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Salient Object Detection in Low Contrast Images via Global Convolution and Boundary Refinement

Salient Object Detection in Low Contrast Images via Global C...

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

作者： Nan Mu Xin Xu Xiaolong Zhang School of Computer Science and Technology Wuhan University of Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology China

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Benefit from the powerful features created by using deep learning technology, salient object detection has recently witnessed remarkable progresses. However, it is difficult for a deep network to achieve satisfactory results in low contrast images, due to the low signal to noise ratio property, thus previous deep learning based saliency methods may output maps with ambiguous salient objects and blurred boundaries. To address this issue, we propose a deep fully convolutional framework with a global convolutional module (GCM) and a boundary refinement module (BRM) for saliency detection. Our model drives the network to learn the local and global information to discriminate pixels belonging to salient objects or not, thus can produce more uniform saliency map. To refine the localization and classification performance of the network, five GCMs are integrated to preserve more spatial knowledge of feature maps and enable the densely connections with classifiers. Besides, to propagate saliency information with rich boundary content, a BRM is embed behind each convolutional layer. Experiments on six challenging datasets show that the proposed saliency model achieves state-of-the-art performance compared to nine existing approaches in terms of nine evaluation metrics.

关键词： Convolution Feature extraction Object detection Machine learning Semantics Kernel Visualization

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OC-FakeDect: Classifying Deepfakes Using One-class Variational Autoencoder

OC-FakeDect: Classifying Deepfakes Using One-class Variation...

引用

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

作者： Hasam Khalid Simon S. Woo Computer Science and Engineering Department Sungkyunkwan University South Korea

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

An image forgery method called Deepfakes can cause security and privacy issues by changing the identity of a person in a photo through the replacement of his/her face with a computer-generated image or another person's face. Therefore, a new challenge of detecting Deepfakes arises to protect individuals from potential misuses. Many researchers have proposed various binary-classification based detection approaches to detect deepfakes. However, binary-classification based methods generally require a large amount of both real and fake face images for training, and it is challenging to collect sufficient fake images data in advance. Besides, when new deepfakes generation methods are introduced, little deepfakes data will be available, and the detection performance may be mediocre. To overcome these data scarcity limitations, we formulate deepfakes detection as a one-class anomaly detection problem. We propose OC-FakeDect, which uses a one-class Variational Autoencoder (VAE) to train only on real face images and detects non-real images such as deepfakes by treating them as anomalies. Our preliminary result shows that our one class-based approach can be promising when detecting Deepfakes, achieving a 97.5% accuracy on the NeuralTextures data of the well-known FaceForensics++ benchmark dataset without using any fake images for the training process.

关键词： Face Training Image reconstruction Forensics Streaming media Anomaly detection Benchmark testing

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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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A topological encoding convolutional neural network for segmentation of 3D multiphoton images of brain vasculature using persistent homology

A topological encoding convolutional neural network for segm...

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

作者： Mohammad Haft-Javaherian Martin Villiger Chris B. Schaffer Nozomi Nishimura Polina Golland Brett E. Bouma Wellman Center for Photomedicine Massachusetts General Hospital Harvard Medical School Boston MA USA Meinig School of Biomedical Engineering Cornell University Ithaca NY USA Computer Science and Artificial Intelligence Laboratory (CSAIL) Massachusetts Institute of Technology Cambridge MA USA

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

The clinical evidence suggests that cognitive disorders are associated with vasculature dysfunction and decreased blood flow in the brain. Hence, a functional understanding of the linkage between brain functionality and the vascular network is essential. However, methods to systematically and quantitatively describe and compare structures as complex as brain blood vessels are lacking. 3D imaging modalities such as multiphoton microscopy enables researchers to capture the network of brain vasculature with high spatial resolutions. Nonetheless, image processing and inference are some of the bottlenecks for biomedical research involving imaging, and any advancement in this area impacts many research groups. Here, we propose a topological encoding convolutional neural network based on persistent homology to segment 3D multiphoton images of brain vasculature. We demonstrate that our model outperforms state-of-the-art models in terms of the Dice coefficient and it is comparable in terms of other metrics such as sensitivity. Additionally, the topological characteristics of our model's segmentation results mimic manual ground truth. Our code and model are open source at https://***/mhaft/DeepVess.

关键词： Image segmentation Three-dimensional displays Biological system modeling computer vision computer architecture Biomedical imaging

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Continual Diffusion with STAMINA: STack-And-Mask INcremental Adapters

Continual Diffusion with STAMINA: STack-And-Mask INcremental...

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

作者： James Seale Smith Yen-Chang Hsu Zsolt Kira Yilin Shen Hongxia Jin Samsung Research America Georgia Institute of Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Recent work has demonstrated a remarkable ability to customize text-to-image diffusion models to multiple, fine-grained concepts in a sequential (i.e., continual) manner while only providing a few example images for each concept. This setting is known as continual diffusion. Here, we ask the question: Can we scale these methods to longer concept sequences without forgetting? Although prior work mitigates the forgetting of previously learned concepts, we show that its capacity to learn new tasks reaches saturation over longer sequences. We address this challenge by introducing a novel method, STack-And-Mask INcremental Adapters (STAMINA), which is composed of low-ranked attention-masked adapters and customized MLP tokens. STAMINA is designed to enhance the robust fine-tuning properties of LoRA for sequential concept learning via learnable hard-attention masks parameterized with low rank MLPs, enabling precise, scalable learning via sparse adaptation. Notably, all introduced trainable parameters can be folded back into the model after training, inducing no additional inference parameter costs. We show that STAMINA outperforms the prior SOTA for the setting of text-to-image continual customization on a 50-concept benchmark composed of landmarks and human faces, with no stored replay data. Additionally, we extended our method to the setting of continual learning for image classification, demonstrating that our gains also translate to state-of-the-art performance in this standard benchmark.

