novel view, we propose a multi-aspect knowledge distillation method using Multimodal Large Language Models (MLLMs). Our approach involves: 1) querying Large Language Model with multi-aspect questions relevant to the knowledge we want to transfer to the model, 2) extracting corresponding logits from MLLM, and 3) expanding the model's output dimensions to distill these multi-aspect logits. We then apply cross-entropy loss to class logits and binary cross-entropy loss to multi-aspect logits. Through our method, the model can learn not only the knowledge about visual aspects but also the abstract and complex aspects that require a deeper understanding. We primarily apply our method to image classification, and to explore the potential for extending our model, we expand it to other tasks, such as object detection. In all experimental results, our method improves the performance of the baselines. Additionally, we analyze the effect of multi-aspect knowledge distillation. These results demonstrate that our method can transfer knowledge about various aspects to the model and the aspect knowledge can enhance model performance in computer vision tasks. This paper demonstrates the great potential of multi-aspect knowledge distillation, and we believe it offers a promising direction for future research in computer vision and beyond.
Retrievals Can Be Detrimental: A Contrastive Backdoor Attack Paradigm on Retrieval-Augmented Diffusion Models
Authors: Hao Fang, Xiaohang Sui, Hongyao Yu, Jiawei Kong, Sijin Yu, Bin Chen, Hao Wu, Shu-Tao Xia
Date: 2025-01-23 00:00:00
Tasks: Backdoor Attack, Contrastive Learning, RAG, Retrieval, Retrieval-augmented Generation

Diffusion models (DMs) have recently demonstrated remarkable generation capability. However, their training generally requires huge computational resources and large-scale datasets. To solve these, recent studies empower DMs with the advanced Retrieval-Augmented Generation (RAG) technique and propose retrieval-augmented diffusion models (RDMs). By incorporating rich knowledge from an auxiliary database, RAG enhances diffusion models' generation and generalization ability while significantly reducing model parameters. Despite the great success, RAG may introduce novel security issues that warrant further investigation. In this paper, we reveal that the RDM is susceptible to backdoor attacks by proposing a multimodal contrastive attack approach named BadRDM. Our framework fully considers RAG's characteristics and is devised to manipulate the retrieved items for given text triggers, thereby further controlling the generated contents. Specifically, we first insert a tiny portion of images into the retrieval database as target toxicity surrogates. Subsequently, a malicious variant of contrastive learning is adopted to inject backdoors into the retriever, which builds shortcuts from triggers to the toxicity surrogates. Furthermore, we enhance the attacks through novel entropy-based selection and generative augmentation strategies that can derive better toxicity surrogates. Extensive experiments on two mainstream tasks demonstrate the proposed BadRDM achieves outstanding attack effects while preserving the model's benign utility.
CuriousBot: Interactive Mobile Exploration via Actionable 3D Relational Object Graph
Authors: YiXuan Wang, Leonor Fermoselle, Tarik Kelestemur, Jiuguang Wang, Yunzhu Li
Date: 2025-01-23 00:00:00
Tasks: Object

Mobile exploration is a longstanding challenge in robotics, yet current methods primarily focus on active perception instead of active interaction, limiting the robot's ability to interact with and fully explore its environment. Existing robotic exploration approaches via active interaction are often restricted to tabletop scenes, neglecting the unique challenges posed by mobile exploration, such as large exploration spaces, complex action spaces, and diverse object relations. In this work, we introduce a 3D relational object graph that encodes diverse object relations and enables exploration through active interaction. We develop a system based on this representation and evaluate it across diverse scenes. Our qualitative and quantitative results demonstrate the system's effectiveness and generalization capabilities, outperforming methods that rely solely on vision-language models (VLMs).
Gradient-Free Adversarial Purification with Diffusion Models
Authors: Xuelong Dai, Dong Wang, Duan Mingxing, Bin Xiao
Date: 2025-01-23 00:00:00
Tasks: Adversarial Defense, Adversarial Purification, Super-Resolution

Adversarial training and adversarial purification are two effective and practical defense methods to enhance a model's robustness against adversarial attacks. However, adversarial training necessitates additional training, while adversarial purification suffers from low time efficiency. More critically, current defenses are designed under the perturbation-based adversarial threat model, which is ineffective against the recently proposed unrestricted adversarial attacks. In this paper, we propose an effective and efficient adversarial defense method that counters both perturbation-based and unrestricted adversarial attacks. Our defense is inspired by the observation that adversarial attacks are typically located near the decision boundary and are sensitive to pixel changes. To address this, we introduce adversarial anti-aliasing to mitigate adversarial modifications. Additionally, we propose adversarial super-resolution, which leverages prior knowledge from clean datasets to benignly recover images. These approaches do not require additional training and are computationally efficient without calculating gradients. Extensive experiments against both perturbation-based and unrestricted adversarial attacks demonstrate that our defense method outperforms state-of-the-art adversarial purification methods.