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Symmetric Network with Spatial Relationship Modeling for Natural Language-based Vehicle Retrieval

Symmetric Network with Spatial Relationship Modeling for Nat...

引用

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

作者： Zhao, Chuyang Chen, Haobo Zhang, Wenyuan Chen, Junru Zhang, Sipeng Li, Yadong Li, Boxun MEGVII Technol Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Natural language (NL) based vehicle retrieval aims to search specific vehicle given text description. Different from the image-based vehicle retrieval, NL-based vehicle retrieval requires considering not only vehicle appearance, but also surrounding environment and temporal relations. In this paper, we propose a Symmetric Network with Spatial Relationship Modeling (SSM) method for NL-based vehicle retrieval. Specifically, we design a symmetric network to learn the unified cross-modal representations between text descriptions and vehicle images, where vehicle appearance details and vehicle trajectory global information are preserved. Besides, to make better use of location information, we propose a spatial relationship modeling methods to take surrounding environment and mutual relationship between vehicles into consideration. The qualitative and quantitative experiments verify the effectiveness of the proposed method. We achieve 43.92% MRR accuracy on the test set of the 6th AI City Challenge on natural language-based vehicle retrieval track, yielding the 4th place on the public leaderboard.

关键词： Data privacy Visualization conferences Urban areas Linguistics Trajectory pattern recognition

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Class-wise Thresholding for Robust Out-of-Distribution Detection

Class-wise Thresholding for Robust Out-of-Distribution Detec...

引用

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

作者： Guarrera, Matteo Jin, Baihong Lin, Tung-Wei Zuluaga, Maria A. Chen, Yuxin Sangiovanni-Vincentelli, Alberto Univ Calif Berkeley Berkeley CA 94720 USA EURECOM Biot France Univ Chicago Chicago IL 60637 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We consider the problem of detecting Out-of-Distribution (OoD) input data when using deep neural networks, and we propose a simple yet effective way to improve the robustness of several popular OoD detection methods against label shift. Our work is motivated by the observation that most existing OoD detection algorithms consider all training/test data as a whole, regardless of which class entry each input activates (inter-class differences). Through extensive experimentation, we have found that such practice leads to a detector whose performance is sensitive and vulnerable to label shift. To address this issue, we propose a class-wise thresholding scheme that can apply to most existing OoD detection algorithms and can maintain similar OoD detection performance even in the presence of label shift in the test distribution.

关键词： Training Deep learning computer vision conferences Neural networks Training data Detectors

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Reasoning with Multi-Structure Commonsense Knowledge in Visual Dialog

Reasoning with Multi-Structure Commonsense Knowledge in Visu...

引用

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

作者： Zhang, Shunyu Jiang, Xiaoze Yang, Zequn Wan, Tao Qin, Zengchang Beihang Univ Sch ASEE Intelligent Comp & Machine Learning Lab Beijing Peoples R China Beihang Univ Beijing Adv Innovat Ctr Biomed Engn Sch BSME Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Visual Dialog requires an agent to engage in a conversation with humans grounded in an image. Many studies on Visual Dialog focus on the understanding of the dialog history or the content of an image, while a considerable amount of commonsense-required questions are ignored. Handling these scenarios depends on logical reasoning that requires commonsense priors. How to capture relevant commonsense knowledge complementary to the history and the image remains a key challenge. In this paper, we propose a novel model by Reasoning with Multi-structure Commonsense Knowledge (RMK). In our model, the external knowledge is represented with sentence-level facts and graph-level facts, to properly suit the scenario of the composite of dialog history and image. On top of these multi-structure representations, our model can capture relevant knowledge and incorporate them into the vision and semantic features, via graph-based interaction and transformer-based fusion. Experimental results and analysis on VisDial v1.0 and VisDialCK datasets show that our proposed model effectively outperforms comparative methods.

关键词： Visualization Fuses Semantics Knowledge based systems Oral communication Transformers pattern recognition

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Strain Detection based on Breath and Motion Features Obtained by a Force Sensor for Smart Toilet Systems

Strain Detection based on Breath and Motion Features Obtaine...

