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Deep Multi-Task Learning for Joint Localization, Perception, and Prediction

Deep Multi-Task Learning for Joint Localization, Perception,...

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

作者： Phillips, John Martinez, Julieta Barsan, Ioan Andrei Casas, Sergio Sadat, Abbas Urtasun, Raquel Uber Adv Technol Grp Pittsburgh PA 15201 USA Univ Waterloo Waterloo ON Canada Univ Toronto Toronto ON Canada

ISBN: (纸本)9781665445092

Over the last few years, we have witnessed tremendous progress on many subtasks of autonomous driving including perception, motion forecasting, and motion planning. However, these systems often assume that the car is accurately localized against a high-definition map. In this paper we question this assumption, and investigate the issues that arise in state-of-the-art autonomy stacks under localization error. Based on our observations, we design a system that jointly performs perception, prediction, and localization. Our architecture is able to reuse computation between the three tasks, and is thus able to correct localization errors efficiently. We show experiments on a large-scale autonomy dataset, demonstrating the efficiency and accuracy of our proposed approach.

关键词： Location awareness computer vision Graphics processing units Object detection computer architecture Planning pattern recognition

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Zero-Shot Spatio-Temporal Action Detection by Enhancing Context-Relation Capability of vision-Language Models 27th

Zero-Shot Spatio-Temporal Action Detection by Enhancing Con...

引用

27th International conference on pattern recognition, ICPR 2024

作者： Babazaki, Yasunori Shibata, Takashi Takahashi, Toru NEC Corporation Kawasaki Japan

ISBN: (纸本)9783031781094

We present a zero-shot spatio-temporal action detection framework that enhances the relational extraction capabilities of vision-language models. Zero-shot spatio-temporal action detection involves identifying a person’s actions in a video and recognizing the time and place of these actions without prior training on those specific actions. Large-scale pre-trained vision-language models like CLIP exhibit zero-shot recognition capabilities for various tasks but struggle with extracting local features and relationships. By explicitly enhancing the extraction of person-context relationships in input videos and improving vision-language feature extraction, our proposed framework performs spatio-temporal action detection. It effectively captures local features and relationships between people and contexts while leveraging the strengths of zero-shot recognition from large-scale vision-language models. The two key components of our framework are person tracking in each input frame while ensuring smooth bounding-box shapes across frames, and the explicit interaction between visual features and language features in the shallow layers of visual feature extraction. We demonstrate the effectiveness of our framework through comprehensive experiments on two well-known action detection datasets, JHMDB and UCF101-24. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Visual languages

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Intelligent IoT Control System based on Hand Gesture recognition 5

Intelligent IoT Control System based on Hand Gesture Recogni...

引用

5th ieee International conference on Advanced Information and Communication Technologies, AICT 2023

作者： Balazh, Denys Mrak, Vasyl Sydor, Artur Andrushchak, Volodymyr Rusyn, Bohdan Maksymyuk, Taras Lviv Polytechnic National University Department of Telecommunications Lviv Ukraine Institute Nas of Ukraine Department of Remote Sensing Information Technologies Karpenko Physico-Mechanical Lviv Ukraine

ISBN: (纸本)9798350372571

This paper introduces a cutting-edge smart home system, focusing on the seamless integration of hand gesture recognition with Internet of Things (IoT) technologies. Employing advanced computer vision, the system allows users to control home appliances, particularly lighting, through intuitive hand gestures. Central to this innovation are cameras that accurately interpret these gestures in real time. The system architecture, built around the ESP8266-01 microcontroller and HW-655 relay module, responds promptly to these gesture commands, ensuring a high level of responsiveness and user convenience. © 2023 ieee.

关键词： computer vision

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Feedback control of event cameras

Feedback control of event cameras

引用

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

作者： Delbruck, Tobi Graca, Rui Paluch, Marcin UZH ETH Zurich Inst Neuroinformat Zurich Switzerland

ISBN: (纸本)9781665448994

Dynamic vision sensor event cameras produce a variable data rate stream of brightness change events. Event production at the pixel level is controlled by threshold, bandwidth, and refractory period bias current parameter settings. Biases must be adjusted to match application requirements and the optimal settings depend on many factors. As a first step towards automatic control of biases, this paper proposes fixed-step feedback controllers that use measurements of event rate and noise. The controllers regulate the event rate within an acceptable range using threshold and refractory period control, and regulate noise using bandwidth control. Experiments demonstrate model validity and feedback control.

