pwc-archive/papers-with-abstracts huggingface dataset:
Unsupervised Contrastive Analysis for Salient Pattern Detection using Conditional Diffusion Models
Authors: Cristiano Patrício, Carlo Alberto Barbano, Attilio Fiandrotti, Riccardo Renzulli, Marco Grangetto, Luis F. Teixeira, João C. Neves
Date: 2024-06-02 00:00:00
Tasks: Contrastive Learning, Data Augmentation, image-classification, Image Classification

Contrastive Analysis (CA) regards the problem of identifying patterns in images that allow distinguishing between a background (BG) dataset (i.e. healthy subjects) and a target (TG) dataset (i.e. unhealthy subjects). Recent works on this topic rely on variational autoencoders (VAE) or contrastive learning strategies to learn the patterns that separate TG samples from BG samples in a supervised manner. However, the dependency on target (unhealthy) samples can be challenging in medical scenarios due to their limited availability. Also, the blurred reconstructions of VAEs lack utility and interpretability. In this work, we redefine the CA task by employing a self-supervised contrastive encoder to learn a latent representation encoding only common patterns from input images, using samples exclusively from the BG dataset during training, and approximating the distribution of the target patterns by leveraging data augmentation techniques. Subsequently, we exploit state-of-the-art generative methods, i.e. diffusion models, conditioned on the learned latent representation to produce a realistic (healthy) version of the input image encoding solely the common patterns. Thorough validation on a facial image dataset and experiments across three brain MRI datasets demonstrate that conditioning the generative process of state-of-the-art generative methods with the latent representation from our self-supervised contrastive encoder yields improvements in the generated image quality and in the accuracy of image classification. The code is available at https://github.com/CristianoPatricio/unsupervised-contrastive-cond-diff.
Sensitivity-Informed Augmentation for Robust Segmentation
Authors: Laura Zheng, Wenjie Wei, Tony Wu, Jacob Clements, Shreelekha Revankar, Andre Harrison, Yu Shen, Ming C. Lin
Date: 2024-06-03 00:00:00
Tasks: Autonomous Driving, Data Augmentation, Segmentation, Sensitivity, Virtual Try-on

Segmentation is an integral module in many visual computing applications such as virtual try-on, medical imaging, autonomous driving, and agricultural automation. These applications often involve either widespread consumer use or highly variable environments, both of which can degrade the quality of visual sensor data, whether from a common mobile phone or an expensive satellite imaging camera. In addition to external noises like user difference or weather conditions, internal noises such as variations in camera quality or lens distortion can affect the performance of segmentation models during both development and deployment. In this work, we present an efficient, adaptable, and gradient-free method to enhance the robustness of learning-based segmentation models across training. First, we introduce a novel adaptive sensitivity analysis (ASA) using Kernel Inception Distance (KID) on basis perturbations to benchmark perturbation sensitivity of pre-trained segmentation models. Then, we model the sensitivity curve using the adaptive SA and sample perturbation hyperparameter values accordingly. Finally, we conduct adversarial training with the selected perturbation values and dynamically re-evaluate robustness during online training. Our method, implemented end-to-end with minimal fine-tuning required, consistently outperforms state-of-the-art data augmentation techniques for segmentation. It shows significant improvement in both clean data evaluation and real-world adverse scenario evaluation across various segmentation datasets used in visual computing and computer graphics applications.
FaceCom: Towards High-fidelity 3D Facial Shape Completion via Optimization and Inpainting Guidance
Authors: Yinglong Li, Hongyu Wu, Xiaogang Wang, Qingzhao Qin, Yijiao Zhao, Yong Wang, Aimin Hao
Date: 2024-06-04 00:00:00
Proceeding: CVPR 2024 1
Tasks: Image Inpainting

