pwc-archive/papers-with-abstracts huggingface dataset:
Only My Model On My Data: A Privacy Preserving Approach Protecting one Model and Deceiving Unauthorized Black-Box Models
Authors: Weiheng Chai, Brian Testa, Huantao Ren, Asif Salekin, Senem Velipasalar
Date: 2024-02-14 00:00:00
Tasks: Adversarial Attack, Classification, Emotion Classification, Face Recognition, image-classification, Image Classification, Medical Image Classification, model, Privacy Preserving

Deep neural networks are extensively applied to real-world tasks, such as face recognition and medical image classification, where privacy and data protection are critical. Image data, if not protected, can be exploited to infer personal or contextual information. Existing privacy preservation methods, like encryption, generate perturbed images that are unrecognizable to even humans. Adversarial attack approaches prohibit automated inference even for authorized stakeholders, limiting practical incentives for commercial and widespread adaptation. This pioneering study tackles an unexplored practical privacy preservation use case by generating human-perceivable images that maintain accurate inference by an authorized model while evading other unauthorized black-box models of similar or dissimilar objectives, and addresses the previous research gaps. The datasets employed are ImageNet, for image classification, Celeba-HQ dataset, for identity classification, and AffectNet, for emotion classification. Our results show that the generated images can successfully maintain the accuracy of a protected model and degrade the average accuracy of the unauthorized black-box models to 11.97%, 6.63%, and 55.51% on ImageNet, Celeba-HQ, and AffectNet datasets, respectively.
Few-Shot Object Detection with Sparse Context Transformers
Authors: Jie Mei, Mingyuan Jiu, Hichem Sahbi, Xiaoheng Jiang, Mingliang Xu
Date: 2024-02-14 00:00:00
Tasks: Few-Shot Object Detection, Object, object-detection, Object Detection, Transfer Learning

Few-shot detection is a major task in pattern recognition which seeks to localize objects using models trained with few labeled data. One of the mainstream few-shot methods is transfer learning which consists in pretraining a detection model in a source domain prior to its fine-tuning in a target domain. However, it is challenging for fine-tuned models to effectively identify new classes in the target domain, particularly when the underlying labeled training data are scarce. In this paper, we devise a novel sparse context transformer (SCT) that effectively leverages object knowledge in the source domain, and automatically learns a sparse context from only few training images in the target domain. As a result, it combines different relevant clues in order to enhance the discrimination power of the learned detectors and reduce class confusion. We evaluate the proposed method on two challenging few-shot object detection benchmarks, and empirical results show that the proposed method obtains competitive performance compared to the related state-of-the-art.
Fast Window-Based Event Denoising with Spatiotemporal Correlation Enhancement
Authors: Huachen Fang, Jinjian Wu, Qibin Hou, Weisheng Dong, Guangming Shi
Date: 2024-02-14 00:00:00
Tasks: Denoising

Previous deep learning-based event denoising methods mostly suffer from poor interpretability and difficulty in real-time processing due to their complex architecture designs. In this paper, we propose window-based event denoising, which simultaneously deals with a stack of events while existing element-based denoising focuses on one event each time. Besides, we give the theoretical analysis based on probability distributions in both temporal and spatial domains to improve interpretability. In temporal domain, we use timestamp deviations between processing events and central event to judge the temporal correlation and filter out temporal-irrelevant events. In spatial domain, we choose maximum a posteriori (MAP) to discriminate real-world event and noise, and use the learned convolutional sparse coding to optimize the objective function. Based on the theoretical analysis, we build Temporal Window (TW) module and Soft Spatial Feature Embedding (SSFE) module to process temporal and spatial information separately, and construct a novel multi-scale window-based event denoising network, named MSDNet. The high denoising accuracy and fast running speed of our MSDNet enables us to achieve real-time denoising in complex scenes. Extensive experimental results verify the effectiveness and robustness of our MSDNet. Our algorithm can remove event noise effectively and efficiently and improve the performance of downstream tasks.
MultiMedEval: A Benchmark and a Toolkit for Evaluating Medical Vision-Language Models
Authors: Corentin Royer, Bjoern Menze, Anjany Sekuboyina
Date: 2024-02-14 00:00:00
Tasks: Benchmarking, Diversity

