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Forward-Forward Algorithm for Hyperspectral Image Classification

Forward-Forward Algorithm for Hyperspectral Image Classifica...

引用

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

作者： Abel A. Reyes-Angulo Sidike Paheding Michigan Technological University Houghton MI Fairfield University Fairfield CT

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The back-propagation algorithm has long been the de-facto standard in optimizing weights and biases in neural networks, particularly in cutting-edge deep-learning models. Its widespread adoption in fields like natural language processing, computer vision, and remote sensing has revolutionized automation in various tasks. The popularity of back-propagation stems from its ability to achieve outstanding performance in tasks such as classification, detection, and segmentation. Nevertheless, back-propagation is not without its limitations, encompassing sensitivity to initial conditions, vanishing gradients, overfitting, and computational complexity. The recent introduction of a forward-forward algorithm (FFA), which computes local goodness functions to optimize network parameters, alleviates the dependence on substantial computational resources and the constant need for architectural scaling. This study investigates the application of FFA for hyperspectral image classification. Experimental results and comparative analysis are provided with the use of the traditional back-propagation algorithm. Preliminary results show the potential behind FFA and its promises.

关键词： Backpropagation Training computer vision Sensitivity Feature extraction Classification algorithms pattern recognition

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SkipPLUS: Skip the First Few Layers to Better Explain vision Transformers

SkipPLUS: Skip the First Few Layers to Better Explain Vision...

引用

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

作者： Faridoun Mehri Mohsen Fayyaz Mahdieh Soleymani Baghshah Mohammad Taher Pilehvar Sharif University of Technology Iran University of Tehran Iran Cardiff University UK

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Despite their remarkable performance, the explainability of vision Transformers (ViTs) remains a challenge. While forward attention-based token attribution techniques have become popular in text processing, their suitability for ViTs hasn't been extensively explored. In this paper, we compare these methods against state-of-the-art input attribution methods from the vision literature, revealing their limitations due to improper aggregation of information across layers. To address this, we introduce two general techniques, PLUS and SkipPLUS, that can be composed with any input attribution method to more effectively aggregate information across layers while handling noisy layers. Through comprehensive and quantitative evaluations of faithfulness and human interpretability on a variety of ViT architectures and datasets, we demonstrate the effectiveness of PLUS and SkipPLUS, establishing a new state-of-the-art in white-box token attribution. We conclude with a comparative analysis highlighting the strengths and weaknesses of the best versions of all the studied methods. The code used in this paper is freely available at https://***/NightMachinery/SkipPLUS-CVPR-2024.

关键词： Training computer vision Animals Aggregates conferences Transformers pattern recognition

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Does Image Anonymization Impact computer vision Training?

Does Image Anonymization Impact Computer Vision Training?

引用

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

作者： Håkon Hukkelås Frank Lindseth Deparment of Computer Science Norwegian University of Science and Technology

Image anonymization is widely adapted in practice to comply with privacy regulations in many regions. However, anonymization often degrades the quality of the data, reducing its utility for computer vision development. In this paper, we investigate the impact of image anonymization for training computer vision models on key computer vision tasks (detection, instance segmentation, and pose estimation). Specifically, we benchmark the recognition drop on common detection datasets, where we evaluate both traditional and realistic anonymization for faces and full bodies. Our comprehensive experiments reflect that traditional image anonymization substantially impacts final model performance, particularly when anonymizing the full body. Furthermore, we find that realistic anonymization can mitigate this decrease in performance, where our experiments reflect a minimal performance drop for face anonymization. Our study demonstrates that realistic anonymization can enable privacy-preserving computer vision development with minimal performance degradation across a range of important computer vision benchmarks.

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On Training Sketch Recognizers for New Domains

On Training Sketch Recognizers for New Domains

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Yesilbek, Kemal Tugrul Sezgin, T. Metin Beat Res BV Amsterdam Netherlands Koc Univ Istanbul Turkey

