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Tri-VAE: Triplet Variational Autoencoder for Unsupervised Anomaly Detection in Brain Tumor MRI

Tri-VAE: Triplet Variational Autoencoder for Unsupervised An...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Hansen Wijanarko Evelyne Calista Li-Fen Chen Yong-Sheng Chen National Yang Ming Chiao Tung University Taiwan

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The intricate manifestations of pathological brain lesions in imaging data pose challenges for supervised detection methods due to the scarcity of annotated samples. To overcome this difficulty, our focus shifts to unsupervised anomaly detection. In this work, we exclusively train the proposed model using healthy data to identify unseen anomalies during testing. This study entails investigating the triplet-based variational autoencoder to simultaneously learn the distribution of healthy brain data and denoising capabilities. Importantly, we rectify a misconception inherent in prior projection-based approaches which relies on the presumption that healthy regions within images would persist unaltered in the reconstructed output. This inadvertently implied a substantial likeness in latent space representations between lesion and lesion-free images. However, this assumption might not hold true, particularly due to the potential significant impact of lesion area intensities on the projection process notably for autoencoders with single information bottleneck. To overcome this limitation, we disentangled metric learning from latent sampling. This approach ensures that both lesion and lesion-free input images are projected into the same distribution, specifically the lesion-free projection. Moreover, we introduce a semantic-guided gated cross skip module to enhance spatial detail retrieval while suppressing anomalies, leveraging robust healthy brain representation semantics exist in the deeper levels of the decoder. We also discovered that incorporating structure similarity index measure as an extra training objective bolsters the capability of anomaly detection for the proposed model.

关键词： Training Measurement Pathology Semantics Noise reduction Brain modeling pattern recognition

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Must Unsupervised Continual Learning Relies on Previous Information?

Must Unsupervised Continual Learning Relies on Previous Info...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Haoyang Cheng Haitao Wen Heqian Qiu Lanxiao Wang Minjian Zhang Hongliang Li University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Open-world recognition has recently gained significant attention owing to its ability to bridge the gap between experimental scenarios and real-world applications. Since continual learning can learn from a sequence of dynamic data streams, it obtains extensive applications in open-world recognition. However, because of the production of data annotation is usually time-consuming and labor-intensive in real-world scenarios, it’s necessary to develop unsupervised continual learning. Recent studies start to investigate unsupervised continual learning (i.e., UCL), but mainly focus on rehearsal and regularization strategies to enhance the anti-forgetting capability of UCL. In practice, rehearsal and regularization are information-dependent, which require information from previous data as supervised signals, e.g., replayed data and previous model. In this paper, we propose an information-free method, Alternate Task Discrimination (ATD), which is a self-supervised pretext task for continuity and improves anti-forgetting capability via encouraging the model to discriminate which data stream current sample is from. The whole process doesn’t rely on any previous information. In order to perform ATD effectively in UCL framework, we design an alternating optimization algorithm where UCL and ATD are optimized respectively. We validate the effectiveness of the proposed method on multiple standard UCL benchmarks, where it obtains considerable improvements compared with baseline methods. In addition, our approach can be used as a plug-in unit, which makes further achievements when collaborated with existing popular UCL methods.

关键词： Continuing education computer vision conferences Production Benchmark testing Data models pattern recognition

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A Novel Technique Combining Image Processing, Plant Development Properties, and the Hungarian Algorithm, to Improve Leaf Detection in Maize

A Novel Technique Combining Image Processing, Plant Developm...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Nazifa Azam Khan Oliver A. S. Lyon Mark Eramian Ian McQuillan Department of Computer Science University of Saskatchewan Saskatoon SK Canada School of Computing Queen’s University Kingston ON Canada