关键词： Training Continuing education Costs Text to image Benchmark testing Diffusion models pattern recognition

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TherISuRNet - A Computationally Efficient Thermal Image Super-Resolution Network

TherISuRNet - A Computationally Efficient Thermal Image Supe...

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

作者： Vishal Chudasama Heena Patel Kalpesh Prajapati Kishor Upla Raghavendra Ramachandra Kiran Raja Christoph Busch Sardar Vallabhbhai National Institute of Technology (SVNIT) Surat India Norwegian University of Science and Technology (NTNU) Gjøvik Norway

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Human perception is limited to perceive the objects beyond the range of visible wavelengths in the Electromagnetic (EM) spectrum. This prevents them to recognize objects in different conditions such as poor illumination or severe weather (e.g., under fog or smoke). The technological advancement in thermographic imaging enables the visualization of objects beyond visible range which enables it's use in many applications such as military, medical, agriculture, etc. However, due to the hardware constraints, the thermal cameras are limited with poor spatial resolution when compared to similar visible range RGB cameras. In this paper, we propose a Super-Resolution (SR) of thermal images using a deep neural network architecture which we refer to as TherISuRNet. We use progressive upscaling strategy with asymmetrical residual learning in the network which is computationally efficient for different upscaling factors such as ×2, ×3 and ×4. The proposed architecture consists of different modules for low and high-frequency feature extraction along with upsampling blocks. The effectiveness of the proposed architecture in TherISuRNet is verified by evaluating it with different datasets. The obtained results indicate superior results as compared to other state-of-the-art SR methods.

关键词： Cameras Spatial resolution Feature extraction Training computer architecture

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When Person Re-identification Meets Changing Clothes

When Person Re-identification Meets Changing Clothes

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

作者： Fangbin Wan Yang Wu Xuelin Qian Yixiong Chen Yanwei Fu School of Data Science Fudan University Kyoto University School of Computer Science Fudan University

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Person re-identification (ReID) is now an active research topic for AI-based video surveillance applications such as specific person search, but the practical issue that the target person(s) may change clothes (clothes inconsistency problem) has been overlooked for long. For the first time, this paper systematically studies this problem. We first overcome the difficulty of lack of suitable dataset, by collecting a small yet representative real dataset for testing whilst building a large realistic synthetic dataset for training and deeper studies. Facilitated by our new datasets, we are able to conduct various interesting new experiments for studying the influence of clothes inconsistency. We find that changing clothes makes ReID a much harder problem in the sense of bringing difficulties to learning effective representations and also challenges the generalization ability of previous ReID models to identify persons with unseen (new) clothes. Representative existing ReID models are adopted to show informative results on such a challenging setting, and we also provide some preliminary efforts on improving the robustness of existing models on handling the clothes inconsistency issue in the data. We believe that this study can be inspiring and helpful for encouraging more researches in this direction. The dataset is available on the project website: https://***/***.

关键词： Cameras Benchmark testing Lighting Buildings Games Data models Biological system modeling

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Fourier Prior-Based Two-Stage Architecture for Image Restoration

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

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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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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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Unsupervised domain adaptation and super resolution on drone images for autonomous dry herbage biomass estimation

Unsupervised domain adaptation and super resolution on drone...

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

作者： Paul Albert Mohamed Saadeldin Badri Narayanan Brian Mac Namee Deirdre Hennessy Noel E. O’ Connor Kevin McGuinness School of Electronic Engineering Dublin City University Insight Centre for Data Analytics VistaMilk School of Computer Science University College Dublin Teagasc

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Herbage mass yield and composition estimation is an important tool for dairy farmers to ensure an adequate supply of high quality herbage for grazing and subsequently milk production. By accurately estimating herbage mass and composition, targeted nitrogen fertiliser application strategies can be deployed to improve localised regions in a herbage field, effectively reducing the negative impacts of over-fertilization on biodiversity and the environment. In this context, deep learning algorithms offer a tempting alternative to the usual means of sward composition estimation, which involves the destructive process of cutting a sample from the herbage field and sorting by hand all plant species in the herbage. The process is labour intensive and time consuming and so not utilised by farmers. Deep learning has been successfully applied in this context on images collected by high-resolution cameras on the ground. Moving the deep learning solution to drone imaging, however, has the potential to further improve the herbage mass yield and composition estimation task by extending the ground-level estimation to the large surfaces occupied by fields/paddocks. Drone images come at the cost of lower resolution views of the fields taken from a high altitude and requires further herbage ground-truth collection from the large surfaces covered by drone images. This paper proposes to transfer knowledge learned on ground-level images to raw drone images in an unsupervised manner. To do so, we use unpaired image style translation to enhance the resolution of drone images by a factor of eight and modify them to appear closer to their ground-level counterparts. We then use the enhanced drone images to train a semi-supervised algorithm that uses ground-truthed, ground-level images as the labelled data together with a large amount of unlabeled drone images. We validate our results on a small held-out drone image test set to show the validity of our approach, which opens the way for automate

关键词： Deep learning computer vision Image resolution Costs Estimation Prediction algorithms Cameras

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