ISBN: (纸本)9781665448994

Sketch recognition algorithms are engineered and evaluated using publicly available datasets contributed by the sketch recognition community over the years. While existing datasets contain sketches of a limited set of generic objects, each new domain inevitably requires collecting new data for training domain specific recognizers. This gives rise to two fundamental concerns: First, will the data collection protocol yield ecologically valid data? Second, will the amount of collected data suffice to train sufficiently accurate classifiers? In this paper, we draw attention to these two concerns. We show that the ecological validity of the data collection protocol and the ability to accommodate small datasets are significant factors impacting recognizer accuracy in realistic scenarios. More specifically, using sketch-based gaming as a use case, we show that deep learning methods, as well as more traditional methods, suffer significantly from dataset shift. Furthermore, we demonstrate that in realistic scenarios where data is scarce and expensive, standard measures taken for adapting deep learners to small datasets fall short of comparing favorably with alternatives. Although transfer learning, and extensive data augmentation help deep learners, they still perform significantly worse compared to standard setups (e.g., SVMs and GBMs with standard feature representations). We pose learning from small datasets as a key problem for the deep sketch recognition field, one which has been ignored in the bulk of the existing literature.

关键词： Training Deep learning computer vision Protocols conferences Transfer learning Data collection

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NeRF-Editing: Geometry Editing of Neural Radiance Fields

NeRF-Editing: Geometry Editing of Neural Radiance Fields

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Yuan, Yu-Jie Sun, Yang-Tian Lai, Yu-Kun Ma, Yuewen Jia, Rongfei Gao, Lin Chinese Acad Sci Inst Comp Technol Beijing Key Lab Mobile Comp & Pervas Device Beijing Peoples R China Univ Chinese Acad Sci Sch Comp & Control Engn Beijing Peoples R China Cardiff Univ Sch Comp Sci & Informat Cardiff Wales Alibaba Grp Hangzhou Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Implicit neural rendering, especially Neural Radiance Field (NeRF), has shown great potential in novel view synthesis of a scene. However, current NeRF-based methods cannot enable users to perform user-controlled shape deformation in the scene. While existing works have proposed some approaches to modify the radiance field according to the user's constraints, the modification is limited to color editing or object translation and rotation. In this paper, we propose a method that allows users to perform controllable shape deformation on the implicit representation of the scene, and synthesizes the novel view images of the edited scene without re-training the network. Specifically, we establish a correspondence between the extracted explicit mesh representation and the implicit neural representation of the target scene. Users can first utilize well-developed mesh-based deformation methods to deform the mesh representation of the scene. Our method then utilizes user edits from the mesh representation to bend the camera rays by introducing a tetrahedra mesh as a proxy, obtaining the rendering results of the edited scene. Extensive experiments demonstrate that our framework can achieve ideal editing results not only on synthetic data, but also on real scenes captured by users.

关键词： Geometry Deep learning computer vision Shape Image color analysis Neural networks Rendering (computer graphics)

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Spike timing-based unsupervised learning of orientation, disparity, and motion representations in a spiking neural network

Spike timing-based unsupervised learning of orientation, dis...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Barbier, Thomas Teuliere, Celine Triesch, Jochen Univ Clermont Auvergne Inst Pascal SIGMA Clermont CNRS F-63000 Clermont Ferrand France Frankfurt Inst Adv Studies Frankfurt Germany

ISBN: (纸本)9781665448994

Neuromorphic vision sensors present unique advantages over their frame based counterparts. However, unsupervised learning of efficient visual representations from their asynchronous output is still a challenge, requiring a rethinking of traditional image and video processing methods. Here we present a network of leaky integrate and fire neurons that learns representations similar to those of simple and complex cells in the primary visual cortex of mammals from the input of two event-based vision sensors. Through the combination of spike timing-dependent plasticity and homeostatic mechanisms, the network learns visual feature detectors for orientation, disparity, and motion in a fully unsupervised fashion. We validate our approach on a mobile robotic platform.

关键词： Visualization Neuromorphics conferences Neurons Detectors vision sensors Robot sensing systems

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N-ROD: a Neuromorphic Dataset for Synthetic-to-Real Domain Adaptation

N-ROD: a Neuromorphic Dataset for Synthetic-to-Real Domain A...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Cannici, Marco Plizzari, Chiara Planamente, Mirco Ciccone, Marco Bottino, Andrea Caputo, Barbara Matteucci, Matteo Politecn Milan Milan Italy Politecn Torino Turin Italy Ist Italiano Tecnol Genoa Italy

