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CarlaScenes: A synthetic dataset for odometry in autonomous driving

CarlaScenes: A synthetic dataset for odometry in autonomous ...

引用

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

作者： Kloukiniotis, Andreas Papandreou, Andreas Anagnostopoulos, Christos Lalos, Aris Kapsalas, Petros Nguyen, D-, V Moustakas, Konstantinos Univ Patras Patras Greece ISI Ind Syst Inst Patras Patras Greece Panasonic Automot Langen Germany

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Despite the great scientific effort to capture adequately the complex environments in which autonomous vehicles (AVs) operate there are still use-cases that even SoA methods fail to handle. Specifically in odometry problems, on the one hand, geometric solutions operate with certain assumptions that are often breached in AVs, and on the other hand, deep learning methods do not achieve high accuracy. To contribute to that we present CarlaScenes, a large-scale simulation dataset captured using the CARLA simulator. The dataset is oriented to address the challenging odometry scenarios that cause the current state of art odometers to deviate from their normal operations. Based on a case study of failures presented in experiments we distinguished 7 different sequences of data. CarlaScenes besides providing consistent reference poses, includes data with semantic annotation at the instance level for both image and lidar. The full dataset is available at https://***/CarlaScenes/***.

关键词： Deep learning computer vision Laser radar conferences Semantics XML Trajectory

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Patch-wise Contrastive Style Learning for Instagram Filter Removal

Patch-wise Contrastive Style Learning for Instagram Filter R...

引用

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

作者： Kinli, Furkan Ozcan, Baris Kirac, Furkan Ozyegin Univ Vis & Graph Lab Video Istanbul Turkey

ISBN: (纸本)9781665487399

Image-level corruptions and perturbations degrade the performance of CNNs on different downstream vision tasks. Social media filters are one of the most common resources of various corruptions and perturbations for real-world visual analysis applications. The negative effects of these distractive factors can be alleviated by recovering the original images with their pure style for the inference of the downstream vision tasks. Assuming these filters substantially inject a piece of additional style information to the social media images, we can formulate the problem of recovering the original versions as a reverse style transfer problem. We introduce Contrastive Instagram Filter Removal Network (CIFR), which enhances this idea for Instagram filter removal by employing a novel multi-layer patch-wise contrastive style learning mechanism. Experiments show our proposed strategy produces better qualitative and quantitative results than the previous studies. Moreover, we present the results of our additional experiments for proposed architecture within different settings. Finally, we present the inference outputs and quantitative comparison of filtered and recovered images on localization and segmentation tasks to encourage the main motivation for this problem.

关键词： Training Matched filters Social networking (online) Perturbation methods Multimedia Web sites Semantics computer architecture

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A Neural-network Enhanced Video Coding Framework beyond VVC

A Neural-network Enhanced Video Coding Framework beyond VVC

引用

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

作者： Li, Junru Li, Yue Lin, Chaoyi Zhang, Kai Zhang, Li Bytedance Inc Multimedia Lab San Diego CA 92122 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

This paper presents a hybrid video compression framework, aiming at providing a demonstration of applying deep learning-based approaches beyond conventional coding framework. The proposed hybrid framework is established over the Enhanced Compression Model (ECM) of which the core is the Versatile Video Coding (VVC) standard. We propose to integrate a series of enhanced coding tools, such as block partitioning, intra prediction, and inter prediction to further remove the spatial and temporal redundancy. Moreover, deep learning-based technologies including loop filter and super resolution are involved to restore the compression distortion. Compared with the VVC software VTM-11.0, experimental results demonstrate the effectiveness of the proposed learning-based framework, leading to 25.81%, 35.08%, and 37.54% bit-rate savings for Y, Cb and Cr components, respectively under random access configuration. In addition, the proposed framework achieves 39.313 and 32.050 PSNRs in the test set under 1 Mbps and 0.1 Mbps video compression tracks of CLIC-2022. 33.522, 30.758, and 28.300 in terms of PSNR are obtained in 0.3 bpp, 0.15 bpp, and 0.075 bpp image compression tracks.

关键词： Video coding Image coding Filtering Redundancy Video compression Software pattern recognition

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Semantically Grounded Visual Embeddings for Zero-Shot Learning

Semantically Grounded Visual Embeddings for Zero-Shot Learni...

