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Efficient Space-time Video Super Resolution using Low-Resolution Flow and Mask Upsampling

Efficient Space-time Video Super Resolution using Low-Resolu...

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

作者： Dutta, Saikat Shah, Nisarg A. Mittal, Anurag IIT Madras Chennai Tamil Nadu India IIT Jodhpur Karwar Rajasthan India

ISBN: (纸本)9781665448994

This paper explores an efficient solution for Space-time Super-Resolution, aiming to generate High-resolution Slow-motion videos from Low Resolution and Low Frame rate videos. A simplistic solution is the sequential running of Video Super Resolution and Video Frame interpolation models. However, this type of solutions are memory inefficient, have high inference time, and could not make the proper use of space-time relation property. To this extent, we first interpolate in LR space using quadratic modeling. Input LR frames are super-resolved using a state-of-the-art Video Super-Resolution method. Flowmaps and blending mask which are used to synthesize LR interpolated frame is reused in HR space using bilinear upsampling. This leads to a coarse estimate of HR intermediate frame which often contains artifacts along motion boundaries. We use a refinement network to improve the quality of HR intermediate frame via residual learning. Our model is lightweight and performs better than current state-of-the-art models in REDS STSR Validation set.

关键词： Interpolation computer vision conferences Superresolution Refining pattern recognition Videos

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Traffic Video Event Retrieval via Text Query using Vehicle Appearance and Motion Attributes

Traffic Video Event Retrieval via Text Query using Vehicle A...

引用

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

作者： Tien-Phat Nguyen Ba-Thinh Tran-Le Xuan-Dang Thai Nguyen, Tam, V Do, Minh N. Minh-Triet Tran Univ Sci Ho Chi Minh City Vietnam Vietnam Natl Univ Ho Chi Minh City Vietnam John von Neumann Inst Ho Chi Minh City Vietnam Univ Dayton Dayton OH 45469 USA Univ Illinois Urbana IL USA

ISBN: (纸本)9781665448994

Traffic event retrieval is one of the important tasks for intelligent traffic system management. To find accurate candidate events in traffic videos corresponding to a specific text query, it is necessary to understand the text query's attributes, represent the visual and motion attributes of vehicles in videos, and measure the similarity between them. Thus we propose a promising method for vehicle event retrieval from a natural-language-based specification. We utilize both appearance and motion attributes of a vehicle and adapt the COOT model to evaluate the semantic relationship between a query and a video track. Experiments with the test dataset of Track 5 in AI City Challenge 2021 show that our method is among the top 6 with a score of 0.1560.

关键词： Visualization computer vision Tracking conferences Urban areas Semantics pattern recognition

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A Strong Baseline for Vehicle Re-Identification

A Strong Baseline for Vehicle Re-Identification

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

作者： Huynh, Su, V Nguyen, Nam H. Nguyen, Ngoc T. Nguyen, Vinh T. Q. Huynh, Chau Chuong Nguyen Cybercore AI Ho Chi Minh City Vietnam

ISBN: (纸本)9781665448994

Vehicle Re-Identification (Re-ID) aims to identify the same vehicle across different cameras, hence plays an important role in modern traffic management systems. The technical challenges require the algorithms must be robust in different views, resolution, occlusion and illumination conditions. In this paper, we first analyze the main factors hindering the Vehicle Re-ID performance. We then present our solutions, specifically targeting the dataset Track 2 of the 5th AI City Challenge, including (1) reducing the domain gap between real and synthetic data, (2) network modification by stacking multi heads with attention mechanism, (3) adaptive loss weight adjustment. Our method achieves 61.34% mAP on the private CityFlow testset without using external dataset or pseudo labeling, and outperforms all previous works at 87.1% mAP on the Veri benchmark. The code is available at https://***/cybercore-co-ltd/track2_aicity_2021.

关键词： Training computer vision Target tracking Head conferences Urban areas Stacking

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All You Can Embed: Natural Language based Vehicle Retrieval with Spatio-Temporal Transformers

All You Can Embed: Natural Language based Vehicle Retrieval ...

