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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...

引用

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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Learning to Classify New Foods Incrementally Via Compressed Exemplars

Learning to Classify New Foods Incrementally Via Compressed ...

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

作者： Justin Yang Zhihao Duan Jiangpeng He Fengqing Zhu Elmore School of Electrical and Computer Engineering Purdue University West Lafayette Indiana USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Food image classification systems play a crucial role in health monitoring and diet tracking through image-based dietary assessment techniques. However, existing food recognition systems rely on static datasets characterized by a pre-defined fixed number of food classes. This contrasts drastically with the reality of food consumption, which features constantly changing data. Therefore, food image classification systems should adapt to and manage data that continuously evolves. This is where continual learning plays an important role. A challenge in continual learning is catastrophic forgetting, where ML models tend to discard old knowledge upon learning new information. While memory-replay algorithms have shown promise in mitigating this problem by storing old data as exemplars, they are hampered by the limited capacity of memory buffers, leading to an imbalance between new and previously learned data. To address this, our work explores the use of neural image compression to extend buffer size and enhance data diversity. We introduced the concept of continuously learning a neural compression model to adaptively improve the quality of compressed data and optimize the bitrates per pixel (bpp) to store more exemplars. Our extensive experiments, including evaluations on food-specific datasets including Food-101 and VFN-74, as well as the general dataset ImageNet-100, demonstrate improvements in classification accuracy. This progress is pivotal in advancing more realistic food recognition systems that are capable of adapting to continually evolving data. Moreover, the principles and methodologies we’ve developed hold promise for broader applications, extending their benefits to other domains of continual machine learning systems.

关键词： Continuing education Adaptation models computer vision Image coding conferences Machine learning Data models

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Fast Hardware-Aware Neural Architecture Search

Fast Hardware-Aware Neural Architecture Search

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

作者： Li Lyna Zhang Yuqing Yang Yuhang Jiang Wenwu Zhu Yunxin Liu Microsoft Research Tsinghua University

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Designing accurate and efficient convolutional neural architectures for vast amount of hardware is challenging because hardware designs are complex and diverse. This paper addresses the hardware diversity challenge in Neural Architecture Search (NAS). Unlike previous approaches that apply search algorithms on a small, human-designed search space without considering hardware diversity, we propose HURRICANE that explores the automatic hardware-aware search over a much larger search space and a two-stage search algorithm, to efficiently generate tailored models for different types of hardware. Extensive experiments on ImageNet demonstrate that our algorithm outperforms state-of-the-art hardware-aware NAS methods under the same latency constraint on three types of hardware. Moreover, the discovered architectures achieve much lower latency and higher accuracy than current state-of-the-art efficient models. Remarkably, HURRICANE achieves a 76.67% top-1 accuracy on ImageNet with a inference latency of only 16.5 ms for DSP, which is a 3.47% higher accuracy and a 6.35× inference speedup than FBNet-iPhoneX, respectively. For VPU, we achieve a 0.53% higher top-1 accuracy than Proxyless-mobile with a 1.49× speedup. Even for well-studied mobile CPU, we achieve a 1.63% higher top-1 accuracy than FBNet-iPhoneX with a comparable inference latency. HURRICANE also reduces the training time by 30.4% compared to SPOS.

关键词： Hardware computer architecture Graphics processing units Hurricanes Training Measurement

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Unified Physical-Digital Attack Detection Challenge

Unified Physical-Digital Attack Detection Challenge

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

作者： Haocheng Yuan Ajian Liu Junze Zheng Jun Wan Jiankang Deng Sergio Escalera Hugo Jair Escalante Isabelle Guyon Zhen Lei M.U.S.T MAIS CASIA China SAI UCAS China ICL UK CVC Spain INAOE CINVESTAV Mexico ChaLearn USA CAIR HKISI CAS

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Face Anti-Spoofing (FAS) is crucial to safeguard Face recognition (FR) Systems. In real-world scenarios, FRs are confronted with both physical and digital attacks. However, existing algorithms often address only one type of attack at a time, which poses significant limitations in real-world scenarios where FR systems face hybrid physical-digital threats. To facilitate the research of Unified Attack Detection (UAD) algorithms, a large-scale UniAttackData dataset has been collected. UniAttackData is the largest public dataset for Unified Attack Detection, with a total of 28,706 videos, where each unique identity encompasses all advanced attack types. Based on this dataset, we organized a Unified Physical-Digital Face Attack Detection Challenge to boost the research in Unified Attack Detections. It attracted 136 teams for the development phase, with 13 qualifying for the final round. The results re-verified by the organizing team were used for the final ranking. This paper comprehensively reviews the challenge, detailing the dataset introduction, protocol definition, evaluation criteria, and a summary of published results. Finally, we focus on the detailed analysis of the highest-performing algorithms and offer potential directions for unified physical-digital attack detection inspired by this competition. Challenge Website: https://***/view/face-anti-spoofing-challenge/welcome/challengecvpr2024

关键词： Measurement computer vision Protocols Codes Reviews Face recognition conferences

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GPT as Psychologist? Preliminary Evaluations for GPT-4V on Visual Affective Computing

GPT as Psychologist? Preliminary Evaluations for GPT-4V on V...