We propose FaceCom, a method for 3D facial shape completion, which delivers high-fidelity results for incomplete facial inputs of arbitrary forms. Unlike end-to-end shape completion methods based on point clouds or voxels, our approach relies on a mesh-based generative network that is easy to optimize, enabling it to handle shape completion for irregular facial scans. We first train a shape generator on a mixed 3D facial dataset containing 2405 identities. Based on the incomplete facial input, we fit complete faces using an optimization approach under image inpainting guidance. The completion results are refined through a post-processing step. FaceCom demonstrates the ability to effectively and naturally complete facial scan data with varying missing regions and degrees of missing areas. Our method can be used in medical prosthetic fabrication and the registration of deficient scanning data. Our experimental results demonstrate that FaceCom achieves exceptional performance in fitting and shape completion tasks. The code is available at https://github.com/dragonylee/FaceCom.git.
Advancing Generalized Transfer Attack with Initialization Derived Bilevel Optimization and Dynamic Sequence Truncation
Authors: Yaohua Liu, Jiaxin Gao, Xuan Liu, Xianghao Jiao, Xin Fan, Risheng Liu
Date: 2024-06-04 00:00:00
Tasks: Bilevel Optimization

Transfer attacks generate significant interest for real-world black-box applications by crafting transferable adversarial examples through surrogate models. Whereas, existing works essentially directly optimize the single-level objective w.r.t. the surrogate model, which always leads to poor interpretability of attack mechanism and limited generalization performance over unknown victim models. In this work, we propose the \textbf{B}il\textbf{E}vel \textbf{T}ransfer \textbf{A}ttac\textbf{K} (BETAK) framework by establishing an initialization derived bilevel optimization paradigm, which explicitly reformulates the nested constraint relationship between the Upper-Level (UL) pseudo-victim attacker and the Lower-Level (LL) surrogate attacker. Algorithmically, we introduce the Hyper Gradient Response (HGR) estimation as an effective feedback for the transferability over pseudo-victim attackers, and propose the Dynamic Sequence Truncation (DST) technique to dynamically adjust the back-propagation path for HGR and reduce computational overhead simultaneously. Meanwhile, we conduct detailed algorithmic analysis and provide convergence guarantee to support non-convexity of the LL surrogate attacker. Extensive evaluations demonstrate substantial improvement of BETAK (e.g., $\mathbf{53.41}$\% increase of attack success rates against IncRes-v$2_{ens}$) against different victims and defense methods in targeted and untargeted attack scenarios. The source code is available at https://github.com/callous-youth/BETAK.
OpenGaussian: Towards Point-Level 3D Gaussian-based Open Vocabulary Understanding
Authors: Yanmin Wu, Jiarui Meng, Haijie Li, Chenming Wu, Yahao Shi, Xinhua Cheng, Chen Zhao, Haocheng Feng, Errui Ding, Jingdong Wang, Jian Zhang
Date: 2024-06-04 00:00:00
Tasks: 3DGS, Object

This paper introduces OpenGaussian, a method based on 3D Gaussian Splatting (3DGS) capable of 3D point-level open vocabulary understanding. Our primary motivation stems from observing that existing 3DGS-based open vocabulary methods mainly focus on 2D pixel-level parsing. These methods struggle with 3D point-level tasks due to weak feature expressiveness and inaccurate 2D-3D feature associations. To ensure robust feature presentation and 3D point-level understanding, we first employ SAM masks without cross-frame associations to train instance features with 3D consistency. These features exhibit both intra-object consistency and inter-object distinction. Then, we propose a two-stage codebook to discretize these features from coarse to fine levels. At the coarse level, we consider the positional information of 3D points to achieve location-based clustering, which is then refined at the fine level. Finally, we introduce an instance-level 3D-2D feature association method that links 3D points to 2D masks, which are further associated with 2D CLIP features. Extensive experiments, including open vocabulary-based 3D object selection, 3D point cloud understanding, click-based 3D object selection, and ablation studies, demonstrate the effectiveness of our proposed method. The source code is available at our project page: https://3d-aigc.github.io/OpenGaussian
DA-HFNet: Progressive Fine-Grained Forgery Image Detection and Localization Based on Dual Attention
Authors: Yang Liu, Xiaofei Li, Jun Zhang, Shengze Hu, Jun Lei
Date: 2024-06-03 00:00:00
Tasks: Forgery Image Detection