关键词： computer vision Current measurement conferences Bandwidth Production vision sensors Cameras

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EdgeCam: A Distributed Camera Operating System for Inference Scheduling and Continuous Learning 9

EdgeCam: A Distributed Camera Operating System for Inference...

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9th ACM/ieee conference on Internet of Things Design and Implementation (IoTDI)

作者： Doug, Yuqi Gao, Guanyu Nanjing Univ Sci & Technol Nanjing Peoples R China

ISBN: (纸本)9798350370256;9798350370263

Deep Neural Networks (DNNs) are commonly used in camera systems for video surveillance. However, the computational demands of DNN inference pose challenges for on-edge video analytics due to potential delay. Additionally, edge cameras typically employ lightweight models, which are susceptible to data drift. In this demo, we present EdgeCam, an open-source distributed camera operating system that incorporates inference scheduling and continuous learning for video analytics. EdgeCam comprises multiple edge nodes and the cloud, enabling collaborative video analytics. Edge nodes also collect drift data to support continuous learning and maintain recognition accuracy. We have implemented essential functionalities and algorithms, ensuring modularity and ease of configuration. The source code of EdgeCam is at https://***/MSNLAB/EdgeCam.

关键词： Video analytics edge/cloud computing machine learning inference computer vision applications

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AidUI: Toward Automated recognition of Dark patterns in User Interfaces 23

AidUI: Toward Automated Recognition of Dark Patterns in User...

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45th ieee/ACM International conference on Software Engineering (ICSE)

作者： Mansur, S. M. Hasan Salma, Sabiha Awofisayo, Damilola Moran, Kevin George Mason Univ Dept Comp Sci Fairfax VA 22030 USA Duke Univ Dept Comp Sci Durham NC 27706 USA George Mason Univ Aspiring Scientist Summer Internship Program ASSI Fairfax VA 22030 USA

ISBN: (纸本)9781665457019

Past studies have illustrated the prevalence of UI dark patterns, or user interfaces that can lead end-users toward (unknowingly) taking actions that they may not have intended. Such deceptive UI designs can be either intentional (to benefit an online service) or unintentional (through complicit design practices) and can result in adverse effects on end users, such as oversharing personal information or financial loss. While significant research progress has been made toward the development of dark pattern taxonomies across different software domains, developers and users currently lack guidance to help recognize, avoid, and navigate these often subtle design motifs. However, automated recognition of dark patterns is a challenging task, as the instantiation of a single type of pattern can take many forms, leading to significant variability. In this paper, we take the first step toward understanding the extent to which common UI dark patterns can be automatically recognized in modern software applications. To do this, we introduce AIDUI, a novel automated approach that uses computer vision and natural language processing techniques to recognize a set of visual and textual cues in application screenshots that signify the presence of ten unique UI dark patterns, allowing for their detection, classification, and localization. To evaluate our approach, we have constructed CONTEXTDP, the current largest dataset of fully-localized UI dark patterns that spans 175 mobile and 83 web UI screenshots containing 301 dark pattern instances. The results of our evaluation illustrate that AIDUI achieves an overall precision of 0.66, recall of 0.67, F1-score of 0.65 in detecting dark pattern instances, reports few false positives, and is able to localize detected patterns with an IoU score of 0.84. Furthermore, a significant subset of our studied dark patterns can be detected quite reliably (F1 score of over 0.82), and future research directions may allow for improved detection of add

关键词： Dark pattern UI Analysis UI Design

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Sign2Text: Deep Learning-based Sign Language Translation System Using vision Transformers and PHI-1.5B 6

Sign2Text: Deep Learning-based Sign Language Translation Sys...

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6th ieee International conference on Artificial Intelligence in Engineering and Technology, IICAIET 2024

作者： Gadha Lekshmi, P. Francis, Rohith Sree Chitra Thirunal College of Engineering Department of Computer Science Engineering Trivandrum India

ISBN: (纸本)9798350389692

Sign language is essential for communication among deaf individuals, yet barriers persist effectively in translating its rich linguistic expressions into textual representations. The dynamic nature of signing poses a significant challenge for existing sign language recognition systems, particularly in intricate and continuous contexts. Moreover, these systems primarily cater to specific sign language dialects, neglecting the diverse linguistic landscape of sign languages globally. This paper proposes an innovative sign language translation system that addresses these challenges by leveraging recent advancements in computer vision and natural language processing. Our approach focuses on Indian Sign Language (ISL), which presents unique challenges due to its utilization of both hands. The system utilizes a vision Transformer (ViT) trained on a comprehensive video dataset to classify various sign language elements, while integrating a sophisticated language model, PHI-1.5B, to refine translated text for grammatical correctness and structural integrity. By combining ViT and PHI-1.5B, our system aims to achieve robust and contextually relevant translation of ISL gestures into textual representations. © 2024 ieee.