引用

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

作者： Nawaz, Shah Cavazza, Jacopo Del Bue, Alessio Ist Italiano Tecnol IIT Pattern Anal & Comp Vis PAVIS Genoa Italy Ist Italiano Tecnol IIT Visual Geometry & Modelling VGM Genoa Italy Deutsch Elektronen Synchrotron DESY Hamburg Germany

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Zero-shot learning methods rely on fixed visual and semantic embeddings, extracted from independent vision and language models, both pre-trained for other large-scale tasks. This is a weakness of current zero-shot learning frameworks as such disjoint embeddings fail to adequately associate visual and textual information to their shared semantic content. Therefore, we propose to learn semantically grounded and enriched visual information by computing a joint image and text model with a two-stream network on a proxy task. To improve this alignment between image and textual representations, provided by attributes, we leverage ancillary captions to provide grounded semantic information. Our method, dubbed joint embeddings for zero-shot learning is evaluated on several benchmark datasets, improving the performance of existing state-of-the-art methods in both standard (+1.6% on aPY, +2.6% on FLO) and generalized (+2.1% on AWA2, +2.2% on CUB) zero-shot recognition.

关键词： Learning systems Visualization Computational modeling Soft sensors conferences Semantics Benchmark testing

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Out-Of-Distribution Detection In Unsupervised Continual Learning

Out-Of-Distribution Detection In Unsupervised Continual Lear...

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

作者： He, Jiangpeng Zhu, Fengqing Purdue Univ Elmore Family Sch Elect & Comp Engn W Lafayette IN 47907 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Unsupervised continual learning aims to learn new tasks incrementally without requiring human annotations. However, most existing methods, especially those targeted on image classification, only work in a simplified scenario by assuming all new data belong to new tasks, which is not realistic if the class labels are not provided. Therefore, to perform unsupervised continual learning in real life applications, an out-of-distribution detector is required at beginning to identify whether each new data corresponds to a new task or already learned tasks, which still remains under-explored yet. In this work, we formulate the problem for Out-of-distribution Detection in Unsupervised Continual Learning (OOD-UCL) with the corresponding evaluation protocol. In addition, we propose a novel OOD detection method by correcting the output bias at first and then enhancing the output confidence for in-distribution data based on task discriminativeness, which can be applied directly without modifying the learning procedures and objectives of continual learning. Our method is evaluated on CIFAR-100 dataset by following the proposed evaluation protocol and we show improved performance compared with existing OOD detection methods under the unsupervised continual learning scenario.

关键词： computer vision Protocols Annotations conferences Detectors pattern recognition Task analysis

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引用

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

作者： Shaharabany, Tal Wolf, Lior Tel Aviv Univ Tel Aviv Israel

ISBN: (纸本)9798350301298

A phrase grounding model receives an input image and a text phrase and outputs a suitable localization map. We present an effective way to refine a phrase ground model by considering self-similarity maps extracted from the latent representation of the model's image encoder. Our main insights are that these maps resemble localization maps and that by combining such maps, one can obtain useful pseudo-labels for performing self-training. Our results surpass, by a large margin, the state of the art in weakly supervised phrase grounding. A similar gap in performance is obtained for a recently proposed downstream task called WWbL, in which only the image is input, without any text. Our code is available at https://***/talshaharabany/Similarity-Maps-forSelf-Training-Weakly-Supervised- Phrase-Grounding.

关键词： language reasoning vision

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SymDNN: Simple & Effective Adversarial Robustness for Embedded Systems

SymDNN: Simple & Effective Adversarial Robustness for Embedd...

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

作者： Dey, Swarnava Dasgupta, Pallab Chakrabarti, Partha P. Indian Inst Technol Kharagpur Kharagpur 721302 W Bengal India

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We propose SymDNN, a Deep Neural Network (DNN) inference scheme, to segment an input image into small patches, replace those patches with representative symbols, and use the reconstructed image for CNN inference. This approach of deconstruction of images, and the reconstruction from cluster centroids trained on clean images, enhances robustness against adversarial attacks. The input transform used in SymDNN is learned from very large datasets, making it difficult to approximate for adaptive adversarial attacks. For example, SymDNN achieves 23% and 42% robust accuracy at L-infinity attack strengths of 8/255 and 4/255 respectively, against BPDA under a complete white box setting, where most input processing based defenses break completely. SymDNN is not a future-proof adversarial defense that can defend any attack, but it is one of the few readily usable defenses in resource-limited embedded systems that defends against a wide range of attacks.

关键词： computer vision Adaptation models Visualization Embedded systems Computational modeling Symbols Transforms

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Region-based Appearance and Flow Characteristics for Anomaly Detection in Infrared Surveillance Imagery

Region-based Appearance and Flow Characteristics for Anomaly...

