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COOD: Combined out-of-distribution detection using multiple measures for anomaly & novel class detection in large-scale hierarchical classification

COOD: Combined out-of-distribution detection using multiple ...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Laurens E. Hogeweg Rajesh Gangireddy Django Brunink Vincent J. Kalkman Ludo Cornelissen Jacob W. Kamminga Intel Naturalis Biodiversity Center University of Twente

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

High-performing out-of-distribution (OOD) detection, both anomaly and novel class, is an important prerequisite for the practical use of classification models. In this paper we focus on the species recognition task in images, concerned with large databases, a large number of fine-grained hierarchical classes, severe class imbalance, and varying image quality. We propose a framework for combining individual OOD measures into one combined OOD (COOD) measure using a supervised model. The individual measures are several existing state-of-the-art measures and several novel OOD measures developed with novel class detection and hierarchical class structure in mind. COOD was extensively evaluated on three large-scale (500k+ images) biodiversity datasets in the context of anomaly and novel class detection. We show that COOD outperforms individual, including state-of-the-art, OOD measures by a large margin in terms of TPR@1%FPR in the majority of experiments, e.g., improving detecting ImageNet images (OOD) from 54.3% to 83.3% for the iNaturalist 2018 dataset. SHAP (feature contribution) analysis shows that different individual OOD measures are essential for various tasks, indicating that multiple OOD measures and combinations are needed to generalize. Additionally, we show that explicitly considering ID images that are incorrectly classified for the original (species) recognition task is important for constructing high-performing OOD detection methods and for practical applicability. The framework can easily be extended or adapted to other tasks and media modalities.

关键词： Image quality computer vision Image recognition Databases conferences Biological system modeling Media

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Robust Data Augmentation and Ensemble Method for Object Detection in Fisheye Camera Images

Robust Data Augmentation and Ensemble Method for Object Dete...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Viet Hung Duong Duc Quyen Nguyen Thien Van Luong Huan Vu Tien Cuong Nguyen VNPT Group VNPT AI Hanoi Vietnam Faculty of Computer Science Phenikaa University Hanoi Vietnam University of Transport and Communications Hanoi Vietnam

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In recent years, traffic surveillance systems have begun leveraging fisheye lenses to minimize the requisite number of cameras for comprehensive coverage of streets and intersections. However, as fisheye images have large radial distortion, they pose new challenges to standard object detection algorithms. In this study, we propose a robust object detection method in traffic scenarios using fisheye cameras. Specifically, we develop a novel data augmentation method, which is applied to VisDrone dataset. Note that we select this dataset for augmentation, since it bears resemblances to the Fisheye8K dataset. Furthermore, we leverage pseudo labels generated by a pre-trained object detection model based on the Fisheye8K and original VisDrone dataset to further enrich the training data. Finally, we utilize various state-of-the-art object detection models trained with different combinations of the proposed augmented data, which are then combined with robust ensemble techniques to further enhance the overall object detection performance. As a result, our proposed method achieves a final F1 score of 64.06% on the 2024 AI City Challenge - Track 4 and ranks first among the competing teams.

关键词： Surveillance Urban areas Training data Object detection Traffic control Data augmentation Cameras

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Fourier Prior-Based Two-Stage Architecture for Image Restoration

Fourier Prior-Based Two-Stage Architecture for Image Restora...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Hemkant Nehete Amit Monga Partha Kaushik Brajesh Kumar Kaushik Indian Institute of Technology Roorkee India

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This work presents a novel two stage architecture designed to enhance degraded images affected by environmental factors such as haze, blur, fog, and rain. Despite the dominance of deep Convolutional Neural Networks (CNNs) and Transformers in single image restoration tasks, existing methods neglect the intrinsic priors for physical properties of degradation. To enhance the generalization ability of image restoration models, we propose Fourier prior based on a key observation that substituting the Fourier amplitude of degraded images with that of clean images effectively mitigates degradation. Therefore, amplitude contains degradation information, while the phase retains background structures. Consequently, a two-stage model is proposed, that consists of Amplitude Refinement Unit (ARU) and the Phase Refinement Unit (PRU), that separately restore both amplitude and phase information, respectively. ARU and PRU leverage a CNN-Transformer-based architecture to extract local and global features, overcoming computational constraints posed by large image sizes in Transformers. Additionally, a multi-scale approach in ARU refines amplitude features at coarse and fine levels, improving restoration efficiency. Experimental results across multiple image restoration tasks, like image deraining, dehazing, and low-light enhancement, indicate that the proposed architecture improved the performance in terms of PSNR, SSIM, and computational efficiency compared to state-of-the-art Transformer approaches.

