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Optimized Uncertainty Estimation for vision Transformers: Enhancing Adversarial Robustness and Performance Using Selective Classification

Optimized Uncertainty Estimation for Vision Transformers: En...

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2023 International conference on High Performance Computing, Network, Storage, and Analysis, SC workshops 2023

作者： Pautsch, Erik Li, John Rizzi, Silvio Thiruvathukal, George K. Pantoja, Maria Loyola University Chicago Chicago United States Ucsd San Diego United States Argonne National Lab Chicago United States CalPoly San Luis Obispo United States

ISBN: (纸本)9798400707858

Deep Learning models often exhibit undue confidence when encountering out-of-distribution (OOD) inputs, misclassifying with high confidence. The ideal outcome, in these cases, would be an "I do not know"verdict. We enhance the trustworthiness of our models through selective classification, allowing the model to abstain from making predictions when facing uncertainty. Rather than a singular prediction, the model offers a prediction distribution, enabling users to gauge the model's trustworthiness and determine the need for human intervention. We assess uncertainty in two baseline models: a Convolutional Neural Network (CNN) and a vision Transformer (ViT). By leveraging these uncertainty values, we minimize errors by refraining from predictions during high uncertainty. Additionally, we evaluate these models across various distributed architectures, including new AI architectures, Cerebras CS-2, and SambaNova SN30. © 2023 ACM.

关键词： Forecasting

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Can we generate real faces from rPPG signals? Probably not

Can we generate real faces from rPPG signals? Probably not

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ieee Annual conference on Pervasive Computing and Communications workshops (PerCom)

作者： Honghan Li Nhi Nguyen Constantino Álvarez Casado Xiaoting Wu Miguel Bordallo López Division of Bioengineering Graduate School of Engineering Science Osaka University Japan Center for Machine Vision and Signal Analysis (CMVS) University of Oulu Finland

The potential of generating authentic human facial images from remote photo-plethysmography (rPPG) signals is a compelling idea, with significant implications for biometric authentication and human-computer interaction. This study explores it by using a large-scale dataset to train a diffusion-based generative model, leveraging rPPG signals extracted from facial videos. The initial training phase yields promising results, with the model demonstrating a capacity to synthesize facial likenesses that closely match the corresponding subjects in the training dataset. However, the performance notably falters during validation with an independent dataset, where a marked divergence between generated and actual faces becomes apparent. A subsequent human perception study corroborates this discrepancy. These observations suggest that rPPG signals alone may not be reliable for accurately generating realistic facial imagery.

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Continual Learning with Transformers for Image Classification

Continual Learning with Transformers for Image Classificatio...

引用

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

作者： Beyza Ermis Giovanni Zappella Martin Wistuba Aditya Rawal Cé dric Archambeau AWS Berlin AWS Santa Clara

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

In many real-world scenarios, data to train machine learning models become available over time. However, neural network models struggle to continually learn new concepts without forgetting what has been learnt in the past. This phenomenon is known as catastrophic forgetting and it is often difficult to prevent due to practical constraints, such as the amount of data that can be stored or the limited computation sources that can be used. Moreover, training large neural networks, such as Transformers, from scratch is very costly and requires a vast amount of training data, which might not be available in the application domain of interest. A recent trend indicates that dynamic architectures based on an expansion of the parameters can reduce catastrophic forgetting efficiently in continual learning, but this needs complex tuning to balance the growing number of parameters and barely share any information across tasks. As a result, they struggle to scale to a large number of tasks without significant overhead. In this paper, we validate in the computer vision domain a recent solution called Adaptive Distillation of Adapters (ADA), which is developed to perform continual learning using pre-trained Transformers and Adapters on text classification tasks. We empirically demonstrate on different classification tasks that this method maintains a good predictive performance without retraining the model or increasing the number of model parameters over the time. Besides it is significantly faster at inference time compared to the state-of-the-art methods.

关键词： Training computer vision Adaptation models Computational modeling Neural networks Training data Predictive models

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Interactive Rotated Object Detection for Novel Class Detection in Remotely Sensed Imagery

Interactive Rotated Object Detection for Novel Class Detecti...

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2025 ieee/CVF Winter conference on Applications of computer vision workshops, WACVW 2025

作者： Burges, Marvin Dias, Philipe Ambrozio Woody, Carson Walters, Sarah Lunga, Dalton Tu Wien Vienna Vienna Austria Oak Ridge National Laboratory Oak RidgeTN United States