引用

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

作者： Akaho, Rina Yoshioka, Mototaka Panasonic Ind Corp Osaka Japan

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Aging people may be prone to accidents in bathrooms and toilets. The detection of strain motion for a smart toilet application has not been studied sufficiently. In this paper, we propose a method for strain detection from a force sensor placed on a toilet seat for a smart toilet healthcare application. The method first extracts breath and motion features that are assumed to be key components for the strain detection. The method then learns the discriminator model based on the random forest classifier using the aforementioned features. Finally, the method recognizes actions in the toilet room. There were five detection actions: seating, taking up toilet paper, wiping bottom, which are normal actions when sitting on a toilet seat, and strain actions (strong and weak). An experiment with 19 subjects was also conducted. Compared with a microwave sensor-based recognition, which is a conventional method (accuracy = 61.6%), our method was able to recognize the actions with high accuracy of 80.2% (significant test: T = 12.7, P < 0.01) in the experiment. Our strain detection method has the potential to be used as a smart toilet system to prevent blood pressure elevation and collapse caused by strain in the future.

关键词： conferences Windings Time series analysis Medical services Feature extraction Microwave theory and techniques pattern recognition

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Leveraging Unlabeled Data for Sketch-based Understanding

Leveraging Unlabeled Data for Sketch-based Understanding

引用

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

作者： Morales, Javier Murrugarra-Llerena, Nils Saavedra, Jose M. Univ Chile Dept Comp Sci Santiago Chile Weber State Univ Dept Comp Sci Ogden UT 84408 USA Univ Los Andes Santiago Chile

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Sketch-based understanding is a critical component of human cognitive learning and is a primitive communication means between humans. This topic has recently attracted the interest of the computer vision community as sketching represents a powerful tool to express static objects and dynamic scenes. Unfortunately, despite its broad application domains, the current sketch-based models strongly rely on labels for supervised training, ignoring knowledge from unlabeled data, thus limiting the underlying generalization and the applicability. Therefore, we present a study about the use of unlabeled data to improve a sketch-based model. To this end, we evaluate variations of VAE and semi-supervised VAE, and present an extension of BYOL to deal with sketches. Our results show the superiority of sketch-BYOL, which outperforms other self-supervised approaches increasing the retrieval performance for known and unknown categories. Furthermore, we show how other tasks can benefit from our proposal.

关键词： Training computer vision TV Limiting Image edge detection Image retrieval Data models

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Cluster-to-adapt: Few Shot Domain Adaptation for Semantic Segmentation across Disjoint Labels

Cluster-to-adapt: Few Shot Domain Adaptation for Semantic Se...

引用

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

作者： Kalluri, Tarun Chandraker, Manmohan Univ Calif San Diego La Jolla CA 92093 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Domain adaptation for semantic segmentation across datasets consisting of the same categories has seen several recent successes. However, a more general scenario is when the source and target datasets correspond to non-overlapping label spaces. For example, categories in segmentation datasets change vastly depending on the type of environment or application, yet share many valuable semantic relations. Existing approaches based on feature alignment or discrepancy minimization do not take such category shift into account. In this work, we present Cluster-to-Adapt (C2A), a computationally efficient clustering-based approach for domain adaptation across segmentation datasets with completely different, but possibly related categories. We show that such a clustering objective enforced in a transformed feature space serves to automatically select categories across source and target domains that can be aligned for improving the target performance, while preventing negative transfer for unrelated categories. We demonstrate the effectiveness of our approach through experiments on the challenging problem of outdoor to indoor adaptation for semantic segmentation in few-shot as well as zero-shot settings, with consistent improvements in performance over existing approaches and baselines in all cases.

关键词： Training Image segmentation computer vision conferences Semantics Minimization pattern recognition

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PhoneDepth: A Dataset for Monocular Depth Estimation on Mobile Devices

PhoneDepth: A Dataset for Monocular Depth Estimation on Mobi...

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

作者： Benavides, Fausto Tapia Ignatov, Andrey Timofte, Radu Swiss Fed Inst Technol Zurich Switzerland JMU Wurzburg Wurzburg Germany

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Monocular depth estimation has been studied as a classic and learning based computer vision problem for decades. However, little attention received the efficiency and the deployment of methods on mobile hardware. All publicly available datasets have severe limitations related to their applicability to camera data captured with real mobile devices. For instance, the main issues with current datasets include (but not exhaustively) low quality of images due the cameras or collection methods, domain specifically generated datasets as is the case for autonomous driving, small number of samples, sparse depthmaps, etc. In response, we introduce PhoneDepth, a novel dataset that aims to take advantage of modern phones hardware and professional stereo cameras. Depthmaps are acquired from three sources: Time of Flight sensor, Dual Pixel sensor and stereo camera;while the images correspond to mobile phone photos. We prove its high value by training neural networks with multiple depth supervision, fine-tuning on other datasets and for depth refinement. Along with the dataset we present benchmark models and a toolbox to facilitate the dataset usage.

关键词： Training computer vision Three-dimensional displays Estimation Cameras Mobile handsets Hardware

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BEHAVIOR vision Suite: Customizable Dataset Generation via Simulation

BEHAVIOR Vision Suite: Customizable Dataset Generation via S...