The increasing difficulty in accurately detecting forged images generated by AIGC(Artificial Intelligence Generative Content) poses many risks, necessitating the development of effective methods to identify and further locate forged areas. In this paper, to facilitate research efforts, we construct a DA-HFNet forged image dataset guided by text or image-assisted GAN and Diffusion model. Our goal is to utilize a hierarchical progressive network to capture forged artifacts at different scales for detection and localization. Specifically, it relies on a dual-attention mechanism to adaptively fuse multi-modal image features in depth, followed by a multi-branch interaction network to thoroughly interact image features at different scales and improve detector performance by leveraging dependencies between layers. Additionally, we extract more sensitive noise fingerprints to obtain more prominent forged artifact features in the forged areas. Extensive experiments validate the effectiveness of our approach, demonstrating significant performance improvements compared to state-of-the-art methods for forged image detection and localization.The code and dataset will be released in the future.
Leveraging Predicate and Triplet Learning for Scene Graph Generation
Authors: Jiankai Li, Yunhong Wang, Xiefan Guo, Ruijie Yang, Weixin Li
Date: 2024-06-04 00:00:00
Proceeding: CVPR 2024 1
Tasks: Graph Generation, Relation, Scene Graph Generation, Transfer Learning, Triplet

Scene Graph Generation (SGG) aims to identify entities and predict the relationship triplets \textit{\textless subject, predicate, object\textgreater } in visual scenes. Given the prevalence of large visual variations of subject-object pairs even in the same predicate, it can be quite challenging to model and refine predicate representations directly across such pairs, which is however a common strategy adopted by most existing SGG methods. We observe that visual variations within the identical triplet are relatively small and certain relation cues are shared in the same type of triplet, which can potentially facilitate the relation learning in SGG. Moreover, for the long-tail problem widely studied in SGG task, it is also crucial to deal with the limited types and quantity of triplets in tail predicates. Accordingly, in this paper, we propose a Dual-granularity Relation Modeling (DRM) network to leverage fine-grained triplet cues besides the coarse-grained predicate ones. DRM utilizes contexts and semantics of predicate and triplet with Dual-granularity Constraints, generating compact and balanced representations from two perspectives to facilitate relation recognition. Furthermore, a Dual-granularity Knowledge Transfer (DKT) strategy is introduced to transfer variation from head predicates/triplets to tail ones, aiming to enrich the pattern diversity of tail classes to alleviate the long-tail problem. Extensive experiments demonstrate the effectiveness of our method, which establishes new state-of-the-art performance on Visual Genome, Open Image, and GQA datasets. Our code is available at \url{https://github.com/jkli1998/DRM}
Multi-Scale Direction-Aware Network for Infrared Small Target Detection
Authors: Jinmiao Zhao, Zelin Shi, Chuang Yu, Yunpeng Liu
Date: 2024-06-04 00:00:00

Infrared small target detection faces the problem that it is difficult to effectively separate the background and the target. Existing deep learning-based methods focus on appearance features and ignore high-frequency directional features. Therefore, we propose a multi-scale direction-aware network (MSDA-Net), which is the first attempt to integrate the high-frequency directional features of infrared small targets as domain prior knowledge into neural networks. Specifically, an innovative multi-directional feature awareness (MDFA) module is constructed, which fully utilizes the prior knowledge of targets and emphasizes the focus on high-frequency directional features. On this basis, combined with the multi-scale local relation learning (MLRL) module, a multi-scale direction-aware (MSDA) module is further constructed. The MSDA module promotes the full extraction of local relations at different scales and the full perception of key features in different directions. Meanwhile, a high-frequency direction injection (HFDI) module without training parameters is constructed to inject the high-frequency directional information of the original image into the network. This helps guide the network to pay attention to detailed information such as target edges and shapes. In addition, we propose a feature aggregation (FA) structure that aggregates multi-level features to solve the problem of small targets disappearing in deep feature maps. Furthermore, a lightweight feature alignment fusion (FAF) module is constructed, which can effectively alleviate the pixel offset existing in multi-level feature map fusion. Extensive experimental results show that our MSDA-Net achieves state-of-the-art (SOTA) results on the public NUDT-SIRST, SIRST and IRSTD-1k datasets.
3D Imaging of Complex Specular Surfaces by Fusing Polarimetric and Deflectometric Information
Authors: Jiazhang Wang, Oliver Cossairt, Florian Willomitzer
Date: 2024-06-04 00:00:00