ISBN: (纸本)9798331536626

In this paper, we propose IRTR-DETR, an Interactive and Real-Time Rotated DEtection TRansformer that extends IRT-DETR to predict rotated bounding boxes. IRTR-DETR maintains the Human-In-The-Loop (HIL) workflow of IRTDETR but introduces rotation-aware heads for improved detection of objects with arbitrary orientations. Similarly to IRTDETR, IRTR-DETR can be trained with a small labeled sample set in an interactive setting, but we show that it can also be pretrained on related but not identical data-such as a building damage dataset-before being applied to tasks like identifying buildings under construction. We demonstrate the efficacy of our approach on the publicly available Tiny-DOTA and xBD dataset, as well as two study-cases on proprietary datasets of greenhouses and houses under construction ('waffle homes'). Detecting greenhouses is highly relevant in the context of damage assessment, while 'waffle homes' aid understanding typical floorplans and building codes in different areas, both thereby supporting population modeling, emergency response, and policy planning. Our method outperforms the state of the art in interactive rotated object detection on the Tiny-DOTA dataset by 5.7 percent, and improves upon the non interactive RTDETR by 7.85 to 19.39 percent (depending on the number of provided samples) while maintaining its real-time efficiency. © 2025 ieee.

关键词： pattern recognition

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Efficient Multi-Purpose Cross-Attention Based Image Alignment Block for Edge Devices

Efficient Multi-Purpose Cross-Attention Based Image Alignmen...

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

作者： Bahri Batuhan Bilecen Alparslan Fiş ne Mustafa Ayazoğ lu Aselsan Research Ankara Turkey

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Image alignment, also known as image registration, is a critical block used in many computer vision problems. One of the key factors in alignment is efficiency, as inefficient aligners can cause significant overhead to the overall problem. In the literature, there are some blocks that appear to do the alignment operation, although most do not focus on efficiency. Therefore, an image alignment block which can both work in time and/or space and can work on edge devices would be beneficial for almost all networks dealing with multiple images. Given its wide usage and importance, we propose an efficient, cross-attention-based, multipurpose image alignment block (XABA) suitable to work within edge devices. Using cross-attention, we exploit the relationships between features extracted from images. To make cross-attention feasible for real-time image alignment problems and handle large motions, we provide a pyramidal block based cross-attention scheme. This also captures local relationships besides reducing memory requirements and number of operations. Efficient XABA models achieve real-time requirements of running above 20 FPS performance on NVIDIA Jetson Xavier with 30W power consumption compared to other powerful computers. Used as a sub-block in a larger network, XABA also improves multi-image super-resolution network performance in comparison to other alignment methods.

关键词： Performance evaluation computer vision Power demand Image edge detection Superresolution Feature extraction Optical imaging

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MLP-GAN for Brain Vessel Image Segmentation 48

MLP-GAN for Brain Vessel Image Segmentation

引用

48th ieee International conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Xie, Bin Tang, Hao Duan, Bin Cai, Dawen Yan, Yan Illinois Institute of Technology Department of Computer Science ChicagoIL United States ETH Computer Vision Lab Zurich Switzerland University of Michigan Department of Cell and Developmental Biology Ann ArborMI United States

ISBN: (纸本)9781728163277

Brain vessel image segmentation can be used as a promising biomarker for better prevention and treatment of different diseases. One successful approach is to consider the segmentation as an image-to-image translation task and perform a conditional Generative Adversarial Network (cGAN) to learn a transformation between two distributions. In this paper, we present a novel multi-view approach, MLP-GAN, which splits a 3D volumetric brain vessel image into three different dimensional 2D images (i.e., sagittal, coronal, axial) and then feed them into three different 2D cGANs. The proposed MLP-GAN not only alleviates the memory issue which exists in the original 3D neural networks but also retains 3D spatial information. Specifically, we utilize U-Net as the backbone for our generator and redesign the pattern of skip connection integrated with the MLP-Mixer [1] which has attracted lots of attention recently. Our model obtains the ability to capture cross-patch information to learn global information with the MLP-Mixer. Extensive experiments are performed on the public brain vessel dataset [2] that show our MLP-GAN outperforms other state-of-the-art methods. © 2023 ieee.

关键词： Image segmentation

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Multi-Camera Multi-Vehicle Tracking with Domain Generalization and Contextual Constraints

Multi-Camera Multi-Vehicle Tracking with Domain Generalizati...

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

作者： Nhat Minh Chung Huy Dinh-Anh Le Vuong Ai Nguyen Quang Qui-Vinh Nguyen Thong Duy-Minh Nguyen Tin-Trung Thá i Synh Viet-Uyen Ha School of Computer Science and Engineering International University Ho Chi Minh City Vietnam Vietnam National University Ho Chi Minh City Vietnam

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

In this paper, we propose a system for Multi-Camera Multi-Target (MCMT) Vehicle Tracking in Track 1 of AI City Challenge 2022. There are many technical difficulties to the MCMT problem such as a common lack of labeled data in real scenarios, a distortion of vehicle detailed appearances in recording, and ambiguity between highly similar vehicles. Taking those into account, we develop a 3-component MCMT system that exploits vehicle behavior, leverages synthetic data and multiple augmentation techniques, and enforces contextual constraints. Specifically, our system involves a motion-driven vehicle tracker for obtaining robust trajectories, applying MixStyle domain generalization on the TransReID model to exploit as much labeled data as possible, and experimenting with contextual constraints such as our proposed neighbor matching to address ambiguity in terms of vehicle appearances. Overall, our system achieved an IDF1 score of 0.7255.