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Gaus, Yona Falinie A. Bhowmik, Neelanjan Isaac-Medina, Brian K. S. Shum, Hubert P. H. Atapour-Abarghouei, Amir Breckon, Toby P. Durham University Department of Computer Science Durham United Kingdom Durham University Department of Engineering Durham United Kingdom

ISBN: (纸本)9798350302493

Anomaly detection is a classical problem within automated visual surveillance, namely the determination of the normal from the abnormal when operational data availability is highly biased towards one class (normal) due to both insufficient sample size, and inadequate distribution coverage for the other class (abnormal). In this work, we propose the dual use of both visual appearance and localized motion characteristics, derived from optic flow, applied on a per-region basis to facilitate object-wise anomaly detection within this context. Leveraging established object localization techniques from a region proposal network, optic flow is extracted from each object region and combined with appearance in the far infrared (thermal) band to give a 3-channel spatiotemporal tensor representation for each object (1 × thermal - spatial appearance;2 × optic flow magnitude as x and y components - temporal motion). This formulation is used as the basis for training contemporary semi-supervised anomaly detection approaches in a region-based manner such that anomalous objects can be detected as a combination of appearance and/or motion within the scene. Evaluation is performed using the Long-Term infrared (thermal) Imaging (LTD) benchmark dataset against which successful detection of both anomalous object appearance and motion characteristics are demonstrated using a range of semi-supervised anomaly detection approaches. © 2023 ieee.

关键词： Anomaly detection

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BeCAPTCHA-Type: Biometric Keystroke Data Generation for Improved Bot Detection

BeCAPTCHA-Type: Biometric Keystroke Data Generation for Impr...

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Dealcala, Daniel Morales, Aythami Tolosana, Ruben Acien, Alejandro Fierrez, Julian Hernandez, Santiago Ferrer, Miguel A. Diaz, Moises Universidad Autonoma de Madrid Biometrics and Data Pattern Analytics Lab Spain University Las Palmas Gran Canaria Spain

ISBN: (纸本)9798350302493

This work proposes a data driven learning model for the synthesis of keystroke biometric data. The proposed method is compared with two statistical approaches based on Universal and User-dependent models. These approaches are validated on a bot detection task, using the keystroke synthetic data to improve the training process of keystroke-based bot detection systems. Our experimental framework considers a dataset with 136 million keystroke events from 168 thousand subjects. We have analyzed the performance of the three synthesis approaches through qualitative and quantitative experiments. Different bot detectors are considered based on several supervised classifiers (Support Vector Machine, Random Forest, Gaussian Naive Bayes and a Long Short-Term Memory network) and a learning framework including human and synthetic samples. The experiments demonstrate the realism of the synthetic samples. The classification results suggest that in scenarios with large labeled data, these synthetic samples can be detected with high accuracy. However, if the proposed synthetic data is nor properly modelled using massive data by bot detectors, then that data will be very difficult to detect even for the most sophisticate bot detectors. Furthermore, these results show the great potential of the presented models for improving the training of bot detection technology. © 2023 ieee.

关键词： Biometrics

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SyncMask: Synchronized Attentional Masking for Fashion-centric vision-Language Pretraining

SyncMask: Synchronized Attentional Masking for Fashion-centr...

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

作者： Song, Chull Hwan Hwang, Taebaek Yoon, Jooyoung Choi, Shunghyun Gu, Yeong Hyeon Dealicious Inc Seoul South Korea Sejong Univ Seoul South Korea

ISBN: (纸本)9798350353006

vision-language models (VLMs) have made significant strides in cross-modal understanding through large-scale paired datasets. However, in fashion domain, datasets of-en exhibit a disparity between the information conveyed in image and text. This issue stems from datasets containing multiple images of a single fashion item all paired with one text, leading to cases where some textual details are not visible in individual images. This mismatch, particularly when non-co-occurring elements are masked, undermines the training of conventional VLM objectives like Masked Language Modeling and Masked Image Modeling, thereby hindering the model's ability to accurately align fine-grained visual and textual features. Addressing this problem, we pro-pose Synchronized attentional Masking (SyncMask), which generate masks that pinpoint the image patches and word tokens where the information co-occur in both image and text. This synchronization is accomplished by harnessing cross-attentional features obtained from a momentum model, ensuring a precise alignment between the two modalities. Additionally, we enhance grouped batch sampling with semi-hard negatives, effectively mitigating false negative issues in Image-Text Matching and Image-Text Contrastive learning objectives within fashion datasets. Our experiments demonstrate the effectiveness of the proposed approach, outperforming existing methods in three downstream tasks.

关键词： Visual languages

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