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OutfitTransformer: Outfit Representations for Fashion Recommendation

OutfitTransformer: Outfit Representations for Fashion Recomm...

引用

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

作者： Sarkar, Rohan Bodla, Navaneeth Vasileva, Mariya Lin, Yen-Liang Beniwal, Anurag Lu, Alan Medioni, Gerard Purdue Univ W Lafayette IN 47907 USA Amazon Seattle WA 98109 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Predicting outfit compatibility and retrieving complementary items are critical components for a fashion recommendation system. We present a scalable framework, Out-fitTransformer, that learns compatibility of the entire out-fit and supports large-scale complementary item retrieval. We model outfits as an unordered set of items and leverage self-attention mechanism to learn the relationships between items. We train the framework using a proposed set-wise outfit ranking loss to generate a target item embedding given an outfit, and a target item specification. The generated target item embedding is then used to retrieve compatible items that match the outfit. Experimental results demonstrate that our approach outperforms state-of-the-art methods on compatibility prediction, fill-in-the-blank, and complementary item retrieval tasks.

关键词： computer vision conferences Task analysis Recommender systems pattern matching

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Cross-Domain Multi-task Learning for Object Detection and Saliency Estimation

Cross-Domain Multi-task Learning for Object Detection and Sa...

引用

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

作者： Khattar, Apoorv Hegde, Srinidhi Hebbalaguppe, Ramya TCS Res Mumbai Maharashtra India

ISBN: (纸本)9781665448994

Multi-task learning (MTL) is a learning paradigm that aims at joint optimization of multiple tasks using a single neural network for better performance and generalization. In practice, MTL rests on the inherent assumption of availability of common datasets with ground truth labels for each of the downstream tasks. However, collecting such a common annotated dataset is laborious for complex computer vision tasks such as the saliency estimation which would require the eye fixation points as the ground truth data. To this end, we propose a novel MTL framework in the absence of common annotated dataset for joint estimation of important downstream tasks in computer vision - object detection and saliency estimation. Unlike many state-of-the-art methods, that rely on common annotated datasets for training, we consider the annotations from different datasets for jointly training different tasks, calling this setting as cross-domain MTL. We adapt MUTAN [3] framework to fuse features from different datasets to learn domain invariant features capturing the relatedness of different tasks. We demonstrate the improvement in the performance and generalizability of our MTL architecture. We also show that the proposed MTL network offers a 13% reduction in memory footprint due to parameter sharing between the related tasks.

关键词： Training computer vision Neural networks Estimation Object detection computer architecture pattern recognition

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Superpixel Estimation for Hyperspectral Imagery

Superpixel Estimation for Hyperspectral Imagery

引用

27th ieee conference on computer vision and pattern recognition (CVPR)

作者： Massoudifar, Pegah Rangarajan, Anand Gader, Paul Univ Florida Dept CISE Gainesville FL 32611 USA

ISBN: (纸本)9781479943098

In the past decade, there has been a growing need for machine learning and computer vision components (segmentation, classification) in the hyperspectral imaging domain. Due to the complexity and size of hyperspectral imagery and the enormous number of wavelength channels, the need for combining compact representations with image segmentation and superpixel estimation has emerged in this area. Here, we present an approach to superpixel estimation in hyperspectral images by adapting the well known UCM approach to hyperspectral volumes. This approach benefits from the channel information at each pixel of the hyperspectral image while obtaining a compact representation of the hyperspectral volume using principal component analysis. Our experimental evaluation demonstrates that the additional information of spectral channels will substantially improve superpixel estimation from a single "monochromatic" channel. Furthermore, superpixel estimation performed on the compact hyperspectral representation outperforms the same when executed on the entire volume.

关键词： hyperspectral imagery information channels image segmentation Compact Compact pattern pixel extra message principal components analysis wavelengths

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Temporal Segmentation and Activity Classification from First-person Sensing

Temporal Segmentation and Activity Classification from First...