关键词： Training Tracking conferences Urban areas Distortion Data models Trajectory

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Multi-Head Distillation for Continual Unsupervised Domain Adaptation in Semantic Segmentation

Multi-Head Distillation for Continual Unsupervised Domain Ad...

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

作者： Antoine Saporta Arthur Douillard Tuan-Hung Vu Patrick Pé rez Matthieu Cord Sorbonne Universit&#x00E9Valeo.ai Sorbonne Universit&#x00E9Heuritech Valeo.ai

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Unsupervised Domain Adaptation (UDA) is a transfer learning task which aims at training on an unlabeled target domain by leveraging a labeled source domain. Beyond the traditional scope of UDA with a single source domain and a single target domain, real-world perception systems face a variety of scenarios to handle, from varying lighting conditions to many cities around the world. In this context, UDAs with several domains increase the challenges with the addition of distribution shifts within the different target domains. This work focuses on a novel framework for learning UDA, continuous UDA, in which models operate on multiple target domains discovered sequentially, without access to previous target domains. We propose MuHDi, for Multi-Head Distillation, a method that solves the catastrophic forgetting problem, inherent in continual learning tasks. MuHDi performs distillation at multiple levels from the previous model as well as an auxiliary target-specialist segmentation head. We report both extensive ablation and experiments on challenging multi-target UDA semantic segmentation benchmarks to validate the proposed learning scheme and architecture.

关键词： Training Adaptation models Urban areas Semantics Transfer learning computer architecture Benchmark testing

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Multi-Layer Modeling of Dense Vegetation from Aerial LiDAR Scans

Multi-Layer Modeling of Dense Vegetation from Aerial LiDAR S...

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

作者： Ekaterina Kalinicheva Loic Landrieu Clé ment Mallet Nesrine Chehata ENSG IGN LASTIG Universit&#x00E9 Gustave Eiffel Marne-la-Vallee France INRAE UMR 1202 BIOGECO Universit&#x00E9 de Bordeaux France Bordeaux INP Universit&#x00E9 Bordeaux Montaigne France

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

The analysis of the multi-layer structure of wild forests is an important challenge of automated large-scale forestry. While modern aerial LiDARs offer geometric information across all vegetation layers, most datasets and methods focus only on the segmentation and reconstruction of the top of canopy. We release WildForest3D, which consists of 29 study plots and over 2000 individual trees across 47 000m 2 with dense 3D annotation, along with occupancy and height maps for 3 vegetation layers: ground vegetation, understory, and overstory. We propose a 3D deep network architecture predicting for the first time both 3D point-wise labels and high-resolution layer occupancy rasters simultaneously. This allows us to produce a precise estimation of the thickness of each vegetation layer as well as the corresponding watertight meshes, therefore meeting most forestry purposes. Both the dataset and the model are released in open access: https://***/ekalinicheva/multi_layer_vegetation.

关键词： computer vision Solid modeling Three-dimensional displays Laser radar Annotations Atmospheric modeling Time series analysis

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Transferring Unconditional to Conditional GANs with Hyper-Modulation

Transferring Unconditional to Conditional GANs with Hyper-Mo...

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

作者： Hé ctor Laria Yaxing Wang Joost van de Weijer Bogdan Raducanu Computer Vision Center Barcelona Spain Nankai University China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

GANs have matured in recent years and are able to generate high-resolution, realistic images. However, the computational resources and the data required for the training of high-quality GANs are enormous, and the study of transfer learning of these models is therefore an urgent topic. Many of the available high-quality pretrained GANs are unconditional (like StyleGAN). For many applications, however, conditional GANs are preferable, because they provide more control over the generation process, despite often suffering more training difficulties. Therefore, in this paper, we focus on transferring from high-quality pretrained unconditional GANs to conditional GANs. This requires architectural adaptation of the pretrained GAN to perform the conditioning. To this end, we propose hyper-modulated generative networks that allow for shared and complementary supervision. To prevent the additional weights of the hypernetwork to overfit, with subsequent mode collapse on small target domains, we introduce a self-initialization procedure that does not require any real data to initialize the hypernetwork parameters. To further improve the sample efficiency of the transfer, we apply contrastive learning in the discriminator, which effectively works on very limited batch sizes. In extensive experiments, we validate the efficiency of the hypernetworks, self-initialization and contrastive loss for knowledge transfer on standard benchmarks. Our code is available at https://***/hecoding/Hyper-Modulation.

关键词： Training conferences Transfer learning Modulation Process control Generative adversarial networks Data models

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