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Manual determination of plant phenotypic properties such as plant architecture, growth, and health is very time consuming and sometimes destructive. Automatic image analysis has become a popular approach. This research aims to identify the position (and number) of leaves from a temporal sequence of high-quality indoor images consisting of multiple views, focussing in particular of images of maize. The procedure used a segmentation on the images, using the convex hull to pick the best view at each time step, followed by a skeletonization of the corresponding image. To remove skeleton spurs, a discrete skeleton evolution pruning process was applied. Pre-existing statistics regarding maize development was incorporated to help differentiate between true leaves and false leaves. Furthermore, for each time step, leaves were matched to those of the previous and next three days using the graph-theoretic Hungarian algorithm. This matching algorithm can be used to both remove false positives, and also to predict true leaves, even if they were completely occluded from the image itself. The algorithm was evaluated using an open dataset consisting of 13 maize plants across 27 days from two different views. The total number of true leaves from the dataset was 1843, and our proposed techniques detects a total of 1690 leaves including 1674 true leaves, and only 16 false leaves, giving a recall of 90.8%, and a precision of 99.0%.

关键词： Skeleton Image segmentation Topology Prediction algorithms Agriculture computer vision

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Systematic Evaluation of Backdoor Data Poisoning Attacks on Image Classifiers

Systematic Evaluation of Backdoor Data Poisoning Attacks on ...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Loc Truong Chace Jones Brian Hutchinson Andrew August Brenda Praggastis Robert Jasper Nicole Nichols Aaron Tuor Western Washington University Pacific Northwest National Laboratory

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Backdoor data poisoning attacks have recently been demonstrated in computer vision research as a potential safety risk for machine learning (ML) systems. Traditional data poisoning attacks manipulate training data to induce unreliability of an ML model, whereas backdoor data poisoning attacks maintain system performance unless the ML model is presented with an input containing an embedded "trigger " that provides a predetermined response advantageous to the adversary. Our work builds upon prior backdoor data-poisoning research for ML image classifiers and systematically assesses different experimental conditions including types of trigger patterns, persistence of trigger patterns during retraining, poisoning strategies, architectures (ResNet-50, NasNet, NasNet-Mobile), datasets (Flowers, CIFAR-10), and potential defensive regularization techniques (Contrastive Loss, Logit Squeezing, Manifold Mixup, Soft-Nearest-Neighbors Loss). Experiments yield four key findings. First, the success rate of backdoor poisoning attacks varies widely, depending on several factors, including model architecture, trigger pattern and regularization technique. Second, we find that poisoned models are hard to detect through performance inspection alone. Third, regularization typically reduces backdoor success rate, although it can have no effect or even slightly increase it, depending on the form of regularization. Finally, backdoors inserted through data poisoning can be rendered ineffective after just a few epochs of additional training on a small set of clean data without affecting the model's performance.

关键词： Data models Training Computational modeling Machine learning Training data computer vision Safety

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AR-CP: Uncertainty-Aware Perception in Adverse Conditions with Conformal Prediction and Augmented Reality For Assisted Driving

AR-CP: Uncertainty-Aware Perception in Adverse Conditions wi...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Achref Doula Max Mühlhäuser Alejandro Sanchez Guinea Technical University of Darmstadt Germany

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Deep learning models are pivotal in enhancing driver assistance systems and improving environmental perception. However, the tendency of neural networks towards overconfident predictions poses a risk of inaccurate predictions, potentially compromising driver safety in adverse conditions. To mitigate this issue, we introduce AR-CP, an uncertainty-aware framework designed to augment driver perception in scenarios characterized by adverse weather and insufficient lighting, through the integration of conformal prediction and augmented reality (AR). Our framework initiates with a conformal prediction step that produces an uncertainty-aware prediction set including potential object classes at a predefined probability level. Subsequently, AR is used to provide a simplified and informative visualization of the closest common parent class of the classes in the prediction set, thereby reducing the likelihood of misinformation. We provide a principled formulation and theoretical analysis of our framework. We evaluate AR-CP on the ROAD dataset, a large dataset containing different difficult situations that induce high uncertainty during prediction time. The results show that our framework outperforms state-of-the-art approaches in providing smaller prediction sets while holding the theoretical guarantees, ensuring an uncertainty-aware prediction, and reducing user confusion. We conduct an immersive user study with 15 participants to investigate the effects of our concept on the quality of perception, situation awareness, and mental load of participants. The results show that our concept facilitates a safer driving experience while holding the mental load low and the situation awareness high.