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ieee-computer-society conference on computer vision and pattern recognition workshops

作者： Spriggs, Ekaterina H. De La Torre, Fernando Hebert, Martial Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9781424439942

Temporal segmentation of human motion into actions is central to the understanding and building of computational models of human motion and activity recognition. Several issues contribute to the challenge of temporal segmentation and classification of human motion. These include the large variability in the temporal scale and Periodicity of human actions, the complexity of representing articulated motion, and the exponential nature of all possible movement combinations. We provide initial results from investigating two distinct problems - classification of the overall task being performed, and the more difficult problem of classifying individual frames over time into specific actions. We explore first-person sensing through a wearable camera and Inertial Measurement Units (IMUs)for temporally segmenting human motion into actions and performing activity classification in the context of cooking and recipe preparation in a natural environment. We present baseline results for supervised and unsupervised temporal segmentation, and recipe recognition in the CMU-Multimodal activity database (CMU-MMAC).

关键词： computer vision

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Trusting the computer in computer vision: A Privacy-Affirming Framework 30

Trusting the Computer in Computer Vision: A Privacy-Affirmin...

引用

30th ieee/CVF conference on computer vision and pattern recognition workshops (CVPRW)

作者： Chen, Andrew Tzer-Yeu Biglari-Abhari, Morteza Wang, Kevin I-Kai Univ Auckland Dept Elect & Comp Engn Embedded Syst Res Grp Auckland New Zealand

ISBN: (纸本)9781538607336

The use of surveillance cameras continues to increase, ranging from conventional applications such as law enforcement to newer scenarios with looser requirements such as gathering business intelligence. Humans still play an integral part in using and interpreting the footage from these systems, but are also a significant factor in causing unintentional privacy breaches. As computer vision methods continue to improve, we argue in this position paper that system designers should reconsider the role of machines in surveillance, and how automation can be used to help protect privacy. We explore this by discussing the impact of the human-in-the-loop, the potential for using abstraction and distributed computing to further privacy goals, and an approach for determining when video footage should be hidden from human users. We propose that in an ideal surveillance scenario, a privacy-affirming framework causes collected camera footage to be processed by computers directly, and never shown to humans. This implicitly requires humans to establish trust, to believe that computer vision systems can generate sufficiently accurate results without human supervision, so that if information about people must be gathered, unintentional data collection is mitigated as much as possible.

关键词： Estimation Field programmable gate arrays OFDM Modulation Filtration Clocks Signal processing algorithms

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An Adversarial Approach for Explaining the Predictions of Deep Neural Networks

An Adversarial Approach for Explaining the Predictions of De...

引用

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

作者： Rahnama, Arash Tseng, Andrew Modzy Vienna VA 22182 USA

ISBN: (纸本)9781665448994

Machine learning models have been successfully applied to a wide range of applications including computer vision, natural language processing, and speech recognition. A successful implementation of these models however, usually relies on deep neural networks (DNNs) which are treated as opaque black-box systems due to their incomprehensible complexity and intricate internal mechanism. In this work, we present a novel algorithm for explaining the predictions of a DNN using adversarial machine learning. Our approach identifies the relative importance of input features in relation to the predictions based on the behavior of an adversarial attack on the DNN. Our algorithm has the advantage of being fast, consistent, and easy to implement and interpret. We present our detailed analysis that demonstrates how the behavior of an adversarial attack, given a DNN and a task, stays consistent for any input test data point proving the generality of our approach. Our analysis enables us to produce consistent and efficient explanations. We illustrate the effectiveness of our approach by conducting experiments using a variety of DNNs, tasks, and datasets. Finally, we compare our work with other well-known techniques in the current literature.

关键词： Deep learning computer vision Machine learning algorithms Computational modeling conferences Speech recognition Prediction algorithms

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CorrGAN: Input Transformation Technique Against Natural Corruptions

CorrGAN: Input Transformation Technique Against Natural Corr...

