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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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Ice hockey player identification via transformers and weakly supervised learning

Ice hockey player identification via transformers and weakly...

引用

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

作者： Vats, Kanav McNally, William Walters, Pascale Clausi, David A. Zelek, John S. Univ Waterloo Syst Design Engn Waterloo ON Canada Stathletes Inc St Catharines ON Canada

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Identifying players in video is a foundational step in computer vision-based sports analytics. Obtaining player identities is essential for analyzing the game and is used in downstream tasks such as game event recognition. Transformers are the existing standard in natural language processing (NLP) and are swiftly gaining traction in computer vision. Motivated by the increasing success of transformers in computer vision, we introduce a transformer network for recognizing players through their jersey numbers in broadcast National Hockey League (NHL) videos. The transformer takes temporal sequences of player frames (called player tracklets) as input and outputs the probabilities of jersey numbers present in the frames. The proposed network performs better than the previous benchmark on the same dataset. We implement a weakly-supervised training approach by generating approximate frame-level labels for jersey number presence and use the frame-level labels for faster training. We also utilize player shifts available in the NHL play-by-play data by reading the game time using optical character recognition (OCR) to get the players on the ice rink at a certain game time. Using player-shifts improved the player identification accuracy by 6%.

关键词： Training computer vision Optical character recognition Supervised learning Games Transformers Ice

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Learning Unbiased Representations via Mutual Information Backpropagation

Learning Unbiased Representations via Mutual Information Bac...

引用

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

作者： Ragonesi, Ruggero Volpi, Riccardo Cavazza, Jacopo Murino, Vittorio Ist Italiano Tecnol PAVIS Dept Genoa Italy Univ Genoa DITEN Dept Genoa Italy Naver Labs Europe Grenoble France Univ Verona Dept Comp Sci Verona Italy Huawei Technol Ltd Ireland Res Ctr Dublin Ireland

ISBN: (纸本)9781665448994

We are interested in learning data-driven representations that can generalize well, even when trained on inherently biased data. In particular, we face the case where some attributes (bias) of the data, if learned by the model, can severely compromise its generalization properties. We tackle this problem through the lens of information theory, leveraging recent findings for a differentiable estimation of mutual information. We propose a novel end-to-end optimization strategy, which simultaneously estimates and minimizes the mutual information between the learned representation and specific data attributes. When applied on standard benchmarks, our model shows comparable or superior classification performance with respect to state-of-the-art approaches. Moreover, our method is general enough to be applicable to the problem of "algorithmic fairness", with competitive results.

关键词： Training computer vision Face recognition Estimation Benchmark testing Mutual information Tuning

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Information Hiding in RGB Images Using an Improved Matrix pattern Approach 30

Information Hiding in RGB Images Using an Improved Matrix Pa...

引用

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

作者： Nilizadeh, Amirfarhad Mazurczyk, Wojciech Zou, Cliff Leavens, Gary T. Univ Cent Florida Dept Comp Sci Orlando FL 32816 USA Warsaw Univ Technol Inst Telecommun Warsaw Poland

ISBN: (纸本)9781538607336

In this paper, a novel steganography algorithm based on an improved "Matrix pattern" (MP) method is presented. In this process, firstly, an RGB image is divided into the non-overlapping square-sized blocks. Next, 95 dynamic-sized unique matrix patterns are automatically generated using the 4th and 5th bit layers of the green layer of each block, which are assigned to 95 English keyboard characters. Then, the blue layer of each block is used for embedding secret messages by adding matrix patterns which are assigned to the characters of the secret message. The results show that this algorithm has a high resistance against steganalysis attacks, including Regular Singular (RS), Sample Pair (SP), and PVD based attacks. Furthermore, the proposed algorithm not only improves capacity by over 27% when compared to the existing method, but also results in a slightly better transparency of the stego-image.