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

作者： Scribano, Carmelo Sapienza, Davide Franchini, Giorgia Verucchi, Micaela Bertogna, Marko Univ Modena & Reggio Emilia Modena Italy Univ Ferrara Ferrara Italy Univ Parma Parma Italy

ISBN: (纸本)9781665448994

Combining Natural Language with vision represents a unique and interesting challenge in the domain of Artificial Intelligence. The AI City Challenge Track 5 for Natural Language-Based Vehicle Retrieval focuses on the problem of combining visual and textual information, applied to a smart-city use case. In this paper, we present All You Can Embed (AYCE), a modular solution to correlate single-vehicle tracking sequences with natural language. The main building blocks of the proposed architecture are (i) BERT to provide an embedding of the textual descriptions, (ii) a convolutional backbone along with a Transformer model to embed the visual information. For the training of the retrieval model, a variation of the Triplet Margin Loss is proposed to learn a distance measure between the visual and language embeddings. The code is publicly available at https://***/cscribano/AYCE_2021.

关键词： Training Visualization Target tracking Natural languages Urban areas computer architecture Loss measurement

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Differentiable Rendering-based Pose-Conditioned Human Image Generation

Differentiable Rendering-based Pose-Conditioned Human Image ...

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

作者： Horiuchi, Yusuke Simo-Serra, Edgar Iizuka, Satoshi Ishikawa, Hiroshi Waseda Univ Tokyo Japan Univ Tsukuba Tsukuba Ibaraki Japan

ISBN: (纸本)9781665448994

Conditional human image generation, or generation of human images with specified pose based on one or more reference images, is an inherently ill-defined problem, as there can be multiple plausible appearance for parts that are occluded in the reference. Using multiple images can mitigate this problem while boosting the performance. In this work, we introduce a differentiable vertex and edge renderer for incorporating the pose information to realize human image generation conditioned on multiple reference images. The differentiable renderer has parameters that can be jointly optimized with other parts of the system to obtain better results by learning more meaningful shape representation of human pose. We evaluate our method on the Market-1501 and DeepFashion datasets and comparison with existing approaches validates the effectiveness of our approach.

关键词： Heating systems computer vision Image synthesis Shape Image edge detection conferences Training data

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InfoScrub: Towards Attribute Privacy by Targeted Obfuscation

InfoScrub: Towards Attribute Privacy by Targeted Obfuscation

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

作者： Wang, Hui-Po Orekondy, Tribhuvanesh Fritz, Mario CISPA Helmholtz Ctr Informat Secur Saarbrucken Germany Max Planck Inst Informat Saarland Informat Campus Saarbrucken Germany

ISBN: (纸本)9781665448994

Personal photos of individuals when shared online, apart from exhibiting a myriad of memorable details, also reveals a wide range of private information and potentially entails privacy risks (e.g., online harassment, tracking). To mitigate such risks, it is crucial to study techniques that allow individuals to limit the private information leaked in visual data. We tackle this problem in a novel image obfuscation framework: to maximize entropy on inferences over targeted privacy attributes, while retaining image fidelity. We approach the problem based on an encoder-decoder style architecture, with two key novelties: (a) introducing a discriminator to perform bi-directional translation simultaneously from multiple unpaired domains;(b) predicting an image interpolation which maximizes uncertainty over a target set of attributes. We find our approach generates obfuscated images faithful to the original input images and additionally increases uncertainty by 6.2x (or up to 0.85 bits) over the non-obfuscated counterparts.

关键词： Privacy Visualization Interpolation computer vision Uncertainty Target tracking conferences

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Contrastive Learning for Sports Video: Unsupervised Player Classification

Contrastive Learning for Sports Video: Unsupervised Player C...

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

作者： Koshkina, Maria Pidaparthy, Hemanth Elder, James H. York Univ Toronto ON Canada

ISBN: (纸本)9781665448994

We address the problem of unsupervised classification of players in a team sport according to their team affiliation, when jersey colours and design are not known a priori. We adopt a contrastive learning approach in which an embedding network learns to maximize the distance between representations of players on different teams relative to players on the same team, in a purely unsupervised fashion, without any labelled data. We evaluate the approach using a new hockey dataset and find that it outperforms prior unsupervised approaches by a substantial margin, particularly for real-time application when only a small number of frames are available for unsupervised learning before team assignments must be made. Remarkably, we show that our contrastive method achieves 94% accuracy after unsupervised training on only a single frame, with accuracy rising to 97% within 500 frames (17 seconds of game time). We further demonstrate how accurate team classification allows accurate team-conditional heat maps of player positioning to be computed.

