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MMVP: A Multimodal MoCap Dataset with vision and Pressure Sensors

MMVP: A Multimodal MoCap Dataset with Vision and Pressure Se...

引用

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

作者： Zhang, He Ren, Shenghao Yuan, Haolei Zhao, Jianhui Li, Fan Sun, Shuangpeng Liang, Zhenghao Yu, Tao Shen, Qiu Cao, Xun Beihang Univ Beijing Peoples R China Tsinghua Univ Beijing Peoples R China Nanjing Univ Nanjing Peoples R China Beijing Weilan Technol Co Ltd Beijing Peoples R China

ISBN: (纸本)9798350353006

Foot contact is an important cue for human motion capture, understanding, and generation. Existing datasets tend to annotate dense foot contact using visual matching with thresholding or incorporating pressure signals. However, these approaches either suffer from low accuracy or are only designed for small-range and slow motion. There is still a lack of a vision-pressure multimodal dataset with large-range and fast human motion, as well as accurate and dense foot-contact annotation. To fill this gap, we propose a Multimodal MoCap Dataset with vision and Pressure sensors, named MMVP. MMVP provides accurate and dense plantar pressure signals synchronized with RGBD observations, which is especially useful for both plausible shape estimation, robust pose fitting without foot drifting, and accurate global translation tracking. To validate the dataset, we propose an RGBD-P SMPL fitting method and also a monocular-video-based baseline framework, VP-MoCap, for human motion capture. Experiments demonstrate that our RGBD-P SMPL Fitting results significantly outperform pure visual motion capture. Moreover, VP-MoCap outperforms SOTA methods in foot-contact and global translation estimation accuracy. We believe the configuration of the dataset and the baseline frameworks will stimulate the research in this direction and also provide a good reference for MoCap applications in various domains. Project page: https://***/MMVP-Dataset/

关键词： foot contact Motion capture multimodal pressure

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Multiclass Confidence and Localization Calibration for Object Detection

Multiclass Confidence and Localization Calibration for Objec...

引用

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

作者： Pathiraja, Bimsara Gunawardhana, Malitha Khan, Muhammad Haris Mohamed Bin Zayed Univ Artificial Intelligence Abu Dhabi U Arab Emirates

ISBN: (纸本)9798350301298

Albeit achieving high predictive accuracy across many challenging computer vision problems, recent studies suggest that deep neural networks (DNNs) tend to make overconfident predictions, rendering them poorly calibrated. Most of the existing attempts for improving DNN calibration are limited to classification tasks and restricted to calibrating in-domain predictions. Surprisingly, very little to no attempts have been made in studying the calibration of object detection methods, which occupy a pivotal space in vision-based security-sensitive, and safety-critical applications. In this paper, we propose a new train-time technique for calibrating modern object detection methods. It is capable of jointly calibrating multiclass confidence and box localization by leveraging their predictive uncertainties. We perform extensive experiments on several in-domain and out-of-domain detection benchmarks. Results demonstrate that our proposed train-time calibration method consistently outperforms several baselines in reducing calibration error for both in-domain and out-of-domain predictions. Our code and models are available at https: //***/bimsarapathiraja/MCCL

关键词： vision applications and systems

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Visual Programming: Compositional visual reasoning without training

Visual Programming: Compositional visual reasoning without t...

引用

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

作者： Gupta, Tanmay Kembhavi, Aniruddha PRIOR Allen Inst AI Seattle WA 98103 USA

ISBN: (纸本)9798350301298

We present VISPROG, a neuro-symbolic approach to solving complex and compositional visual tasks given natural language instructions. VISPROG avoids the need for any task-specific training. Instead, it uses the in-context learning ability of large language models to generate python-like modular programs, which are then executed to get both the solution and a comprehensive and interpretable rationale. Each line of the generated program may invoke one of several off-the-shelf computer vision models, image processing subroutines, or python functions to produce intermediate outputs that may be consumed by subsequent parts of the program. We demonstrate the flexibility of VISPROG on 4 diverse tasks - compositional visual question answering, zero-shot reasoning on image pairs, factual knowledge object tagging, and language-guided image editing. We believe neuro-symbolic approaches like VISPROG are an exciting avenue to easily and effectively expand the scope of AI systems to serve the long tail of complex tasks that people may wish to perform.

