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Deep Spherical Manifold Gaussian Kernel for Unsupervised Domain Adaptation

Deep Spherical Manifold Gaussian Kernel for Unsupervised Dom...

引用

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

作者： Zhang, Youshan Davison, Brian D. Lehigh Univ Comp Sci & Engn Bethlehem PA 18015 USA

ISBN: (纸本)9781665448994

Unsupervised Domain adaptation is an effective method in addressing the domain shift issue when transferring knowledge from an existing richly labeled domain to a new domain. Existing manifold-based methods either are based on traditional models or largely rely on Grassmannian manifold via minimizing differences of single covariance matrices of two domains. In addition, existing pseudo-labeling algorithms inadequately consider the quality of pseudo labels in aligning the conditional distribution between two domains. In this work, a deep spherical manifold Gaussian kernel (DSGK) framework is proposed to map the source and target subspaces into a spherical manifold and reduce the discrepancy between them by embedding both extracted features and a Gaussian kernel. To align the conditional distributions, we further develop an easy-to-hard pseudo label refinement process to improve the quality of the pseudo labels and then reduce categorical spherical manifold Gaussian kernel geodesic loss. Extensive experimental results show that DSGK outperforms state-of-the-art methods, especially on challenging cross-domain learning tasks.

关键词： Manifolds Adaptation models computer vision conferences Feature extraction pattern recognition Noise measurement

来源：评论

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Dot Distance for Tiny Object Detection in Aerial Images

Dot Distance for Tiny Object Detection in Aerial Images

引用

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

作者： Xu, Chang Wang, Jinwang Yang, Wen Yu, Lei Wuhan Univ Sch Elect Informat Wuhan Peoples R China

ISBN: (纸本)9781665448994

Object detection has achieved great progress with the development of anchor-based and anchor-free detectors. However, the detection of tiny objects is still challenging due to the lack of appearance information. In this paper, we observe that Intersection over Union (IoU), the most widely used metric in object detection, is sensitive to slight offsets between predicted bounding boxes and ground truths when detecting tiny objects. Although some new metrics such as GIoU, DIoU and CIoU are proposed, their performance on tiny object detection is still below the expected level by a large margin. In this paper, we propose a simple but effective new metric called Dot Distance (DotD) for tiny object detection where DotD is defined as normalized Euclidean distance between the center points of two bounding boxes. Extensive experiments on tiny object detection dataset show that anchor-based detectors' performance is highly improved over their baselines with the application of DotD.

关键词： Measurement computer vision conferences Object detection Detectors Euclidean distance pattern recognition

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Pano3D: A Holistic Benchmark and a Solid Baseline for 360° Depth Estimation

Pano3D: A Holistic Benchmark and a Solid Baseline for 360° ...

引用

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

作者： Albanis, Georgios Zioulis, Nikolaos Drakoulis, Petros Gkitsas, Vasileios Sterzentsenko, Vladimiros Alvarez, Federico Zarpalas, Dimitrios Daras, Petros Ctr Res & Technol Hellas Thessaloniki Greece Univ Politecn Madrid Madrid Spain

ISBN: (纸本)9781665448994

Pano3D is a new benchmark for depth estimation from spherical panoramas. It aims to assess performance across all depth estimation traits, the primary direct depth estimation performance targeting precision and accuracy, and also the secondary traits, boundary preservation and smoothness. Moreover, Pano3D moves beyond typical intra-dataset evaluation to inter-dataset performance assessment. By disentangling the capacity to generalize in unseen data into different test splits, Pano3D represents a holistic benchmark for 360 degrees depth estimation. We use it as a basis for an extended analysis seeking to offer insights into classical choices for depth estimation. This results into a solid baseline for panoramic depth that followup works can built upon to steer future progress.