We introduce MultiMedEval, an open-source toolkit for fair and reproducible evaluation of large, medical vision-language models (VLM). MultiMedEval comprehensively assesses the models' performance on a broad array of six multi-modal tasks, conducted over 23 datasets, and spanning over 11 medical domains. The chosen tasks and performance metrics are based on their widespread adoption in the community and their diversity, ensuring a thorough evaluation of the model's overall generalizability. We open-source a Python toolkit (github.com/corentin-ryr/MultiMedEval) with a simple interface and setup process, enabling the evaluation of any VLM in just a few lines of code. Our goal is to simplify the intricate landscape of VLM evaluation, thus promoting fair and uniform benchmarking of future models.
TDViT: Temporal Dilated Video Transformer for Dense Video Tasks
Authors: Guanxiong Sun, Yang Hua, Guosheng Hu, Neil Robertson
Date: 2024-02-14 00:00:00
Tasks: Instance Segmentation, object-detection, Object Detection, Semantic Segmentation, Video Instance Segmentation, Video Object Detection

Deep video models, for example, 3D CNNs or video transformers, have achieved promising performance on sparse video tasks, i.e., predicting one result per video. However, challenges arise when adapting existing deep video models to dense video tasks, i.e., predicting one result per frame. Specifically, these models are expensive for deployment, less effective when handling redundant frames, and difficult to capture long-range temporal correlations. To overcome these issues, we propose a Temporal Dilated Video Transformer (TDViT) that consists of carefully designed temporal dilated transformer blocks (TDTB). TDTB can efficiently extract spatiotemporal representations and effectively alleviate the negative effect of temporal redundancy. Furthermore, by using hierarchical TDTBs, our approach obtains an exponentially expanded temporal receptive field and therefore can model long-range dynamics. Extensive experiments are conducted on two different dense video benchmarks, i.e., ImageNet VID for video object detection and YouTube VIS for video instance segmentation. Excellent experimental results demonstrate the superior efficiency, effectiveness, and compatibility of our method. The code is available at https://github.com/guanxiongsun/vfe.pytorch.
Synthesizing Knowledge-enhanced Features for Real-world Zero-shot Food Detection
Authors: Pengfei Zhou, Weiqing Min, Jiajun Song, Yang Zhang, Shuqiang Jiang
Date: 2024-02-14 00:00:00
Tasks: Attribute, Generalized Zero-Shot Object Detection, Nutrition, Zero-Shot Object Detection

Food computing brings various perspectives to computer vision like vision-based food analysis for nutrition and health. As a fundamental task in food computing, food detection needs Zero-Shot Detection (ZSD) on novel unseen food objects to support real-world scenarios, such as intelligent kitchens and smart restaurants. Therefore, we first benchmark the task of Zero-Shot Food Detection (ZSFD) by introducing FOWA dataset with rich attribute annotations. Unlike ZSD, fine-grained problems in ZSFD like inter-class similarity make synthesized features inseparable. The complexity of food semantic attributes further makes it more difficult for current ZSD methods to distinguish various food categories. To address these problems, we propose a novel framework ZSFDet to tackle fine-grained problems by exploiting the interaction between complex attributes. Specifically, we model the correlation between food categories and attributes in ZSFDet by multi-source graphs to provide prior knowledge for distinguishing fine-grained features. Within ZSFDet, Knowledge-Enhanced Feature Synthesizer (KEFS) learns knowledge representation from multiple sources (e.g., ingredients correlation from knowledge graph) via the multi-source graph fusion. Conditioned on the fusion of semantic knowledge representation, the region feature diffusion model in KEFS can generate fine-grained features for training the effective zero-shot detector. Extensive evaluations demonstrate the superior performance of our method ZSFDet on FOWA and the widely-used food dataset UECFOOD-256, with significant improvements by 1.8% and 3.7% ZSD mAP compared with the strong baseline RRFS. Further experiments on PASCAL VOC and MS COCO prove that enhancement of the semantic knowledge can also improve the performance on general ZSD. Code and dataset are available at https://github.com/LanceZPF/KEFS.
Efficient One-stage Video Object Detection by Exploiting Temporal Consistency
Authors: Guanxiong Sun, Yang Hua, Guosheng Hu, Neil Robertson
Date: 2024-02-14 00:00:00
Tasks: object-detection, Object Detection, Video Object Detection