关键词： Training Heating systems computer vision Image color analysis conferences Games Real-time systems

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Boosting Adversarial Robustness using Feature Level Stochastic Smoothing

Boosting Adversarial Robustness using Feature Level Stochast...

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

作者： Addepalli, Sravanti Jain, Samyak Sriramanan, Gaurang Babu, R. Venkatesh Indian Inst Sci Video Analyt Lab Dept Computat & Data Sci Bangalore Karnataka India

ISBN: (纸本)9781665448994

Advances in adversarial defenses have led to a significant improvement in the robustness of Deep Neural Networks. However, the robust accuracy of present state-of-the-art defenses is far from the requirements in critical applications such as robotics and autonomous navigation systems. Further, in practical use cases, network prediction alone might not suffice, and assignment of a confidence value for the prediction can prove crucial. In this work, we propose a generic method for introducing stochasticity in the network predictions, and utilize this for smoothing decision boundaries and rejecting low confidence predictions, thereby boosting the robustness on accepted samples. The proposed Feature Level Stochastic Smoothing based classification also results in a boost in robustness without rejection over existing adversarial training methods. Finally, we combine the proposed method with adversarial detection methods, to achieve the benefits of both approaches.

关键词： Training Deep learning computer vision Smoothing methods conferences Boosting Feature extraction

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Localized Triplet Loss for Fine-grained Fashion Image Retrieval

Localized Triplet Loss for Fine-grained Fashion Image Retrie...

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

作者： D'Innocente, Antonio Garg, Nikhil Zhang, Yuan Bazzani, Loris Donoser, Michael Sapienza Univ Rome Rome Italy Amazon Munich Germany Amazon Seattle WA USA

ISBN: (纸本)9781665448994

Fashion retrieval methods aim at learning a clothing-specific embedding space where images are ranked based on their global visual similarity with a given query. However, global embeddings struggle to capture localized fine-grained similarities between images, because of aggregation operations. Our work deals with this problem by learning localized representations for fashion retrieval based on local interest points of prominent visual features specified by a user. We introduce a localized triplet loss function that compares samples based on corresponding patterns. We incorporate random local perturbation on the interest point as a key regularization technique to enforce local invariance of visual representations. Due to the absence of existing fashion datasets to train on localized representations, we introduce FashionLocalTriplets, a new high-quality dataset annotated by fashion specialists that contains triplets of women's dresses and interest points. The proposed model outperforms state-of-the-art global representations on FashionLocalTriplets.

关键词： Visualization computer vision Annotations Perturbation methods conferences Image retrieval pattern recognition

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Explainable Noisy Label Flipping for Multi-Label Fashion Image Classification

Explainable Noisy Label Flipping for Multi-Label Fashion Ima...

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

作者： Ferreira, Beatriz Quintino Costeira, Joao P. Gomes, Joao P. Univ Lisbon ISR IST Lisbon Portugal

ISBN: (纸本)9781665448994

In online shopping applications, the daily insertion of new products requires an overwhelming annotation effort. Usually done by humans, it comes at a huge cost and yet generates high rates of noisy/missing labels that seriously hinder the effectiveness of CNNs in multi-label classification. We propose SELF-ML, a classification framework that exploits the relation between visual attributes and appearance together with the "low-rank" nature of the feature space. It learns a sparse reconstruction of image features as a convex combination of very few images - a basis - that are correctly annotated. Building on this representation, SELF-ML has a module that relabels noisy annotations from the derived combination of the clean data. Due to such structured reconstruction, SELF-ML gives an explanation of its label-flipping decisions. Experiments on a real-world shopping dataset show that SELF-ML significantly increases the number of correct labels even with few clean annotations.

关键词： Visualization computer vision Annotations conferences Buildings pattern recognition Noise measurement

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