关键词： computer vision conferences Estimation Benchmark testing Solids pattern recognition

来源：评论

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Adversarial Robust Model Compression using In-Train Pruning

Adversarial Robust Model Compression using In-Train Pruning

引用

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

作者： Vemparala, Manoj-Rohit Fasfous, Nael Frickenstein, Alexander Sarkar, Sreetama Zhao, Qi Kuhn, Sabine Frickenstein, Lukas Singh, Anmol Unger, Christian Nagaraja, Naveen-Shankar Wressnegger, Christian Stechele, Walter BMW Autonomous Driving Munich Germany Tech Univ Munich Munich Germany Karlsruhe Inst Technol Karlsruhe Germany

ISBN: (纸本)9781665448994

Efficiently deploying learning-based systems on embedded hardware is challenging for various reasons, two of which are considered in this paper: The model's size and its robustness against attacks. Both need to be addressed even-handedly. We combine adversarial training and model pruning in a joint formulation of the fundamental learning objective during training. Unlike existing post-train pruning approaches, our method does not use heuristics and eliminates the need for a pre-trained model. This allows for a classifier which is robust against attacks and enables better compression of the model, reducing its computational effort. In comparison to prior work, our approach yields 6.21 pp higher accuracy for an 85 % reduction in parameters for ResNet20 on the CIFAR-10 dataset.

关键词： Training computer vision Computational modeling conferences Robustness Hardware pattern recognition

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Manipulation Detection in Satellite Images Using vision Transformer

Manipulation Detection in Satellite Images Using Vision Tran...

引用

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

作者： Horvath, Janos Baireddy, Sriram Hao, Hanxiang Montserrat, Daniel Mas Delp, Edward J. Purdue Univ Sch Elect & Comp Engn Video & Image Proc Lab VIPER W Lafayette IN 47907 USA

ISBN: (纸本)9781665448994

A growing number of commercial satellite companies provide easily accessible satellite imagery. Overhead imagery is used by numerous industries including agriculture, forestry, natural disaster analysis, and meteorology. Satellite images, just as any other images, can be tampered with image manipulation tools. Manipulation detection methods created for images captured by "consumer cameras" tend to fail when used on satellite images due to the differences in image sensors, image acquisition, and processing. In this paper we propose an unsupervised technique that uses a vision Transformer to detect spliced areas within satellite images. We introduce a new dataset which includes manipulated satellite images that contain spliced objects. We show that our proposed approach performs better than existing unsupervised splicing detection techniques.

关键词： Image sensors computer vision Satellites Splicing conferences Forestry Tools

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CompConv: A Compact Convolution Module for Efficient Feature Learning

CompConv: A Compact Convolution Module for Efficient Feature...

引用

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

作者： Zhang, Chen Xu, Yinghao Shen, Yujun Zhejiang Univ Hangzhou Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781665448994

Convolutional Neural Networks (CNNs) have achieved remarkable success in various computer vision tasks but rely on tremendous computational cost. To solve this problem, existing approaches either compress well-trained large-scale models or learn lightweight models with carefully designed network structures. In this work, we make a close study of the convolution operator, which is the basic unit used in CNNs, to reduce its computing load. In particular, we propose a compact convolution module, called CompConv, to facilitate efficient feature learning. With the divide-and-conquer strategy, CompConv is able to save a great many computations as well as parameters to produce a certain dimensional feature map. Furthermore, CompConv discreetly integrates the input features into the outputs to efficiently inherit the input information. More importantly, the novel CompConv is a plug-and-play module that can be directly applied to modern CNN structures to replace the vanilla convolution layers without further effort. Extensive experimental results suggest that CompConv can adequately compress the benchmark CNN structures yet barely sacrifice the performance, surpassing other competitors.

关键词： computer vision Convolution Computational modeling conferences Benchmark testing pattern recognition Computational efficiency

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T-DEED: Temporal-Discriminability Enhancer Encoder-Decoder for Precise Event Spotting in Sports Videos

T-DEED: Temporal-Discriminability Enhancer Encoder-Decoder f...