Recently, one-stage detectors have achieved competitive accuracy and faster speed compared with traditional two-stage detectors on image data. However, in the field of video object detection (VOD), most existing VOD methods are still based on two-stage detectors. Moreover, directly adapting existing VOD methods to one-stage detectors introduces unaffordable computational costs. In this paper, we first analyse the computational bottlenecks of using one-stage detectors for VOD. Based on the analysis, we present a simple yet efficient framework to address the computational bottlenecks and achieve efficient one-stage VOD by exploiting the temporal consistency in video frames. Specifically, our method consists of a location-prior network to filter out background regions and a size-prior network to skip unnecessary computations on low-level feature maps for specific frames. We test our method on various modern one-stage detectors and conduct extensive experiments on the ImageNet VID dataset. Excellent experimental results demonstrate the superior effectiveness, efficiency, and compatibility of our method. The code is available at https://github.com/guanxiongsun/vfe.pytorch.
Switch EMA: A Free Lunch for Better Flatness and Sharpness
Authors: Siyuan Li, Zicheng Liu, Juanxi Tian, Ge Wang, Zedong Wang, Weiyang Jin, Di wu, Cheng Tan, Tao Lin, Yang Liu, Baigui Sun, Stan Z. Li
Date: 2024-02-14 00:00:00
Tasks: Attribute, image-classification, Image Classification, Image Generation, Language Modeling, Language Modelling, object-detection, Object Detection, regression, Self-Supervised Learning, Video Prediction

Exponential Moving Average (EMA) is a widely used weight averaging (WA) regularization to learn flat optima for better generalizations without extra cost in deep neural network (DNN) optimization. Despite achieving better flatness, existing WA methods might fall into worse final performances or require extra test-time computations. This work unveils the full potential of EMA with a single line of modification, i.e., switching the EMA parameters to the original model after each epoch, dubbed as Switch EMA (SEMA). From both theoretical and empirical aspects, we demonstrate that SEMA can help DNNs to reach generalization optima that better trade-off between flatness and sharpness. To verify the effectiveness of SEMA, we conduct comparison experiments with discriminative, generative, and regression tasks on vision and language datasets, including image classification, self-supervised learning, object detection and segmentation, image generation, video prediction, attribute regression, and language modeling. Comprehensive results with popular optimizers and networks show that SEMA is a free lunch for DNN training by improving performances and boosting convergence speeds.
Weatherproofing Retrieval for Localization with Generative AI and Geometric Consistency
Authors: Yannis Kalantidis, Mert Bülent Sarıyıldız, Rafael S. Rezende, Philippe Weinzaepfel, Diane Larlus, Gabriela Csurka
Date: 2024-02-14 00:00:00
Tasks: Image Retrieval, Retrieval, Visual Localization

State-of-the-art visual localization approaches generally rely on a first image retrieval step whose role is crucial. Yet, retrieval often struggles when facing varying conditions, due to e.g. weather or time of day, with dramatic consequences on the visual localization accuracy. In this paper, we improve this retrieval step and tailor it to the final localization task. Among the several changes we advocate for, we propose to synthesize variants of the training set images, obtained from generative text-to-image models, in order to automatically expand the training set towards a number of nameable variations that particularly hurt visual localization. After expanding the training set, we propose a training approach that leverages the specificities and the underlying geometry of this mix of real and synthetic images. We experimentally show that those changes translate into large improvements for the most challenging visual localization datasets. Project page: https://europe.naverlabs.com/ret4loc
Domain-adaptive and Subgroup-specific Cascaded Temperature Regression for Out-of-distribution Calibration
Authors: Jiexin Wang, Jiahao Chen, Bing Su
Date: 2024-02-14 00:00:00
Tasks: Data Augmentation, regression