Accurate and fast 3D imaging of specular surfaces still poses major challenges for state-of-the-art optical measurement principles. Frequently used methods, such as phase-measuring deflectometry (PMD) or shape-from-polarization (SfP), rely on strong assumptions about the measured objects, limiting their generalizability in broader application areas like medical imaging, industrial inspection, virtual reality, or cultural heritage analysis. In this paper, we introduce a measurement principle that utilizes a novel technique to effectively encode and decode the information contained in a light field reflected off a specular surface. We combine polarization cues from SfP with geometric information obtained from PMD to resolve all arising ambiguities in the 3D measurement. Moreover, our approach removes the unrealistic orthographic imaging assumption for SfP, which significantly improves the respective results. We showcase our new technique by demonstrating single-shot and multi-shot measurements on complex-shaped specular surfaces, displaying an evaluated accuracy of surface normals below $0.6^\circ$.
Choroidal Vessel Segmentation on Indocyanine Green Angiography Images via Human-in-the-Loop Labeling
Authors: Ruoyu Chen, Ziwei Zhao, Mayinuer Yusufu, Xianwen Shang, Danli Shi, Mingguang He
Date: 2024-06-04 00:00:00
Tasks: Segmentation

Human-in-the-loop (HITL) strategy has been recently introduced into the field of medical image processing. Indocyanine green angiography (ICGA) stands as a well-established examination for visualizing choroidal vasculature and detecting chorioretinal diseases. However, the intricate nature of choroidal vascular networks makes large-scale manual segmentation of ICGA images challenging. Thus, the study aims to develop a high-precision choroidal vessel segmentation model with limited labor using HITL framework. We utilized a multi-source ICGA dataset, including 55 degree view and ultra-widefield ICGA (UWF-ICGA) images for model development. The choroidal vessel network was pre-segmented by a pre-trained vessel segmentation model, and then manually modified by two ophthalmologists. Choroidal vascular diameter, density, complexity, tortuosity, and branching angle were automatically quantified based on the segmentation. We finally conducted four cycles of HITL. One hundred and fifty 55 degree view ICGA images were used for the first three cycles (50 images per cycle), and twenty UWF-ICGA images for the last cycle. The average time needed to manually correct a pre-segmented ICGA image per cycle reduced from 20 minutes to 1 minute. High segmentation accuracy has been achieved on both 55 degree view ICGA and UWF-ICGA images. Additionally, the multi-dimensional choroidal vascular parameters were significantly associated with various chorioretinal diseases. Our study not only demonstrated the feasibility of the HITL strategy in improving segmentation performance with reduced manual labeling, but also innovatively introduced several risk predictors for choroidal abnormalities.
Dealing with All-stage Missing Modality: Towards A Universal Model with Robust Reconstruction and Personalization
Authors: Yunpeng Zhao, Cheng Chen, Qing You Pang, Quanzheng Li, Carol Tang, Beng-Ti Ang, Yueming Jin
Date: 2024-06-04 00:00:00
Tasks: All, Brain Tumor Segmentation, Tumor Segmentation

Addressing missing modalities presents a critical challenge in multimodal learning. Current approaches focus on developing models that can handle modality-incomplete inputs during inference, assuming that the full set of modalities are available for all the data during training. This reliance on full-modality data for training limits the use of abundant modality-incomplete samples that are often encountered in practical settings. In this paper, we propose a robust universal model with modality reconstruction and model personalization, which can effectively tackle the missing modality at both training and testing stages. Our method leverages a multimodal masked autoencoder to reconstruct the missing modality and masked patches simultaneously, incorporating an innovative distribution approximation mechanism to fully utilize both modality-complete and modality-incomplete data. The reconstructed modalities then contributes to our designed data-model co-distillation scheme to guide the model learning in the presence of missing modalities. Moreover, we propose a CLIP-driven hyper-network to personalize partial model parameters, enabling the model to adapt to each distinct missing modality scenario. Our method has been extensively validated on two brain tumor segmentation benchmarks. Experimental results demonstrate the promising performance of our method, which consistently exceeds previous state-of-the-art approaches under the all-stage missing modality settings with different missing ratios. Code will be available.
Superhuman performance in urology board questions by an explainable large language model enabled for context integration of the European Association of Urology guidelines: the UroBot study
Authors: Martin J. Hetz, Nicolas Carl, Sarah Haggenmüller, Christoph Wies, Maurice Stephan Michel, Frederik Wessels, Titus J. Brinker
Date: 2024-06-03 00:00:00
Tasks: Chatbot, Language Modeling, Language Modelling, Large Language Model, Medical Question Answering, MedQA, Question Answering, RAG, Retrieval-augmented Generation