ISBN: (纸本)9781665448994

Event cameras are novel neuromorphic sensors, which asynchronously capture pixel-level intensity changes in the form of "events". Event simulation from existing RGB datasets is commonly used to overcome the need of large amount of annotated data, which lacks due to the novelty of the event sensors. In this context, the possibility of using event simulation in synthetic scenarios, where data generation is not limited to pre-existing datasets, is to date still unexplored. In this work, we analyze the synth-to-real domain shift in event data, i.e., the gap arising between simulated events obtained from synthetic renderings and those captured with a real camera on real images. To this purpose, we extend to the event modality the popular RGB-D Object Dataset (ROD), which already comes with its synthetic version (SynROD). The resulting Neuromorphic ROD dataset (N-ROD) is the first to enable a synth-to-real analysis on event data, showing the effectiveness of Domain Adaptation techniques in reducing the synth-to-real shift. Moreover, through extensive experiments on multi-modal RGBE data, we show that events can be effectively combined with conventional visual information, encouraging further research in this area. The N-ROD dataset is available at https://***/home/.

关键词： Visualization computer vision Neuromorphics conferences Cameras Rendering (computer graphics) Data models

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NTIRE 2024 Challenge on Stereo Image Super-Resolution: Methods and Results

NTIRE 2024 Challenge on Stereo Image Super-Resolution: Metho...

引用

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

作者： Longguang Wang Yulan Guo Juncheng Li Hongda Liu Yang Zhao Yingqian Wang Zhi Jin Shuhang Gu Radu Timofte

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper summarizes the 3rd NTIRE challenge on stereo image super-resolution (SR) with a focus on new solutions and results. The task of this challenge is to super-resolve a low-resolution stereo image pair to a high-resolution one with a magnification factor of × 4 under a limited computational budget. Compared with single image SR, the major challenge of this challenge lies in how to exploit additional information in another viewpoint and how to maintain stereo consistency in the results. This challenge has 2 tracks, including one track on bicubic degradation and one track on real degradations. In total, 108 and 70 participants were successfully registered for each track, respectively. In the test phase, 14 and 13 teams successfully submitted valid results with PSNR (RGB) scores better than the baseline. This challenge establishes a new benchmark for stereo image SR.

关键词： Degradation computer vision conferences Superresolution Benchmark testing pattern recognition

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Towards Quantitative Evaluation Metrics for Image Editing Approaches

Towards Quantitative Evaluation Metrics for Image Editing Ap...

引用

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

作者： Dana Cohen Hochberg Oron Anschel Alon Shoshan Igor Kviatkovsky Manoj Aggarwal Gérard Medioni Amazon

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In the rapidly evolving field of Generative AI, this work takes initial steps towards establishing a systematic approach for comparing image editing methods. Currently, there is a lack of quantitative metrics for evaluating image editing tasks, with new methods being evaluated mostly qualitatively. Our methodology involves three key components: 1) The creation of a large synthetic dataset using GAN-Control, which enables the generation of ground-truth images for consistent edits across different facial identities; 2) A matching procedure that pairs the edited images with their corresponding ground-truth; and 3) Application of the Perceptual Distance metric to matched pairs. We assessed the effectiveness of our proposed framework through a user study and a set of simulation experiments. Our results indicate that our approach can rank image-editing methods in a way that aligns with human judgment. This research seeks to lay the foundation for a comprehensive evaluation framework for image editing techniques in subsequent studies, initiating a dialogue on this topic.

关键词： Measurement computer vision Systematics Generative AI conferences pattern recognition Synthetic data

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Context-aware Video Anomaly Detection in Long-Term Datasets

Context-aware Video Anomaly Detection in Long-Term Datasets

引用

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

作者： Zhengye Yang Richard J. Radke Department of ECSE Rensselaer Polytechnic Institute Troy NY USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Video anomaly detection research is generally evaluated on short, isolated benchmark videos only a few minutes long. However, in real-world environments, security cameras observe the same scene for months or years at a time, and the notion of anomalous behavior critically depends on context, such as the time of day, day of week, or schedule of events. Here, we propose a context-aware video anomaly detection algorithm, Trinity, specifically targeted to these scenarios. Trinity is especially well-suited to crowded scenes in which individuals cannot be easily tracked, and anomalies are due to speed, direction, or absence of group motion. Trinity is a contrastive learning framework that aims to learn alignments between context, appearance, and motion, and uses alignment quality to classify videos as normal or anomalous. We evaluate our algorithm on both conventional benchmarks and a public webcam-based dataset we collected that spans more than three months of activity.

关键词： Schedules computer vision Target tracking conferences Contrastive learning Benchmark testing pattern recognition

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