关键词： Visualization Uncertainty Roads Lighting Reliability theory Safety pattern recognition

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Beyond the Screen: Evaluating Deepfake Detectors under Moiré pattern Effects

Beyond the Screen: Evaluating Deepfake Detectors under Moir...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Razaib Tariq Minji Heo Simon S. Woo Shahroz Tariq Sungkyunkwan University South Korea CSIRO’s Data61 Australia

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The detection of deepfakes is crucial for mitigating the societal impact of falsified video content. Despite the development of various algorithms for this purpose, challenges arise for detectors in real-world scenarios, especially when users capture deepfake content from screens and upload it online or when detectors operate on external devices like smartphones, requiring the capture of potential deepfakes through the camera for evaluation. A significant challenge in these scenarios is the presence of Moiré patterns, which degrade image quality and complicate conventional classification methods, notably deep neural networks (DNNs). However, the impact of Moiré patterns on the effectiveness of deepfake detection systems has not been adequately explored. This study aims to investigate how capturing deepfake videos via digital screen cameras affects the accuracy of detection mechanisms. We introduced the Moiré patterns by capturing the display of a monitor using a smartphone camera and conducted empirical evaluations using four widely recognized datasets: CelebDF, DFD, DFDC, and FF++. We compare the performance of twelve SOTA detectors on deepfake videos captured under the influence of Moiré patterns. Our findings reveal a performance decrease of up to 33.1 and 31.3 percentage points for image- and video-based detectors. Therefore, highlighting the challenges posed by Moiré patterns and other naturally induced artifacts is critical for improving the effectiveness of real-world deepfake detection efforts. To facilitate further research, we will release the Moiré pattern impact version of CelebDF, DFD, DFDC, and FF++ datasets with this paper. Our code is available here: https://***/Razaib-Tariq/deepmoire

关键词： Image quality Deepfakes computer vision conferences Refining Detectors Cameras

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Evaluating and Improving Compositional Text-to-Visual Generation

Evaluating and Improving Compositional Text-to-Visual Genera...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Baiqi Li Zhiqiu Lin Deepak Pathak Jiayao Li Yixin Fei Kewen Wu Xide Xia Pengchuan Zhang Graham Neubig Deva Ramanan CMU Meta

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

While text-to-visual models now produce photo-realistic images and videos, they struggle with compositional text prompts involving attributes, relationships, and higher-order reasoning such as logic and comparison. In this work, we conduct an extensive human study on GenAI-Bench to evaluate the performance of leading image and video generation models in various aspects of compositional text-to-visual generation. We also compare automated evaluation metrics against our collected human ratings and find that VQAScore – a metric measuring the likelihood that a VQA model views an image as accurately depicting the prompt – significantly outperforms previous metrics such as CLIP-Score. In addition, VQAScore can improve generation in a black-box manner (without finetuning) via simply ranking a few (3 to 9) candidate images. Ranking by VQAScore is 2x to 3x more effective than other scoring methods like PickScore and ImageReward at improving human ratings for DALL-E 3 and Stable Diffusion, especially on compositional prompts that require advanced visio-linguistic reasoning. Lastly, we identify areas for improvement in VQAScore, such as addressing fine-grained visual details. Despite mild limitations, VQAScore serves as the best automated metric as well as reward function for improving prompt alignment. We will release over 80,000 human ratings to facilitate scientific benchmarking of both generative models and automated metrics.

关键词： Measurement Visualization Toxicology Closed box Footwear Cognition pattern recognition

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Event Camera Demosaicing via Swin Transformer and Pixel-focus Loss

Event Camera Demosaicing via Swin Transformer and Pixel-focu...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Yunfan Lu Yijie Xu Wenzong Ma Weiyu Guo Hui Xiong AI Thrust Hong Kong University of Science and Technology (Guangzhou)