关键词： and reasoning language vision

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Compositional Mixture Representations for vision and Text

Compositional Mixture Representations for Vision and Text

引用

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

作者： Alaniz, Stephan Federici, Marco Akata, Zeynep Univ Tubingen Tubingen Germany Max Planck Inst Informat Saarbrucken Germany Univ Amsterdam Amsterdam Netherlands

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Learning a common representation space between vision and language allows deep networks to relate objects in the image to the corresponding semantic meaning. We present a model that learns a shared Gaussian mixture representation imposing the compositionality of the text onto the visual domain without having explicit location supervision. By combining the spatial transformer with a representation learning approach we learn to split images into separately encoded patches to associate visual and textual representations in an interpretable manner. On variations of MNIST and CIFAR10, our model is able to perform weakly supervised object detection and demonstrates its ability to extrapolate to unseen combination of objects.

关键词： Representation learning Visualization Computational modeling Semantics Image retrieval Employment Object detection

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Plateau-reduced Differentiable Path Tracing

Plateau-reduced Differentiable Path Tracing

引用

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

作者： Fischer, Michael Ritschel, Tobias UCL London England

ISBN: (纸本)9798350301298

Current differentiable renderers provide light transport gradients with respect to arbitrary scene parameters. However, the mere existence of these gradients does not guarantee useful update steps in an optimization. Instead, inverse rendering might not converge due to inherent plateaus, i.e., regions of zero gradient, in the objective function. We propose to alleviate this by convolving the high-dimensional rendering function, that maps scene parameters to images, with an additional kernel that blurs the parameter space. We describe two Monte Carlo estimators to compute plateau-reduced gradients efficiently, i.e., with low variance, and show that these translate into net-gains in optimization error and runtime performance. Our approach is a straightforward extension to both black-box and differentiable renderers and enables optimization of problems with intricate light transport, such as caustics or global illumination, that existing differentiable renderers do not converge on. Our code is at ***/mfischerucl/prdpt.

关键词： vision + graphics

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SOCAR: Socially-Obtained CAR Dataset for Image recognition in the Wild

SOCAR: Socially-Obtained CAR Dataset for Image Recognition i...

引用

23rd ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Seo, Taewon Park, Kyung Ho Chung, Hyunhee SOCAR AI Res Seoul South Korea

ISBN: (纸本)9798350320565

While cars have become a significant object in computer vision applications, there are fewer spotlights on publicly-available car-related datasets. Among previously-proposed car datasets, we discover several improvement avenues. As most previous car datasets consist of web-crawled or surveillance camera-taken images, they are insufficient to illustrate various attributes, such as points of view or parts. Moreover, prior datasets primarily deal with a car model recognition task;thus, the scope of applicative studies was limited. To improve these avenues, we propose a Socially-Obtained CAR (SOCAR) dataset, a real-world car image dataset consisting of car images with more prosperous attributes. The key contributions of our study are as follows. First, under coordination with a large-scale car-sharing platform, we retrieve user-taken car images on both external and internal attributes and establish a dataset consisting of 10K images on 14 classes. Second, we design each class to represent a particular car's state;therefore, the SOCAR dataset enables the practitioners to solve various image recognition tasks such as understanding defects, dirt, or car wash. Lastly, we suggest baseline experiment results on the proposed dataset and experimentally examine the trained model effectively capture discriminative regions similar to human vision. We highly expect practitioners to use our SOCAR dataset for academic research on understanding car attributes or computer vision applications.

关键词： computer vision Image recognition Computational modeling Surveillance conferences Reproducibility of results Automobiles

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Anomaly Detection in Autonomous Driving: A Survey

Anomaly Detection in Autonomous Driving: A Survey

引用

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

作者： Bogdoll, Daniel Nitsche, Maximilian Zoellner, J. Marius FZI Res Ctr Informat Technol Karlsruhe Germany KIT Karlsruhe Inst Technol Karlsruhe Germany

ISBN: (纸本)9781665487399

Nowadays, there are outstanding strides towards a future with autonomous vehicles on our roads. While the perception of autonomous vehicles performs well under closed-set conditions, they still struggle to handle the unexpected. This survey provides an extensive overview of anomaly detection techniques based on camera, lidar, radar, multimodal and abstract object level data. We provide a systematization including detection approach, corner case level, ability for an online application, and further attributes. We outline the state-of-the-art and point out current research gaps.

