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t-RAIN: Robust generalization under weather-aliasing label shift attacks

t-RAIN: Robust generalization under weather-aliasing label s...

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2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Marathe, Aboli Prabhu, Sanjana Carnegie Mellon University Machine Learning Department PittsburghPA15213 United States Carnegie Mellon University Department of Electrical and Computer Engineering PittsburghPA15213 United States

ISBN: (纸本)9798350302493

In the classical supervised learning settings, classifiers are fit with the assumption of balanced label distributions and produce remarkable results on the same. In the real world, however, these assumptions often bend and in turn adversely impact model performance. Identifying bad learners in skewed target distributions is even more challenging. Thus achieving model robustness under these "label shift"settings is an important task in autonomous perception. In this paper, we analyze the impact of label shift on the task of multi-weather classification for autonomous vehicles. We use this information as a prior to better assess pedestrian detection in adverse weather. We model the classification performance as an indicator of robustness under 4 label shift scenarios and study the behavior of multiple classes of models. We propose t-RAIN a similarity mapping technique for synthetic data augmentation using large scale generative models and evaluate the performance on DAWN dataset. This mapping boosts model test accuracy by 2.1, 4.4, 1.9, 2.7 % in no-shift, fog, snow, dust shifts respectively. We present state-of-the-art pedestrian detection results on real and synthetic weather domains with best performing 82.69 AP (snow) and 62.31 AP (fog) respectively. © 2023 ieee.

关键词： Snow

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Robustness of Visual Explanations to Common Data Augmentation Methods

Robustness of Visual Explanations to Common Data Augmentatio...

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2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Tětková, Lenka Hansen, Lars Kai Technical University of Denmark Department of Applied Mathematics and Computer Science Richard Petersens Plads 321 Lyngby2800 Kgs. Denmark

ISBN: (纸本)9798350302493

As the use of deep neural networks continues to grow, understanding their behaviour has become more crucial than ever. Post-hoc explainability methods are a potential solution, but their reliability is being called into question. Our research investigates the response of post-hoc visual explanations to naturally occurring transformations, often referred to as augmentations. We anticipate explanations to be invariant under certain transformations, such as changes to the colour map while responding in an equivariant manner to transformations like translation, object scaling, and rotation. We have found remarkable differences in robustness depending on the type of transformation, with some explainability methods (such as LRP composites and Guided Backprop) being more stable than others. We also explore the role of training with data augmentation. We provide evidence that explanations are typically less robust to augmentation than classification performance, regardless of whether data augmentation is used in training or not. © 2023 ieee.

关键词： Deep neural networks

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Rip Current Segmentation: A Novel Benchmark and YOLOv8 Baseline Results

Rip Current Segmentation: A Novel Benchmark and YOLOv8 Basel...

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2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Dumitriu, Andrei Tatui, Florin Miron, Florin Ionescu, Radu Tudor Timofte, Radu CAIDAS IFI University of Würzburg Computer Vision Lab Germany University of Bucharest Romania

ISBN: (纸本)9798350302493

Rip currents are the leading cause of fatal accidents and injuries on many beaches worldwide, emphasizing the importance of automatically detecting these hazardous surface water currents. In this paper, we address a novel task: rip current instance segmentation. We introduce a comprehensive dataset containing 2,466 images with newly created polygonal annotations for instance segmentation, used for training and validation. Additionally, we present a novel dataset comprising 17 drone videos (comprising about 24K frames) captured at 30 FPS, annotated with both polygons for instance segmentation and bounding boxes for object detection, employed for testing purposes. We train various versions of YOLOv8 for instance segmentation on static images and assess their performance on the test dataset (videos). The best results were achieved by the YOLOv8-nano model (runnable on a portable device), with an mAP50 of 88.94% on the validation dataset and 81.21% macro average on the test dataset. The results provide a baseline for future research in rip current segmentation. Our work contributes to the existing literature by introducing a detailed, annotated dataset, and training a deep learning model for instance segmentation of rip currents. The code, training details and the annotated dataset are made publicly available at https://***/Irikos/rip-currents. © 2023 ieee.

关键词： Surface waters

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Investigating Catastrophic Overfitting in Fast Adversarial Training: A Self-fitting Perspective

Investigating Catastrophic Overfitting in Fast Adversarial T...

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2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： He, Zhengbao Li, Tao Chen, Sizhe Huang, Xiaolin Shanghai Jiao Tong University Department of Automation China

ISBN: (纸本)9798350302493

Although fast adversarial training provides an efficient approach for building robust networks, it may suffer from a serious problem known as catastrophic overfitting (CO), where multi-step robust accuracy suddenly collapses to zero. In this paper, we for the first time decouple single-step adversarial examples into data-information and self-information, which reveals an interesting phenomenon called "self-fitting". Self-fitting, i.e., the network learns the self-information embedded in single-step perturbations, naturally leads to the occurrence of CO. When self-fitting occurs, the network experiences an obvious "channel differentiation"phenomenon that some convolution channels accounting for recognizing self-information become dominant, while others for data-information are suppressed. In this way, the network can only recognize images with sufficient self-information and loses generalization ability to other types of data. Based on self-fitting, we provide new insights into the existing methods to mitigate CO and extend CO to multi-step adversarial training. Our findings reveal a self-learning mechanism in adversarial training and open up new perspectives for suppressing different kinds of information to mitigate CO. © 2023 ieee.