关键词： Degradation Rain Computational modeling Frequency-domain analysis computer architecture Transformers Feature extraction

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From 2D Portraits to 3D Realities: Advancing GAN Inversion for Enhanced Image Synthesis

From 2D Portraits to 3D Realities: Advancing GAN Inversion f...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Wonseok Oh Youngjoo Jo Electrical and Computer Engineering University of Michigan ETRI

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Image synthesis using StyleGAN has shown remarkable results in 2D portrait image generation. The works of the GAN inversion to manipulate the real image using StyleGAN latent space also show remarkable achievements. 2D GAN inversion has successfully manipulated global attributes such as facial expressions and gender. However, preserving the hairstyle and identity was difficult according to the pose change. We introduce the 3D GAN inversion encoder to make a high-resolution 3D image based on the Geometry Aware 3D Generative Adversarial Network, known as EG3D, which allows explicit control over the pose of the real image subject with multi-view consistency. Our network projects the single 2D portrait images to novel latent space for 3D GAN inversion for the tri-plane of EG3D. We also present multi-view cycle loss, which aims to increase multi-view consistency. By leveraging the new latent space and loss for 3D GAN inversion, our network can successfully convert 2D portrait images into 3D fast.

关键词： Visualization Three-dimensional displays Image synthesis Computational modeling Semantics Aerospace electronics Generative adversarial networks

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Classification of 2D Ultrasound Breast Cancer Images with Deep Learning

Classification of 2D Ultrasound Breast Cancer Images with De...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Jack Ellis Kofi Appiah Emmanuel Amankwaa-Frempong Sze Chai Kwok Department of Computer Science University of York York UK Sweden-Ghana Medical Center Accra Ghana Division of Natural & Applied Sciences Duke Kunshan University China Affiliated Mental Health Center School of Psychology & Cognitive Sci. ECNU China Shanghai Changning Mental Health Center Shanghai China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Breast cancer is the second most prevalent form of cancer and is the “leading cause of most cancer-related deaths in women”. Most women living in low- and middle-income countries (LMIC) have limited access to the existing poor health systems, restricted access to treatment facilities, and in general lack of breast cancer screening programmes. The likelihood of women living in LMIC attending a health facility with advanced-stage breast cancer is very high and the chances of them being able to afford treatment at that stage, even if the treatment is available, is very low. In this work, we evaluate the capabilities of deep learning as a classification tool with the aim of detecting cancerous ultrasound breast images. We aim to deploy a simple classifier on a mobile device with an inexpensive handheld ultrasound imaging system to pick up breast cancer cases that will need medical attention. We demonstrate in this work that with minimal ultrasound images, a de novo system trained from scratch can achieve accuracy of close to 64% and about 78% when the same model is pre-trained.

关键词： Deep learning Ultrasonic imaging Accuracy Computational modeling Transfer learning Breast cancer Mobile handsets

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Probing Conceptual Understanding of Large Visual-Language Models

Probing Conceptual Understanding of Large Visual-Language Mo...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Madeline Schiappa Raiyaan Abdullah Shehreen Azad Jared Claypoole Michael Cogswell Ajay Divakaran Yogesh Rawat Center for Research in Computer Vision University of Central Florida SRI International

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In recent years large visual-language (V+L) models have achieved great success in various downstream tasks. However, it is not well studied whether these models have a conceptual grasp of the visual content. In this work we focus on conceptual understanding of these large V+L models. To facilitate this study, we propose novel benchmarking datasets for probing three different aspects of content understanding, 1) relations, 2) composition, and 3) context. Our probes are grounded in cognitive science and help determine if a V+L model can, for example, determine if snow garnished with a man is implausible, or if it can identify beach furniture by knowing it is located on a beach. We experimented with many recent state-of-the-art V+L models and observe that these models mostly fail to demonstrate a conceptual understanding. This study reveals several interesting insights such as that cross-attention helps learning conceptual understanding, and that CNNs are better with texture and patterns, while Transformers are better at color and shape. We further utilize some of these insights and investigate a simple finetuning technique that rewards the three conceptual understanding measures with promising initial results. The proposed benchmarks will drive the community to delve deeper into conceptual understanding and foster advancements in the capabilities of large V+L models. The code and dataset is available at: https://***/vlm-robustness

关键词： Training Visualization Shape Snow Color Benchmark testing Transformers

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Event-Based Eye Tracking. AIS 2024 Challenge Survey

Event-Based Eye Tracking. AIS 2024 Challenge Survey

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Zuowen Wang Chang Gao Zongwei Wu Marcos V. Conde Radu Timofte Shih-Chii Liu Qinyu Chen Zheng-jun Zha Wei Zhai Han Han Bohao Liao Yuliang Wu Zengyu Wan Zhong Wang Yang Cao Ganchao Tan Jinze Chen Yan Ru Pei Sasskia Brüers Sébastien Crouzet Douglas McLelland Oliver Coenen Baoheng Zhang Yizhao Gao Jingyuan Li Hayden Kwok-Hay So Philippe Bich Chiara Boretti Luciano Prono Mircea Lică David Dinucu-Jianu Cătălin Grîu Xiaopeng Lin Hongwei Ren Bojun Cheng Xinan Zhang Valentin Vial Anthony Yezzi James Tsai Institute of Neuroinformatics University of Zurich and ETH Zurich Delft University of Technology University of Würzburg Leiden University University of Science and Technology of China Brainchip Inc The University of Hong Kong Polytechnic of Turin The Hong Kong University of Science and Technology (Guangzhou) Georgia Institute of Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This survey reviews the AIS 2024 Event-Based Eye Tracking (EET) Challenge. The task of the challenge focuses on processing eye movement recorded with event cameras and predicting the pupil center of the eye. The challenge emphasizes efficient eye tracking with event cameras to achieve good task accuracy and efficiency trade-off. During the challenge period, 38 participants registered for the Kaggle competition, and 8 teams submitted a challenge factsheet. The novel and diverse methods from the submitted factsheets are reviewed and analyzed in this survey to advance future event-based eye tracking research.