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

作者： Hao Lu Xuesong Niu Jiyao Wang Yin Wang Qingyong Hu Jiaqi Tang Yuting Zhang Kaishen Yuan Bin Huang Zitong Yu Dengbo He Shuiguang Deng Hao Chen Yingcong Chen Shiguang Shan The Hong Kong University of Science & Technology (Guangzhou) The Hong Kong University of Science & Technology Beijing Institute for General Artificial Intelligence Zhejiang University Great Bay University Hangzhou Research Institute Beihang University Chinese Academy of Sciences Institute of Computing Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Multimodal large language models (MLLMs) are designed to process and integrate information from multiple sources, such as text, speech, images, and videos. Despite its success in language understanding, it is critical to evaluate the performance of downstream tasks for better human-centric applications. This paper assesses the application of MLLMs with 5 crucial abilities for affective computing, spanning from visual affective tasks and reasoning tasks. The results show that GPT-4V has high accuracy in facial action unit recognition and micro-expression detection while its general facial expression recognition performance is not accurate. We also highlight the challenges of achieving fine-grained micro-expression recognition and the potential for further study and demonstrate the versatility and potential of GPT-4V for handling advanced tasks in emotion recognition and related fields by integrating with task-related agents for more complex tasks, such as heart rate estimation through signal processing. In conclusion, this paper provides valuable insights into the potential applications and challenges of MLLMs in human-centric computing. Our interesting examples are at https://***/Envision-Research/GPT4Affectivity.

关键词： Heart rate Emotion recognition Visualization computer vision Affective computing Accuracy Face recognition

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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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Understanding ReLU Network Robustness Through Test Set Certification Performance

Understanding ReLU Network Robustness Through Test Set Certi...

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

作者： Nicola Franco Jeanette Miriam Lorenz Karsten Roscher Stephan Günnemann Fraunhofer Institute for Cognitive Systems IKS Munich Germany Dept. of Computer Science & Munich Data Science Institute Technical Univ. of Munich Germany

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Neural networks can be vulnerable to small changes in input within their learning distribution, and this vulnerability increases for distributional shifts or input completely outside their training distribution. To ensure networks are used safely, robustness certificates offer formal assurances about the stability of their predictions in a pre-defined range around the input. However, the relationship between correctness and certified robustness remains unclear. In this work, we investigate the unexpected outcomes of verification methods applied to piecewise linear classifiers for clean, perturbed, in- and out-of-distribution samples. In our experiments focused on image classification, we observed that introducing a modest stability margin around the input sample leads to an important reduction in misclassified samples — approximately a 75% decrease — compared to the roughly 11% for samples that are correctly classified. This finding emphasizes the value of formal verification methods as an extra layer of safety, illustrating their effectiveness in enhancing accuracy for data that falls within the distribution. On the other hand, we provide a theoretical demonstration that formal verification methods robustly certify samples sufficiently far from the training distribution. These results are integrated with an experimental analysis and demonstrate their limitations compared to standard out-of-distribution detection methods.

关键词： Accuracy Perturbation methods Neural networks Reliability theory Robustness Stability analysis Safety

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Multi-Objective Hardware Aware Neural Architecture Search using Hardware Cost Diversity

Multi-Objective Hardware Aware Neural Architecture Search us...

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

作者： Nilotpal Sinha Peyman Rostami Abd El Rahman Shabayek Anis Kacem Djamila Aouada SnT University of Luxembourg

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Hardware-aware Neural Architecture Search approaches (HW-NAS) automate the design of deep learning architectures, tailored specifically to a given target hardware platform. Yet, these techniques demand substantial computational resources, primarily due to the expensive process of assessing the performance of identified architectures. To alleviate this problem, a recent direction in the literature has employed representation similarity metric for efficiently evaluating architecture performance. Nonetheless, since it is inherently a single objective method, it requires multiple runs to identify the optimal architecture set satisfying the diverse hardware cost constraints, thereby increasing the search cost. Furthermore, simply converting the single objective into a multi-objective approach results in an under-explored architectural search space. In this study, we propose a Multi-Objective method to address the HW-NAS problem, called MO-HDNAS, to identify the trade-off set of architectures in a single run with low computational cost. This is achieved by optimizing three objectives: maximizing the representation similarity metric, minimizing hardware cost, and maximizing the hardware cost diversity. The third objective, i.e. hardware cost diversity, is used to facilitate a better exploration of the architecture search space. Experimental results demonstrate the effectiveness of our proposed method in efficiently addressing the HW-NAS problem across six edge devices for the image classification task.

关键词： Performance evaluation Costs Image edge detection computer architecture Search problems Hardware Computational efficiency

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Analyzing U-Net Robustness for Single Cell Nucleus Segmentation from Phase Contrast Images

Analyzing U-Net Robustness for Single Cell Nucleus Segmentat...

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

作者： Chenyi Ling Michael Halter Anne Plant Michael Majurski Jeffrey Stinson Joe Chalfoun Software and Systems Division Information Technology Lab NIST Gaithersburg MD Biosystems and Biomaterials Division Material Measurement Lab NIST Gaithersburg MD

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

We quantify the robustness of the semantic segmentation model U-Net, applied to single cell nuclei detection, with respect to the following factors: (1) automated vs manual training annotations, (2) quantity of training data, and (3) microscope image focus. The difficulty of obtaining sufficient volumes of accurate manually annotated training data to create an accurate Convolutional Neural Networks (CNN) model is overcome by the temporary use of fluorescent labels to automate the creation of training datasets using traditional image processing algorithms. The accuracy measurement is computed with respect to manually annotated masks which were also created to evaluate the effectiveness of using automated training set generation via the fluorescent images. The metric to compute the accuracy is the false positive/negative rate of cell nuclei detection. The goal is to maximize the true positive rate while minimizing the false positive rate. We found that automated segmentation of fluorescently labeled nuclei provides viable training data without the need for manual segmentation. A training dataset size of four large stitched images with medium cell density was enough to reach a true positive rate above 88% and a false positive rate below 20%.

关键词： Image segmentation Training computer architecture Microprocessors Microscopy Manuals

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

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

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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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