Large Language Models (LLMs) are revolutionizing medical Question-Answering (medQA) through extensive use of medical literature. However, their performance is often hampered by outdated training data and a lack of explainability, which limits clinical applicability. This study aimed to create and assess UroBot, a urology-specialized chatbot, by comparing it with state-of-the-art models and the performance of urologists on urological board questions, ensuring full clinician-verifiability. UroBot was developed using OpenAI's GPT-3.5, GPT-4, and GPT-4o models, employing retrieval-augmented generation (RAG) and the latest 2023 guidelines from the European Association of Urology (EAU). The evaluation included ten runs of 200 European Board of Urology (EBU) In-Service Assessment (ISA) questions, with performance assessed by the mean Rate of Correct Answers (RoCA). UroBot-4o achieved an average RoCA of 88.4%, surpassing GPT-4o by 10.8%, with a score of 77.6%. It was also clinician-verifiable and exhibited the highest run agreement as indicated by Fleiss' Kappa (k = 0.979). By comparison, the average performance of urologists on board questions, as reported in the literature, is 68.7%. UroBot's clinician-verifiable nature and superior accuracy compared to both existing models and urologists on board questions highlight its potential for clinical integration. The study also provides the necessary code and instructions for further development of UroBot.
Can Dense Connectivity Benefit Outlier Detection? An Odyssey with NAS
Authors: Hao Fu, Tunhou Zhang, Hai Li, Yiran Chen
Date: 2024-06-04 00:00:00
Tasks: Neural Architecture Search, Outlier Detection, Out of Distribution (OOD) Detection

Recent advances in Out-of-Distribution (OOD) Detection is the driving force behind safe and reliable deployment of Convolutional Neural Networks (CNNs) in real world applications. However, existing studies focus on OOD detection through confidence score and deep generative model-based methods, without considering the impact of DNN structures, especially dense connectivity in architecture fabrications. In addition, existing outlier detection approaches exhibit high variance in generalization performance, lacking stability and confidence in evaluating and ranking different outlier detectors. In this work, we propose a novel paradigm, Dense Connectivity Search of Outlier Detector (DCSOD), that automatically explore the dense connectivity of CNN architectures on near-OOD detection task using Neural Architecture Search (NAS). We introduce a hierarchical search space containing versatile convolution operators and dense connectivity, allowing a flexible exploration of CNN architectures with diverse connectivity patterns. To improve the quality of evaluation on OOD detection during search, we propose evolving distillation based on our multi-view feature learning explanation. Evolving distillation stabilizes training for OOD detection evaluation, thus improves the quality of search. We thoroughly examine DCSOD on CIFAR benchmarks under OOD detection protocol. Experimental results show that DCSOD achieve remarkable performance over widely used architectures and previous NAS baselines. Notably, DCSOD achieves state-of-the-art (SOTA) performance on CIFAR benchmark, with AUROC improvement of $\sim$1.0%.
Exploring Real World Map Change Generalization of Prior-Informed HD Map Prediction Models
Authors: Samuel M. Bateman, Ning Xu, H. Charles Zhao, Yael Ben Shalom, Vince Gong, Greg Long, Will Maddern
Date: 2024-06-04 00:00:00
Tasks: Autonomous Driving

Building and maintaining High-Definition (HD) maps represents a large barrier to autonomous vehicle deployment. This, along with advances in modern online map detection models, has sparked renewed interest in the online mapping problem. However, effectively predicting online maps at a high enough quality to enable safe, driverless deployments remains a significant challenge. Recent work on these models proposes training robust online mapping systems using low quality map priors with synthetic perturbations in an attempt to simulate out-of-date HD map priors. In this paper, we investigate how models trained on these synthetically perturbed map priors generalize to performance on deployment-scale, real world map changes. We present a large-scale experimental study to determine which synthetic perturbations are most useful in generalizing to real world HD map changes, evaluated using multiple years of real-world autonomous driving data. We show there is still a substantial sim2real gap between synthetic prior perturbations and observed real-world changes, which limits the utility of current prior-informed HD map prediction models.