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

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Multi-Modal Temporal Convolutional Network for Anticipating Actions in Egocentric Videos

Multi-Modal Temporal Convolutional Network for Anticipating ...

引用

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

作者： Zatsarynna, Olga Abu Farha, Yazan Gall, Juergen Univ Bonn Bonn Germany

ISBN: (纸本)9781665448994

Anticipating human actions is an important task that needs to be addressed for the development of reliable intelligent agents, such as self-driving cars or robot assistants. While the ability to make future predictions with high accuracy is crucial for designing the anticipation approaches, the speed at which the inference is performed is not less important. Methods that are accurate but not sufficiently fast would introduce a high latency into the decision process. Thus, this will increase the reaction time of the underlying system. This poses a problem for domains such as autonomous driving, where the reaction time is crucial. In this work, we propose a simple and effective multi-modal architecture based on temporal convolutions. Our approach stacks a hierarchy of temporal convolutional layers and does not rely on recurrent layers to ensure a fast prediction. We further introduce a multi-modal fusion mechanism that captures the pairwise interactions between RGB, flow, and object modalities. Results on two large-scale datasets of egocentric videos, EPIC-Kitchens-55 and EPIC-Kitchens-100, show that our approach achieves comparable performance to the state-of-the-art approaches while being significantly faster.

关键词： computer vision conferences computer architecture pattern recognition Autonomous automobiles Reliability Intelligent agents

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SPIN: Simplifying Polar Invariance for Neural networks Application to vision-based irradiance forecasting

SPIN: Simplifying Polar Invariance for Neural networks Appli...

引用

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

作者： Paletta, Quentin Hu, Anthony Arbod, Guillaume Blanc, Philippe Lasenby, Joan Univ Cambridge Cambridge England ENGIE Lab CRIGEN Stains France MINES ParisTech Paris France

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Translational invariance induced by pooling operations is an inherent property of convolutional neural networks, which facilitates numerous computer vision tasks such as classification. Yet to leverage rotational invariant tasks, convolutional architectures require specific rotational invariant layers or extensive data augmentation to learn from diverse rotated versions of a given spatial configuration. Unwrapping the image into its polar coordinates provides a more explicit representation to train a convolutional architecture as the rotational invariance becomes translational, hence the visually distinct but otherwise equivalent rotated versions of a given scene can be learnt from a single image. We show with two common vision-based solar irradiance forecasting challenges (i.e. using ground-taken sky images or satellite images), that this preprocessing step significantly improves prediction results by standardising the scene representation, while decreasing training time by a factor of 4 compared to augmenting data with rotations. In addition, this transformation magnifies the area surrounding the centre of the rotation, leading to more accurate short-term irradiance predictions.

关键词： Training computer vision Satellites Neural networks computer architecture Predictive models Cameras

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Model Level Ensemble for Facial Action Unit recognition at the 3rd ABAW Challenge

Model Level Ensemble for Facial Action Unit Recognition at t...

引用

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

作者： Jiang, Wenqiang Wu, Yannan Qiao, Fengsheng Meng, Liyu Deng, Yuanyuan Liu, Chuanhe Beijing Seek Truth Data Technol Co Ltd Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In this paper, we present our latest work on Action Unit Detection, which is a part of the Affective Behavior Analysis in-the-wild (ABAW) 2022 Competition [15]. Our proposed network is based on the IResnet100 [6]. First of all, We utilize feature pyramid networks (FPN) [25] and single stage headless (SSH) [29] to enlarge the receptive field and extract more facial texture features. Then we employ the ML-ROS data balancing [4] and the BCE Loss plus Multi-label Loss to solve the multi-label imbalance problem. We also use three different models as the base model to fine-tune the Aff-Wild2 dataset. The pre-train backbones are the AU detection model, expression model and face recognition model. Finally, we adopt an ensemble methodology to get the final result. Our f1 score achieved 49.82 on the AU test set and ranked second in this challenge with a very small difference from the first team 49.89.