关键词： Surveys computer vision Visualization Reviews Gaze tracking Cameras Transformers

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Is Synthetic Data all We Need? Benchmarking the Robustness of Models Trained with Synthetic Images

Is Synthetic Data all We Need? Benchmarking the Robustness o...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Krishnakant Singh Thanush Navaratnam Jannik Holmer Simone Schaub-Meyer Stefan Roth Department of Computer Science TU Darmstadt Hessian.AI

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

A long-standing challenge in developing machine learning approaches has been the lack of high-quality labeled data. Recently, models trained with purely synthetic data, here termed synthetic clones, generated using large-scale pre-trained diffusion models have shown promising results in overcoming this annotation bottleneck. As these synthetic clone models progress, they are likely to be deployed in challenging real-world settings, yet their suitability remains understudied. Our work addresses this gap by providing the first benchmark for three classes of synthetic clone models, namely supervised, self-supervised, and multi-modal ones, across a range of robustness measures. We show that existing synthetic self-supervised and multi-modal clones are comparable to or outperform state-of-the-art real-image baselines for a range of robustness metrics – shape bias, background bias, calibration, etc. However, we also find that synthetic clones are much more susceptible to adversarial and real-world noise than models trained with real data. To address this, we find that combining both real and synthetic data further increases the robustness, and that the choice of prompt used for generating synthetic images plays an important part in the robustness of synthetic clones.

关键词： Measurement Analytical models Shape Noise Cloning Benchmark testing Robustness

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Exploring the Role of Audio in Video Captioning

Exploring the Role of Audio in Video Captioning

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Yuhan Shen Linjie Yang Longyin Wen Haichao Yu Ehsan Elhamifar Heng Wang Northeastern University ByteDance

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Recent focus in video captioning has been on designing architectures that can consume both video and text modalities, and using large-scale video datasets with text transcripts for pre-training, such as HowTo100M. Though these approaches have achieved significant improvement, the audio modality is often ignored in video captioning. In this work, we present an audio-visual framework, which aims to fully exploit the potential of the audio modality for captioning. Instead of relying on text transcripts extracted via automatic speech recognition (ASR), we argue that learning with raw audio signals can be more beneficial, as audio has additional information including acoustic events, speaker identity, etc. Our contributions are twofold. First, we observed that the model overspecializes to the audio modality when pre-training with both video and audio modality, since the ground truth (i.e., text transcripts) can be solely predicted using audio. We proposed a Modality Balanced Pre-training (MBP) loss to mitigate this issue and significantly improve the performance on downstream tasks. Second, we slice and dice different design choices of the cross-modal module, which may become an information bottleneck and generate inferior results. We proposed new local-global fusion mechanisms to improve information exchange across audio and video. We demonstrate significant improvements by leveraging the audio modality on four datasets, and even outperform the state of the art on some metrics without relying on the text modality as the input.

关键词： Measurement computer vision Computational modeling conferences Predictive models Acoustics Data mining

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Deepfake Catcher: Can a Simple Fusion be Effective and Outperform Complex DNNs?

Deepfake Catcher: Can a Simple Fusion be Effective and Outpe...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Akshay Agarwal Nalini Ratha IISER Bhopal India University at Buffalo USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Despite having completely different configurations, deep learning architectures learn a specific set of features that are common across architectures. For example, the initial few layers learn the low-level edge features from the images. Based on this fact, in this research, we have showcased the potential of deep neural network fusion for simple and effective deepfake detection. The advantage of building an architecture in such a manner is to build a low-power-consuming and accurate defense that can be deployed on mobile devices. To utilize the pre-trained knowledge and obtain downstream task-specific knowledge, we have identified a breakpoint in different networks and divided the obtained knowledge of a network into fixed and adaptive information. We have kept the fixed knowledge intact while modifying the adaptive knowledge along with entirely new knowledge for the deepfake detection task. In the end, the decision of multiple deep architectures trained based on their breakpoint are combined for improved performance. Extensive comparisons performed with existing state-of-the-art architectures demonstrate the effectiveness of the proposed deepfake detection algorithm. The proposed algorithm not only surpasses the existing state-of-the-art (SOTA) algorithms but also needs low computational power. We have further challenged the proposed algorithm by evaluating it by collecting real-world deepfake images.

关键词： Knowledge engineering Image quality Deepfakes Social networking (online) Architecture Deep architecture computer architecture

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