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Recent research has highlighted improvements in high-quality imaging guided by event cameras, with most of these efforts concentrating on the RGB domain. However, these advancements frequently neglect the unique challenges introduced by the inherent flaws in the sensor design of event cameras in the RAW domain. Specifically, this sensor design results in the partial loss of pixel values, posing new challenges for RAW domain processes like demosaicing. The challenge intensifies as most research in the RAW domain is based on the premise that each pixel contains a value, making the straightforward adaptation of these methods to event camera demosaicing problematic. To end this, we present a Swin-Transformer-based backbone and a pixel-focus loss function for demosaicing with missing pixel values in RAW domain processing. Our core motivation is to refine a general and widely applicable foundational model from the RGB domain for RAW domain processing, thereby broadening the model’s applicability within the entire imaging process. Our method harnesses multi-scale processing and space-to-depth techniques to ensure efficiency and reduce computing complexity. We also proposed the Pixel-focus Loss function for network fine-tuning to improve network convergence based on our discovery of a long-tailed distribution in training loss. Our method has undergone validation on the MIPI Demosaic Challenge dataset, with subsequent analytical experimentation confirming its efficacy. All code and trained models are released here: https://***/yunfanLu/ev-demosaic.

关键词： Training computer vision Computational modeling conferences computer architecture Cameras Transformers

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computer-aided diagnosis system of lung carcinoma using Convolutional Neural Networks

Computer-aided diagnosis system of lung carcinoma using Conv...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Fangjian Han Li Yu Yi Jiang Lensee Bio-Technology Co.Ltd Ningbo China 2nd Xiangya Hospital of Central South University Changsha China

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

With the number of lung cancers' morbidity and mortality is showing a trend of increasing year by year, the demand for pathologists is increasing rapidly, so in this study, we aimed to design a medical pathologically assistant diagnostic system to help pathologists complete diagnostic analysis tasks. A Deep Convolutional Neural Network(DCNN) is adopted to automatically distinguish tumor tissues from normal tissues in digitized hematoxylin and eosin (H&E) stained lung cell pathological slides that collected from The Cancer Genome Atlas (TCGA) and collaborate hospitals, we trained and evaluate WSIs(the whole slide images) captured at 10x magnification and other higher magnification, results show the difference are negligible. Moreover, we also compared the training effect of different models on same level magnification WSIs, the results show that performance of Resnet-18 network model and Resnet-50 network model is nearly consistent. Actually processing time based on Resnet-18 model is shorter than Resnet-50 model, so we don't need deeper network for study. Our system was shown to enormous advantages in accuracy, sensitivity and efficiency, could reduce the burden on pathologists, enable them to spend more time on advanced decision-making tasks, would be widely applied to pathological diagnosis, clinical practice, scientific research and so on.

关键词： Training Lung Cancer Pathology Hospitals Medical diagnostic imaging

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OpenStory: A Large-Scale Open-Domain Dataset for Subject-Driven Visual Storytelling

OpenStory: A Large-Scale Open-Domain Dataset for Subject-Dri...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Zilyu Ye Jinxiu Liu JinJin Cao Zhiyang Chen Ziwei Xuan Mingyuan Zhou Qi Liu Guo-Jun Qi School of Future Technology South China University of Technology Westlake University Foundation Model Research Center CASIA OPPO US Research Center

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Recently, the advancement and evolution of generative AI have been highly compelling. In this paper, we present OpenStory, a large-scale dataset tailored for training subject-focused story visualization models to generate coherent and contextually relevant visual narratives. Addressing the challenges of maintaining subject continuity across frames and capturing compelling narratives, We propose an innovative pipeline that automates the extraction of keyframes from open-domain videos. It ingeniously employs vision-language models to generate descriptive captions, which are then refined by a large language model to ensure narrative flow and coherence. Furthermore, advanced subject masking techniques are applied to isolate and segment the primary subjects. Derived from diverse video sources, including YouTube and existing datasets, OpenStory offers a comprehensive open-domain resource, surpassing prior datasets confined to specific scenarios. With automated captioning instead of manual annotation, high-resolution imagery optimized for subject count per frame, and extensive frame sequences ensuring consistent subjects for temporal modeling, OpenStory establishes itself as an invaluable benchmark. It facilitates advancements in subject-focused story visualization, enabling the training of models capable of comprehending and generating intricate multi-modal narratives from extensive visual and textual inputs.

关键词： Training Visualization computer vision Annotations Large language models Pipelines Manuals

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