关键词： computer vision

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SEM-POS: Grammatically and Semantically Correct Video Captioning

SEM-POS: Grammatically and Semantically Correct Video Captio...

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2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Nadeem, Asmar Hilton, Adrian Dawes, Robert Thomas, Graham Mustafa, Annin United Kingdom Bbc Research and Development United Kingdom

ISBN: (纸本)9798350302493

Generating grammatically and semantically correct captions in video captioning is a challenging task.. The captions generated from the existing methods are either word-by-word that do not align with grammatical structure or miss key information from the input videos. To address these issues, we introduce a novel global-local fusion network, with a Global-Local Fusion Block (GLFB) that encodes and fuses features from different parts of speech (POS) components with visual-spatial features. We use novel combinations of different POS components - 'determinant + subject', 'auxiliary verb', 'verb', and 'determinant + object' for supervision of the POS blocks - Det + Subject, Aux Verb, Verb, and Det + Object respectively. The novel global-local fusion network together with POS blocks helps align the visual features with language description to generate grammatically and semantically correct captions. Extensive qualitative and quantitative experiments on benchmark MSVD and MSRVTT datasets demonstrate that the proposed approach generates more grammatically and semantically correct captions compared to the existing methods, achieving the new state-of-the-art. Ablations on the POS blocks and the GLFB demonstrate the impact of the contributions on the proposed method. © 2023 ieee.

关键词： computer vision

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Exploring Motion Information for Distractor Suppression in Visual Tracking

Exploring Motion Information for Distractor Suppression in V...

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

作者： Liu, Kaiwen Gao, Jin Liu, Haowei Li, Liang Li, Bing Hu, Weiming Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing Peoples R China Univ Chinese Acad Sci Sch AI Beijing Peoples R China Chinese Acad Sci Ctr Excellence Brain Sci & Intelligence Technol Beijing Peoples R China Beijing Inst Basic Med Sci Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In the past few years, Siamese networks have achieved outstanding improvements in visual object tracking. However, visual distractors with similar semantics can be easily misclassified as the target by Siamese networks and may consequently result in the drift problem. Besides, the Hanning window penalty, which is generally used to suppress distractors, could fail in many challengeable scenes. Notably, most failures violate the assumption of motion continuity. Thus, in this work, we explore motion information to mitigate the drift problem in visual tracking. First, we introduce a simple linear Kalman filter to predict the bounding box of the target in the current frame, which acts as a reference for decisions. Second, an IoU-Guided penalty is assembled in the post-processing to suppress distractors effectively. It's worth mentioning that our method is almost cost-free. We conduct numerous experimental validations and analyses of our approach on several challenging sequences and datasets. Our tracker runs at approximately 40 fps and performs well on those sequences which include the Background Clutter attribute. Finally, by simultaneously integrating the IoU-Guidedpenalty and the Hanning window penalty with a strong baseline tracker TransT [7], our method achieves favorable gains by 69.1 -> 71.5, 65.7 -> 66.7, 64.9 -> 65.9 success on OTB-100 [32], LaSOT [12], NFS[18].

关键词： Training Visualization computer vision Target tracking Statistical analysis conferences Semantics

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BinaryViT: Pushing Binary vision Transformers Towards Convolutional Models

BinaryViT: Pushing Binary Vision Transformers Towards Convol...

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2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Le, Phuoc-Hoan Charles Li, Xinlin Huawei Noah's Ark Lab Montreal Research Center Canada Huawei Noah's Ark Lab Canada

ISBN: (纸本)9798350302493

With the increasing popularity and the increasing size of vision transformers (ViTs), there has been an increasing interest in making them more efficient and less computationally costly for deployment on edge devices with limited computing resources. Binarization can be used to help reduce the size of ViT models and their computational cost significantly, using popcount operations when the weights and the activations are in binary. However, ViTs suffer a larger performance drop when directly applying convolutional neural network (CNN) binarization methods or existing binarization methods to binarize ViTs compared to CNNs on datasets with a large number of classes such as ImageNet-1k. With extensive analysis, we find that binary vanilla ViTs such as DeiT miss out on a lot of key architectural properties that CNNs have that allow binary CNNs to have much higher representational capability than binary vanilla ViT. Therefore, we propose BinaryViT, in which inspired by the CNN architecture, we include operations from the CNN architecture into a pure ViT architecture to enrich the representational capability of a binary ViT without introducing convolutions. These include an average pooling layer instead of a token pooling layer, a block that contains multiple average pooling branches, an affine transformation right before the addition of each main residual connection, and a pyramid structure. Experimental results on the ImageNet-1k dataset show the effectiveness of these operations that allow a fully-binary pure ViT model to be competitive with previous state-of-the-art binary (SOTA) CNN models. © 2023 ieee.