引用

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

作者： Artur Xarles Sergio Escalera Thomas B. Moeslund Albert Clapés Universitat de Barcelona Spain Computer Vision Center Spain Aalborg University Denmark

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In this paper, we introduce T-DEED, a Temporal-Discriminability Enhancer Encoder-Decoder for Precise Event Spotting in sports videos. T-DEED addresses multiple challenges in the task, including the need for discriminability among frame representations, high output temporal resolution to maintain prediction precision, and the necessity to capture information at different temporal scales to handle events with varying dynamics. It tackles these challenges through its specifically designed architecture, featuring an encoder-decoder for leveraging multiple temporal scales and achieving high output temporal resolution, along with temporal modules designed to increase token discriminability. Leveraging these characteristics, T-DEED achieves SOTA performance on the FigureSkating and FineDiving datasets. Code is available at https://***/arturxe2/T-DEED.

关键词： computer vision Codes conferences computer architecture pattern recognition Videos Sports

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Finding Facial Forgery Artifacts with Parts-Based Detectors

Finding Facial Forgery Artifacts with Parts-Based Detectors

引用

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

作者： Schwarcz, Steven Chellappa, Rama Univ Maryland College Pk MD 20742 USA Johns Hopkins Univ Baltimore MD USA

ISBN: (纸本)9781665448994

Manipulated videos, especially those where the identity of an individual has been modified using deep neural networks, are becoming an increasingly relevant threat in the modern day. In this paper, we seek to develop a generalizable, explainable solution to detecting these manipulated videos. To achieve this, we design a series of forgery detection systems that each focus on one individual part of the face. These parts-based detection systems, which can be combined and used together in a single architecture, meet all of our desired criteria - they generalize effectively between datasets and give us valuable insights into what the network is looking at when making its decision. We thus use these detectors to perform detailed empirical analysis on the FaceForensics++, Celeb-DF, and Facebook Deep-fake Detection Challenge datasets, examining not just what the detectors find but also collecting and analyzing useful related statistics on the datasets themselves.

关键词： Deep learning computer vision Social networking (online) Face recognition conferences Neural networks Detectors

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Contrastive Domain Adaptation

Contrastive Domain Adaptation

引用

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

作者： Thota, Mamatha Leontidis, Georgios Univ Lincoln Sch Comp Sci Lincoln LN6 7TS England Univ Aberdeen Dept Comp Sci Aberdeen AB24 3UE Scotland

ISBN: (纸本)9781665448994

Recently, contrastive self-supervised learning has become a key component for learning visual representations across many computer vision tasks and benchmarks. However, contrastive learning in the context of domain adaptation remains largely underexplored. In this paper, we propose to extend contrastive learning to a new domain adaptation setting, a particular situation occurring where the similarity is learned and deployed on samples following different probability distributions without access to labels. Contrastive learning learns by comparing and contrasting positive and negative pairs of samples in an unsupervised setting without access to source and target labels. We have developed a variation of a recently proposed contrastive learning framework that helps tackle the domain adaptation problem, further identifying and removing possible negatives similar to the anchor to mitigate the effects of false negatives. Extensive experiments demonstrate that the proposed method adapts well, and improves the performance on the downstream domain adaptation task.

关键词： Training computer vision Adaptation models Visualization Machine learning algorithms Pipelines Machine learning

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Segment Anything in Food Images

Segment Anything in Food Images

引用

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

作者： Saeed S. Alahmari Michael Gardner Tawfiq Salem Najran University Saudi Arabia King Faisal University Saudi Arabia Purdue University USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper introduces a new approach for food image segmentation utilizing the Segment Anything Model (SAM), with the additional refinement achieved through fine-tuning with Low-Rank Adaptation layers (LoRA). The segmentation task involves generating a binary mask for food in RGB images, with pixels categorized as background or food. We conduct various experiments to assess and compare the performance of our proposed method with previous approaches. Our findings indicate that our method consistently outperforms other techniques, achieving an accuracy of 94.14%. The improved accuracy of our approach highlights its potential for various applications in food image analysis, contributing to the advancement of computer vision techniques in the realm of food recognition and segmentation.

关键词： Image segmentation computer vision Adaptation models Technological innovation Accuracy Image recognition Image analysis

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