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

作者： Ge, Yunhao Tang, Yihe Xu, Jiashu Gokmen, Cem Li, Chengshu Ai, Wensi Martinez, Benjamin Jose Aydin, Arman Anvari, Mona Chakravarthy, Ayush K. Yu, Hong-Xing Wong, Josiah Srivastava, Sanjana Lee, Sharon Zhang, Shengxin Itti, Laurent Li, Yunzhu Martin-Martins, Roberto Liu, Miao Zhang, Pengchuan Zhang, Ruohan Fei-Fei, Li Wu, Jiajun Stanford Univ Stanford CA 94305 USA Univ Southern Calif Los Angeles CA 90007 USA Harvard Univ Cambridge MA 02138 USA Meta GenAI Menlo Pk CA USA Meta FAIR Menlo Pk CA USA Univ Texas Austin Austin TX USA Univ Illinois Urbana IL USA

ISBN: (纸本)9798350353006

The systematic evaluation and understanding of computer vision models under varying conditions require large amounts of data with comprehensive and customized labels, which real-world vision datasets rarely satisfy. While current synthetic data generators offer a promising alternative, particularly for embodied AI tasks, they often fall short for computer vision tasks due to low asset and rendering quality, limited diversity, and unrealistic physical properties. We introduce the BEHAVIOR vision Suite (BVS), a set of tools and assets to generate fully customized synthetic data for systematic evaluation of computer vision models, based on the newly developed embodied AI benchmark, BEHAVIOR-1K. BVS supports a large number of adjustable parameters at the scene level (e.g., lighting, object placement), the object level (e.g., joint configuration, attributes such as "filled" and "folded"), and the camera level (e.g., field of view, focal length). Researchers can arbitrarily vary these parameters during data generation to perform controlled experiments. We showcase three example application scenarios: systematically evaluating the robustness of models across different continuous axes of domain shift, evaluating scene understanding models on the same set of images, and training and evaluating simulation-to-real transfer for a novel vision task: unary and binary state prediction. Project website: https://***/

关键词： benchmarking embodied AI simulation synthetic data generation

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Guiding Attention using Partial-Order Relationships for Image Captioning

Guiding Attention using Partial-Order Relationships for Imag...

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

作者： Popattia, Murad Rafi, Muhammad Qureshi, Rizwan Nawaz, Shah Natl Univ Comp & Emerging Sci Karachi Pakistan Hamad Bin Khalifa Univ Doha Qatar Ist Italiano Tecnol IIT Pattern Anal & Comp Vis PAVIS Genoa Italy DESY Hamburg Germany

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The use of attention models for automated image captioning has enabled many systems to produce accurate and meaningful descriptions for images. Over the years, many novel approaches have been proposed to enhance the attention process using different feature representations. In this paper, we extend this approach by creating a guided attention network mechanism, that exploits the relationship between the visual scene and text-descriptions using spatial features from the image, high-level information from the topics, and temporal context from caption generation, which are embedded together in an ordered embedding space. A pairwise ranking objective is used for training this embedding space which allows similar images, topics and captions in the shared semantic space to maintain a partial order in the visual-semantic hierarchy and hence, helps the model to produce more visually accurate captions. The experimental results based on MSCOCO dataset shows the competitiveness of our approach, with many state-of-the-art models on various evaluation metrics.

关键词： Training Measurement Visualization computer vision conferences Semantics computer architecture

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The Effect of Improving Annotation Quality on Object Detection Datasets: A Preliminary Study

The Effect of Improving Annotation Quality on Object Detecti...

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

作者： Ma, Jiaxin Ushiku, Yoshitaka Sagara, Miori OMRON SINIC X Corp Tokyo Japan Baobab Inc Tokyo Japan

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In this study, we partially reannotate conventional benchmark datasets for object detection and check whether there is performance improvement/drop compared with the original annotations. Recent studies on the annotation qualities of ImageNet for image classification revealed some issues of how to associate only a single label to each image accurately. Object detection, on the other hand, should have other nontrivial issues because there are multiple objects in a single image, and realizing consistency among bounding boxes is challenging. A team of professional annotators was formed for MS COCO and Google Open Images datasets. To realize highly-consistent annotations, we prepared carefully designed guidelines for each category and selected quality inspectors who checked the annotation quality of each annotator. Finally, we applied conventional object detection methods for reannotated parts of each dataset. We found mixed results: whether the performance dropped or improved depended on each category and dataset.

关键词： Annotations conferences Object detection Machine learning Benchmark testing Internet pattern recognition

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