关键词： Gold computer vision Face recognition conferences Feature extraction Behavioral sciences Task analysis

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Dual-Branch Collaborative Transformer for Virtual Try-On

Dual-Branch Collaborative Transformer for Virtual Try-On

引用

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

作者： Fenocchi, Emanuele Morelli, Davide Cornia, Marcella Baraldi, Lorenzo Cesari, Fabio Cucchiara, Rita Univ Modena & Reggio Emilia Modena Italy YOOX NET PORTER GRP Milan Italy

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Image-based virtual try-on has recently gained a lot of attention in both the scientific and fashion industry communities due to its challenging setting and practical real-world applications. While pure convolutional approaches have been explored to solve the task, Transformer-based architectures have not received significant attention yet. Following the intuition that self- and cross-attention operators can deal with long-range dependencies and hence improve the generation, in this paper we extend a Transformer-based virtual try-on model by adding a dual-branch collaborative module that can exploit cross-modal information at generation time. We perform experiments on the VITON dataset, which is the standard benchmark for the task, and on a recently collected virtual try-on dataset with multi-category clothing, Dress Code. Experimental results demonstrate the effectiveness of our solution over previous methods and show that Transformer-based architectures can be a viable alternative for virtual try-on.

关键词： Convolutional codes Industries Clothing Collaboration computer architecture Transformers pattern recognition

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A Comparison of Crowd Commotion Measures from Generative Models

A Comparison of Crowd Commotion Measures from Generative Mod...

引用

IEEE conference on computer vision and pattern recognition (CVPR)

作者： Mohammadi, Sadegh Kiani, Hamed Perina, Alessandro Murino, Vittorio Ist Italiano Tecnol Pattern Anal & Comp Vis Dept PAVIS Genoa Italy

ISBN: (纸本)9781467367592

Detecting abnormal events in video sequences is a challenging task that has been broadly investigated over the last decade. The main challenges come from the lack of a clear definition of abnormality and from the scarcity, of ten absence, of abnormal training samples. To address these two shortages, the computer vision community made use of generative models to learn normal behavioral patterns in videos. Then, for each test observation, a (crowd) commotion measure is computed quantifying the deviation from the normal model. In this paper;we evaluated two different families of generative models, namely topic models, representing the standard choice, and the most recent Counting Grids which have never been considered for this task. Moreover;we also extended the 2D Counting Grid, introduced for the analysis of images, to three dimensions, making the model able to capture the spatial-temporal relationships of the videos. In the experimental section, we compared all the approaches on jive challenging sequences showing the superiority of the 3-D counting grid.

关键词： Cameras Computational modeling Context Feature extraction Histograms Surveillance Video sequences

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Cascaded Siamese Self-supervised Audio to Video GAN

Cascaded Siamese Self-supervised Audio to Video GAN

引用

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

作者： Aldausari, Nuha Sowmya, Arcot Marcus, Nadine Mohammadi, Gelareh Univ New South Wales Sch Comp Sci & Engn Sydney NSW Australia

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Generating meaningful videos that are synchronised to audio signals is a complex synthesis task that requires generation of not only realistic videos but also coherent video motions that conform to the provided audio signals. While tremendous effort has been expended on audio-to-video generative models, these models rely heavily on supervised signals such as face/body key points or 3D meshes. However, key point annotation requires time and effort. Besides, some dataset domains do not have predictable structure, which makes the extraction of points of interest infeasible. Our proposed model consists of a cascaded generator-discriminator architecture that works at the pixel level to generate videos according to the associated soundtracks. It adopts a new self-supervised temporal augmentation technique to optimise the correlation between the audio signal and the generated video instead of relying on supervised signals. The proposed architecture has proven its effectiveness in extensive experiments that compared different models across two datasets.

关键词： Solid modeling Correlation Three-dimensional displays Computational modeling conferences computer architecture pattern recognition

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