Although deep neural networks yield high classification accuracy given sufficient training data, their predictions are typically overconfident or under-confident, i.e., the prediction confidences cannot truly reflect the accuracy. Post-hoc calibration tackles this problem by calibrating the prediction confidences without re-training the classification model. However, current approaches assume congruence between test and validation data distributions, limiting their applicability to out-of-distribution scenarios. To this end, we propose a novel meta-set-based cascaded temperature regression method for post-hoc calibration. Our method tailors fine-grained scaling functions to distinct test sets by simulating various domain shifts through data augmentation on the validation set. We partition each meta-set into subgroups based on predicted category and confidence level, capturing diverse uncertainties. A regression network is then trained to derive category-specific and confidence-level-specific scaling, achieving calibration across meta-sets. Extensive experimental results on MNIST, CIFAR-10, and TinyImageNet demonstrate the effectiveness of the proposed method.
Traj-LIO: A Resilient Multi-LiDAR Multi-IMU State Estimator Through Sparse Gaussian Process
Authors: Xin Zheng, Jianke Zhu
Date: 2024-02-14 00:00:00

Nowadays, sensor suits have been equipped with redundant LiDARs and IMUs to mitigate the risks associated with sensor failure. It is challenging for the previous discrete-time and IMU-driven kinematic systems to incorporate multiple asynchronized sensors, which are susceptible to abnormal IMU data. To address these limitations, we introduce a multi-LiDAR multi-IMU state estimator by taking advantage of Gaussian Process (GP) that predicts a non-parametric continuous-time trajectory to capture sensors' spatial-temporal movement with limited control states. Since the kinematic model driven by three types of linear time-invariant stochastic differential equations are independent of external sensor measurements, our proposed approach is capable of handling different sensor configurations and resilient to sensor failures. Moreover, we replace the conventional $\mathrm{SE}(3)$ state representation with the combination of $\mathrm{SO}(3)$ and vector space, which enables GP-based LiDAR-inertial system to fulfill the real-time requirement. Extensive experiments on the public datasets demonstrate the versatility and resilience of our proposed multi-LiDAR multi-IMU state estimator. To contribute to the community, we will make our source code publicly available.
OmniMedVQA: A New Large-Scale Comprehensive Evaluation Benchmark for Medical LVLM
Authors: Yutao Hu, Tianbin Li, Quanfeng Lu, Wenqi Shao, Junjun He, Yu Qiao, Ping Luo
Date: 2024-02-14 00:00:00
Proceeding: CVPR 2024 1
Tasks: Medical Visual Question Answering, Question Answering, Visual Question Answering, Visual Question Answering (VQA)

Large Vision-Language Models (LVLMs) have demonstrated remarkable capabilities in various multimodal tasks. However, their potential in the medical domain remains largely unexplored. A significant challenge arises from the scarcity of diverse medical images spanning various modalities and anatomical regions, which is essential in real-world medical applications. To solve this problem, in this paper, we introduce OmniMedVQA, a novel comprehensive medical Visual Question Answering (VQA) benchmark. This benchmark is collected from 73 different medical datasets, including 12 different modalities and covering more than 20 distinct anatomical regions. Importantly, all images in this benchmark are sourced from authentic medical scenarios, ensuring alignment with the requirements of the medical field and suitability for evaluating LVLMs. Through our extensive experiments, we have found that existing LVLMs struggle to address these medical VQA problems effectively. Moreover, what surprises us is that medical-specialized LVLMs even exhibit inferior performance to those general-domain models, calling for a more versatile and robust LVLM in the biomedical field. The evaluation results not only reveal the current limitations of LVLM in understanding real medical images but also highlight our dataset's significance. Our code with dataset are available at https://github.com/OpenGVLab/Multi-Modality-Arena.
Generalized Portrait Quality Assessment
Authors: Nicolas Chahine, Sira Ferradans, Javier Vazquez-Corral, Jean Ponce
Date: 2024-02-14 00:00:00
Tasks: Face Image Quality Assessment