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

作者： Haque, Mirazul Budnik, Christof J. Yang, Wei UT Dallas Richardson TX 75080 USA Siemens Corp Technol Princeton NJ USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Because of the increasing accuracy of Deep Neural Networks (DNNs) on different tasks, a lot of real times systems are utilizing DNNs. These DNNs are vulnerable to adversarial perturbations and corruptions. Specifically, natural corruptions like fog, blur, contrast etc can affect the prediction of DNN in an autonomous vehicle. In real time, these corruptions are needed to be detected and also the corrupted inputs are needed to be de-noised to be predicted correctly. In this work, we propose CorrGAN approach, which can generate benign input when a corrupted input is provided. In this framework, we train Generative Adversarial Network (GAN) with novel intermediate output-based loss function. The GAN can denoise the corrupted input and generate benign input. Through experimentation, we show that up to 75.2% of the corrupted misclassified inputs can be classified correctly by DNN using CorrGAN.

关键词： Deep learning computer vision Perturbation methods conferences Neural networks computer architecture Generative adversarial networks

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Revisiting the Receptive Field of Conv-GRU in DROID-SLAM

Revisiting the Receptive Field of Conv-GRU in DROID-SLAM

引用

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

作者： Bangunharcana, Antyanta Kim, Soohyun Kim, Kyung-Soo Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

This work focuses on improving the Conv-GRU-based optical flow update within a DROID-SLAM framework. Prior optical flow models typically follow a UNet or coarse-to-fine architecture in order to extract long-range cross-correlation and context cues. This helps flow estimation in the presence of large motion and challenging image regions, e.g., textureless regions. We propose modifications to the Conv-GRU module which follows the rationale of these prior models by integrating (Atrous) Spatial Pyramid Pooling and global self-attention into the Conv-GRU block. By enlarging the receptive field through the aforementioned modifications, the model is able to integrate information from a larger context window, thus improving the robustness even when given inputs that comprise challenging image regions. We show empirically through extensive experiments the gain in accuracy through these modifications.

关键词： computer vision Simultaneous localization and mapping conferences Computational modeling Estimation computer architecture Robustness

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Temporal Driver Action Localization using Action Classification Methods

Temporal Driver Action Localization using Action Classificat...

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

作者： Alyahya, Munirah Alghannam, Shahad Alhussan, Taghreed Saudi Technol & Secur Comprehens Control Co Riyadh Saudi Arabia

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Driver distraction recognition is an essential computer vision task that can play a key role in increasing traffic safety and reducing traffic accidents. In this paper, we propose a temporal driver action localization (TDAL) framework for classifying driver distraction actions, as well as identifying the start and end time of a given driver action. The TDAL framework consists of three stages: preprocessing, 'which takes untrimmed video as input and generates multiple clips;action classification, which classifies the clips;and finally, the classifier output is sent to the temporal action localization to generate the start and end times of the distracted actions. The proposed framework achieves an F1 score of 27.06% on Track 3A2 dataset of NVIDIA AI City 2022 Challenge. The findings show that the TDAL framework contributes to fine-grained driver distraction recognition and paves the way for the development of smart and safe transportation. Code will be available soon.

关键词： Location awareness computer vision Urban areas Data preprocessing Transportation Classification algorithms Safety

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New Techniques for Preserving Global Structure and Denoising with Low Information Loss in Single-Image Super-Resolution 31

New Techniques for Preserving Global Structure and Denoising...

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

作者： Bei, Yijie Damian, Alex Hu, Shijia Menon, Sachit Ravi, Nikhil Rudin, Cynthia Duke Univ Durham NC 27706 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

This work identifies and addresses two important technical challenges in single-image super-resolution: (1) how to upsample an image without magnifying noise and (2) how to preserve large scale structure when upsampling. We summarize the techniques we developed for our second place entry in Track 1 (Bicubic Downsampling), seventh place entry in Track 2 (Realistic Adverse Conditions), and seventh place entry in Track 3 (Realistic difficult) in the 2018 NTIRE Super-Resolution Challenge. Furthermore, we present new neural network architectures that specifically address the two challenges listed above: denoising and preservation of large-scale structure.

关键词： Image resolution Noise reduction Training Task analysis Neural networks computer architecture Knowledge engineering

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