关键词： Convolution

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StillFast: An End-to-End Approach for Short-Term Object Interaction Anticipation

StillFast: An End-to-End Approach for Short-Term Object Inte...

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2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Ragusa, Francesco Farinella, Giovanni Maria Furnari, Antonino University of Catania Next Vision S.r.l. - Spin-off of the University of Catania FPV@IPLab Italy

ISBN: (纸本)9798350302493

Anticipation problem has been studied considering different aspects such as predicting humans' locations, predicting hands and objects trajectories, and forecasting actions and human-object interactions. In this paper, we studied the short-term object interaction anticipation problem from the egocentric point of view, proposing a new end-to-end architecture named StillFast. Our approach simultaneously processes a still image and a video detecting and localizing next-active objects, predicting the verb which describes the future interaction and determining when the interaction will start. Experiments on the large-scale egocentric dataset EGO4D [17] show that our method outperformed state-of-the-art approaches on the considered task. Our method is ranked first in the public leaderboard of the EGO4D short term object interaction anticipation challenge 2022 and it is the official baseline for the 2023 one. Please see the project web page for code and additional details: https://***/stillfast/. © 2023 ieee.

关键词： Forecasting

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Improving Deep Learning-based Automatic Checkout System Using Image Enhancement Techniques

Improving Deep Learning-based Automatic Checkout System Usin...

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2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Pham, Long Hoang Tran, Duong Nguyen-Ngoc Nguyen, Huy-Hung Jeon, Hyung-Joon Tran, Tai Huu-Phuong Jeon, Hyung-Min Jeon, Jae Wook Sungkyunkwan University Department of Electrical and Computer Engineering Korea Republic of

ISBN: (纸本)9798350302493

The retail sector has experienced significant growth in artificial intelligence and computer vision applications, particularly with the emergence of automatic checkout (ACO) systems in stores and supermarkets. ACO systems encounter challenges such as object occlusion, motion blur, and similarity between scanned items while acquiring accurate training images for realistic checkout scenarios is difficult due to constant product updates. This paper improves existing deep learning-based ACO solutions by incorporating several image enhancement techniques in the data pre-processing step. The proposed ACO system employs a detect-and-track strategy, which involves: (1) detecting objects in areas of interest;(2) tracking objects in consecutive frames;and (3) counting objects using a track management pipeline. Several data generation techniques - including copy-and-paste, random placement, and augmentation - are employed to create diverse training data. Additionally, the proposed solution is designed as an open-ended framework that can be easily expanded to accommodate multiple tasks. The system has been evaluated on the AI City Challenge 2023 Track 4 dataset, showcasing outstanding performance by achieving a top-1 ranking on test-set A with an F1 score of 0.9792. © 2023 ieee.

关键词： Image enhancement

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On the Real-Time Semantic Segmentation of Aphid Clusters in the Wild

On the Real-Time Semantic Segmentation of Aphid Clusters in ...

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2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Rahman, Raiyan Indris, Christopher Zhang, Tianxiao Li, Kaidong McCornack, Brian Flippo, Daniel Sharda, Ajay Wang, Guanghui Toronto Metropolitan University Department of Computer Science Toronto Canada University of Kansas Department of Electrical Engineering and Computer Science Lawrence United States Kansas State University Department of Entomology Manhattan United States Kansas State University Department of Biological and Agricultural Engineering Manhattan United States

ISBN: (纸本)9798350302493

Aphid infestations can cause extensive damage to wheat and sorghum fields and spread plant viruses, resulting in significant yield losses in agriculture. To address this issue, farmers often rely on chemical pesticides, which are inefficiently applied over large areas of fields. As a result, a considerable amount of pesticide is wasted on areas without pests, while inadequate amounts are applied to areas with severe infestations. The paper focuses on the urgent need for an intelligent autonomous system that can locate and spray infestations within complex crop canopies, reducing pesticide use and environmental impact. We have collected and labeled a large aphid image dataset in the field, and propose the use of real-time semantic segmentation models to segment clusters of aphids. A multiscale dataset is generated to allow for learning the clusters at different scales. We compare the segmentation speeds and accuracy of four state-of-the-art real-time semantic segmentation models on the aphid cluster dataset, benchmarking them against nonreal-time models. The study results show the effectiveness of a real-time solution, which can reduce inefficient pesticide use and increase crop yields, paving the way towards an autonomous pest detection system. © 2023 ieee.

关键词： Semantics

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