Automated and robust portrait quality assessment (PQA) is of paramount importance in high-impact applications such as smartphone photography. This paper presents FHIQA, a learning-based approach to PQA that introduces a simple but effective quality score rescaling method based on image semantics, to enhance the precision of fine-grained image quality metrics while ensuring robust generalization to various scene settings beyond the training dataset. The proposed approach is validated by extensive experiments on the PIQ23 benchmark and comparisons with the current state of the art. The source code of FHIQA will be made publicly available on the PIQ23 GitHub repository at https://github.com/DXOMARK-Research/PIQ2023.
Less is More: Fewer Interpretable Region via Submodular Subset Selection
Authors: Ruoyu Chen, Hua Zhang, Siyuan Liang, Jingzhi Li, Xiaochun Cao
Date: 2024-02-14 00:00:00
Tasks: Error Understanding, Image Attribution, Interpretability Techniques for Deep Learning

Image attribution algorithms aim to identify important regions that are highly relevant to model decisions. Although existing attribution solutions can effectively assign importance to target elements, they still face the following challenges: 1) existing attribution methods generate inaccurate small regions thus misleading the direction of correct attribution, and 2) the model cannot produce good attribution results for samples with wrong predictions. To address the above challenges, this paper re-models the above image attribution problem as a submodular subset selection problem, aiming to enhance model interpretability using fewer regions. To address the lack of attention to local regions, we construct a novel submodular function to discover more accurate small interpretation regions. To enhance the attribution effect for all samples, we also impose four different constraints on the selection of sub-regions, i.e., confidence, effectiveness, consistency, and collaboration scores, to assess the importance of various subsets. Moreover, our theoretical analysis substantiates that the proposed function is in fact submodular. Extensive experiments show that the proposed method outperforms SOTA methods on two face datasets (Celeb-A and VGG-Face2) and one fine-grained dataset (CUB-200-2011). For correctly predicted samples, the proposed method improves the Deletion and Insertion scores with an average of 4.9% and 2.5% gain relative to HSIC-Attribution. For incorrectly predicted samples, our method achieves gains of 81.0% and 18.4% compared to the HSIC-Attribution algorithm in the average highest confidence and Insertion score respectively. The code is released at https://github.com/RuoyuChen10/SMDL-Attribution.
Crop and Couple: cardiac image segmentation using interlinked specialist networks
Authors: Abbas Khan, Muhammad Asad, Martin Benning, Caroline Roney, Gregory Slabaugh
Date: 2024-02-14 00:00:00
Tasks: Anatomy, Image Segmentation, Segmentation, Semantic Segmentation

Diagnosis of cardiovascular disease using automated methods often relies on the critical task of cardiac image segmentation. We propose a novel strategy that performs segmentation using specialist networks that focus on a single anatomy (left ventricle, right ventricle, or myocardium). Given an input long-axis cardiac MR image, our method performs a ternary segmentation in the first stage to identify these anatomical regions, followed by cropping the original image to focus subsequent processing on the anatomical regions. The specialist networks are coupled through an attention mechanism that performs cross-attention to interlink features from different anatomies, serving as a soft relative shape prior. Central to our approach is an additive attention block (E-2A block), which is used throughout our architecture thanks to its efficiency.
Semi-Supervised Diffusion Model for Brain Age Prediction
Authors: Ayodeji Ijishakin, Sophie Martin, Florence Townend, Federica Agosta, Edoardo Gioele Spinelli, Silvia Basaia, Paride Schito, Yuri Falzone, Massimo Filippi, James Cole, Andrea Malaspina
Date: 2024-02-14 00:00:00
Tasks: model, Prediction

Brain age prediction models have succeeded in predicting clinical outcomes in neurodegenerative diseases, but can struggle with tasks involving faster progressing diseases and low quality data. To enhance their performance, we employ a semi-supervised diffusion model, obtaining a 0.83(p<0.01) correlation between chronological and predicted age on low quality T1w MR images. This was competitive with state-of-the-art non-generative methods. Furthermore, the predictions produced by our model were significantly associated with survival length (r=0.24, p<0.05) in Amyotrophic Lateral Sclerosis. Thus, our approach demonstrates the value of diffusion-based architectures for the task of brain age prediction.