关键词： computer vision Laser radar Roads conferences Radar detection Benchmark testing Cameras

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Prompt-Enhanced Multiple Instance Learning for Weakly Supervised Video Anomaly Detection

Prompt-Enhanced Multiple Instance Learning for Weakly Superv...

引用

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

作者： Chen, Junxi Li, Liang Su, Li Zha, Zheng-Jun Huang, Qingming Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Key Lab Intell Info Proc ICT Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China Univ Sci & Technol China Hefei Peoples R China Chinese Acad Sci Key Lab Safety Beijing Peoples R China

ISBN: (纸本)9798350353006

Weakly-supervised Video Anomaly Detection (wVAD) aims to detect frame-level anomalies using only video-level labels in training. Due to the limitation of coarse-grained labels, Multi-Instance Learning (MIL) is prevailing in wVAD. However, MIL suffers from insufficiency of binary supervision to model diverse abnormal patterns. Besides, the coupling between abnormality and its context hinders the learning of clear abnormal event boundary. In this paper, we propose prompt-enhanced MIL to detect various abnormal events while ensuring clear event boundaries. Concretely, we design the abnormal-aware prompts by using abnormal class annotations together with learnable prompt, which can incorporate semantic priors into video features dynamically. The detector can utilize the semantic-rich features to capture diverse abnormal patterns. In addition, normal context prompt is introduced to amplify the distinction between abnormality and its context, facilitating the generation of clear boundary. With the mutual enhancement of abnormal-aware and normal context prompt, the model can construct discriminative representations to detect divergent anomalies without ambiguous event boundaries. Extensive experiments demonstrate our method achieves SOTA performance on three public bench-marks. The code is available at https://***/Junxi-Chen/PE-MIL.

关键词： computer vision Video Anomaly Detection

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Towards Explaining Image-Based Distribution Shifts

Towards Explaining Image-Based Distribution Shifts

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

作者： Kulinski, Sean Inouye, David I. Purdue Univ Sch Elect & Comp Engn W Lafayette IN 47907 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Distribution shift can have fundamental consequences such as signaling a change in the operating environment or significantly reducing the accuracy of downstream models. Thus, understanding such distribution shifts is critical for examining and hopefully mitigating the effect of such a shift. Most prior work has focused on either natively handling distribution shift (e.g., Domain Generalization) or merely detecting a shift while assuming any detected shift can be understood and handled appropriately by a human operator. For the latter, we hope to aid in these manual mitigation tasks by explaining the distribution shift to an operator. To this end, we suggest two methods: providing a set of interpretable mappings from the original distribution to the shifted one or providing a set of distributional counterfactual examples. We provide preliminary experiments on these two methods, and discuss important concepts and challenges for moving towards a better understanding of image-based distribution shifts.

关键词： computer vision conferences pattern recognition Task analysis

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OutfitGAN: Learning Compatible Items for Generative Fashion Outfits

OutfitGAN: Learning Compatible Items for Generative Fashion ...

引用

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

作者： Moosaei, Maryam Lin, Yusan Akhazhanov, Ablaikhan Chen, Huiyuan Wang, Fei Yang, Hao Visa Res San Francisco CA 94158 USA Univ Calif Los Angeles Los Angeles CA 90024 USA Nazarbayev Univ Astana Kazakhstan

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Fashion-on-demand is becoming an important concept for fashion industries. Many attempts have been made to leverage machine learning methods to generate fashion designs tailored to customers' tastes. However, how to assemble items together (e.g., compatibility) is crucial in designing high-quality outfits for synthesis images. Here we propose a fashion generation model, named OutfitGAN, which contains two core modules: a Generative Adversarial Network and a Compatibility Network. The generative module is able to generate new realistic high quality fashion items from a specific category, while the compatibility network ensures reasonable compatibility among all items. The experimental results show the superiority of our OutfitGAN.

关键词： Industries computer vision conferences Machine learning Generative adversarial networks pattern recognition

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