关键词： Deep learning

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Multi-scale Feature Fusion Extraction Structure for the Leather Defect Detection Algorithm 6

Multi-scale Feature Fusion Extraction Structure for the Leat...

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6th ieee International conference on pattern recognition and Artificial Intelligence, PRAI 2023

作者： He, Xuwen Li, Hao Huang, Zihan Xiong, Xiaorui Liu, Yifan Yao, Yuting Wuhan Polytechnic University School of Mathematics and Computer Science Wuhan China

ISBN: (纸本)9798350325485

Detection of surface defects using computer vision is a pivotal technology for achieving intelligent manufacturing. Leather products are one of the most widely traded goods in the world, and automatic identification, localization, and detection of surface defects in leather are indispensable for achieving intelligent manufacturing of leather products. This research paper proposes FG-DETR, a multi-scale feature fusion extraction structure for small target detection algorithms. Given the recent success of deep learning methods in various related fields and that certain defects and flaws in leather pictures in our dataset are relatively small. FG-DETR is based on DETR with some improvements as follows, Initially, DETR's ResNet50 network performs feature extraction on the original image and produces four scales of feature maps. Second, we apply a modified Feature Pyramid Network (FPN) to downsample and upsample the four scales of feature maps, followed by the fusion and output of a single feature map. A Gaussian kernel and output then process the fused feature maps as the final feature maps. Finally, the feature maps are merged with the position encoding information and combined into the Transformer encoder-decoder architecture, resulting in the detection outcomes through the Position-wise Feed-Forward Networks (FFN). In our leather dataset, the FG-DETR model is more effective in detecting small targets than DETR, as our leather images are large, and the original DETR is nearly ineffective for images with relatively small defects. The average detection accuracy of medium and large targets also improved, with a 7.6 percentage point improvement at AP50. The experimental findings demonstrate that the multi-scale feature fusion extraction structure significantly enhances the detection accuracy of DETR. © 2023 ieee.

关键词： computer vision

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ReMP: Rectified Metric Propagation for Few-Shot Learning

ReMP: Rectified Metric Propagation for Few-Shot Learning

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

作者： Zhao, Yang Li, Chunyuan Yu, Ping Chen, Changyou Univ Buffalo Buffalo NY 14260 USA Microsoft Res Redmond WA USA

ISBN: (纸本)9781665448994

Few-shot learning features the capability of generalizing from a few examples. In this paper, we first identify that a discriminative feature space, namely a rectified metric space, that is learned to maintain the metric consistency from training to testing, is an essential component to the success of metric-based few-shot learning. Numerous analyses indicate that a simple modification of the objective can yield substantial performance gains. The resulting approach, called rectified metric propagation (ReMP), further optimizes an attentive prototype propagation network, and applies a repulsive force to make confident predictions. Extensive experiments demonstrate that the proposed ReMP is effective and efficient, and outperforms the state of the arts on various standard few-shot learning datasets.

关键词： Training computer vision conferences Force Prototypes Performance gain Extraterrestrial measurements

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A Comparative Study of CNN and Transformer Models for Image recognition in Autonomous Driving 2

A Comparative Study of CNN and Transformer Models for Image ...

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2nd International conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Gupta, Rahul Singh, Shashank Yadav, Vandana Dhanda, Namrata Sharma, Pakhi Monad University U.P. Hapur India Department of Computer Science & Engineering S.R. Institute of Management & Technology Lucknow India Department of Computer Science & Engineering Amity University U.P. Lucknow India

ISBN: (纸本)9798331530389

An autonomous driving system requires efficient image recognition to interpret the environment, detect obstacles, and make real-time decisions. This study compares Convolutional Neural Networks (CNNs) and vision Transformers (ViTs) for image recognition tasks in autonomous driving. CNNs achieve higher accuracy (92.5% vs. 91.2%) and faster inference times (25ms vs. 35ms), making them more suitable for real-time applications. ViTs, however, perform better in challenging conditions, such as low lighting and occlusions, by capturing detailed information across entire images. The findings suggest that both architectures offer distinct advantages and could be combined to enhance the reliability and efficiency of autonomous driving systems. © 2025 ieee.

关键词： Convolutional neural networks

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