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LoL-V2T: Large-Scale Esports Video Description Dataset

LoL-V2T: Large-Scale Esports Video Description Dataset

引用

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

作者： Tanaka, Tsunehiko Simo-Serra, Edgar Waseda Univ Tokyo Japan

ISBN: (纸本)9781665448994

Esports is a fastest-growing new field with a largely online-presence, and is creating a demand for automatic domain-specific captioning tools. However, at the current time, there are few approaches that tackle the esports video description problem. In this work, we propose a large-scale dataset for esports video description, focusing on the popular game "League of Legends". The dataset, which we call LoL-V2T, is the largest video description dataset in the video game domain, and includes 9,723 clips with 62,677 captions. This new dataset presents multiple new video captioning challenges such as large amounts of domain-specific vocabulary, subtle motions with large importance, and a temporal gap between most captions and the events that occurred. In order to tackle the issue of vocabulary, we propose a masking the domain-specific words and provide additional annotations for this. In our results, we show that the dataset poses a challenge to existing video captioning approaches, and the masking can significantly improve performance. Our dataset and code is publicly available(1).

关键词： Training Vocabulary computer vision Video description conferences Focusing Games

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Single View Geocentric Pose in the Wild

Single View Geocentric Pose in the Wild

引用

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

作者： Christie, Gordon Foster, Kevin Hagstrom, Shea Hager, Gregory D. Brown, Myron Z. Johns Hopkins Univ Appl Phys Lab Baltimore MD 21218 USA Johns Hopkins Univ Dept Comp Sci Baltimore MD 21218 USA

ISBN: (纸本)9781665448994

Current methods for Earth observation tasks such as semantic mapping, map alignment, and change detection rely on near-nadir images;however, often the first available images in response to dynamic world events such as natural disasters are oblique. These tasks are much more difficult for oblique images due to observed object parallax. There has been recent success in learning to regress an object's geocentric pose, defined as height above ground and orientation with respect to gravity, by training with airborne lidar registered to satellite images. We present a model for this novel task that exploits affine invariance properties to outperform state of the art performance by a wide margin. We also address practical issues required to deploy this method in the wild for real-world applications. Our data and code are publicly available(1).

关键词： Training Earth computer vision Satellites Laser radar conferences Semantics

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SkipPLUS: Skip the First Few Layers to Better Explain vision Transformers

SkipPLUS: Skip the First Few Layers to Better Explain Vision...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Faridoun Mehri Mohsen Fayyaz Mahdieh Soleymani Baghshah Mohammad Taher Pilehvar Sharif University of Technology Iran University of Tehran Iran Cardiff University UK

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Despite their remarkable performance, the explainability of vision Transformers (ViTs) remains a challenge. While forward attention-based token attribution techniques have become popular in text processing, their suitability for ViTs hasn't been extensively explored. In this paper, we compare these methods against state-of-the-art input attribution methods from the vision literature, revealing their limitations due to improper aggregation of information across layers. To address this, we introduce two general techniques, PLUS and SkipPLUS, that can be composed with any input attribution method to more effectively aggregate information across layers while handling noisy layers. Through comprehensive and quantitative evaluations of faithfulness and human interpretability on a variety of ViT architectures and datasets, we demonstrate the effectiveness of PLUS and SkipPLUS, establishing a new state-of-the-art in white-box token attribution. We conclude with a comparative analysis highlighting the strengths and weaknesses of the best versions of all the studied methods. The code used in this paper is freely available at https://***/NightMachinery/SkipPLUS-CVPR-2024.

关键词： Training computer vision Animals Aggregates conferences Transformers pattern recognition

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Long-Tailed recognition of SAR Aerial View Objects by Cascading and Paralleling Experts

Long-Tailed Recognition of SAR Aerial View Objects by Cascad...

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

作者： Yang, Cheng-Yen Hsu, Hung-Min Cai, Jiarui Hwang, Jenq-Neng Univ Washington Dept Elect & Comp Engn Seattle WA 98195 USA

ISBN: (纸本)9781665448994

Aerial View Object Classification (AVOC) has started to adopt deep learning approaches with significant success in recent years, but limited to optical data. On the other hand, Synthetic Aperture Radar (SAR) has wild aerial view related applications in the remote sensing field. However, SAR has received far less attention due to the special characteristics of the SAR data, which is the long-tailed distribution of the aerial view objects that increases the difficulty of classification. In this paper, we present a two-branch framework, including the cascading expert branch and paralleling expert branch, to tackle the long-tailed distribution of the dataset. Our proposed multi-expert architecture achieves 24.675% and 26.029% in the development phase and testing phase, respectively, in the NTIRE 2021 Multi-modal Aerial View Object Classification Challenge Track I. The proposed method is proved to possess the effectiveness (top-tier performance among 157 participants) and efficiency (i.e., a lightweight architecture) for the AVOC task.

关键词： computer architecture Radar tracking Optical imaging Radar polarimetry pattern recognition Optical sensors Task analysis

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Deep Fusion of Appearance and Frame Differencing for Motion Segmentation

Deep Fusion of Appearance and Frame Differencing for Motion ...

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

作者： Ellenfeld, Marc Moosbauer, Sebastian Cardenes, Ruben Klauck, Ulrich Teutsch, Michael Hensoldt Optron GmbH Oberkochen Germany Hensoldt Analyt GmbH Oberkochen Germany Aalen Univ Appl Sci Aalen Germany Univ Western Cape Cape Town South Africa

ISBN: (纸本)9781665448994

Motion segmentation is a technique to detect and localize class-agnostic motion in videos. This motion is assumed to be relative to a stationary background and usually originates from objects such as vehicles or humans. When the camera moves, too, frame differencing approaches that do not have to model the stationary background over minutes, hours, or even days are more promising compared to background subtraction methods. In this paper, we propose a Deep Convolutional Neural Network (DCNN) for multi-modal motion segmentation: the current image contributes with appearance information to distinguish between relevant and irrelevant motion and frame differencing captures the temporal information, which is the scene's motion independent of the camera motion. We fuse this information to receive an effective and efficient approach for robust motion segmentation. The effectiveness is demonstrated using the multi-spectral CDNet-2014 dataset that we re-labeled for motion segmentation. We specifically show that we can detect tiny moving objects significantly better compared to methods based on optical flow.

关键词： computer vision Image segmentation Three-dimensional displays Fuses Motion segmentation Cameras pattern recognition

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Scaled 360 layouts: Revisiting non-central panoramas

Scaled 360 layouts: Revisiting non-central panoramas

引用

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

作者： Berenguel-Baeta, Bruno Bermudez-Cameo, Jesus Guerrero, Jose J. Univ Zaragoza I3A Zaragoza Spain

ISBN: (纸本)9781665448994

From a non-central panorama, 3D lines can be recovered by geometric reasoning. However, their sensitivity to noise and the complex geometric modeling required has led these panoramas being very little investigated. In this work we present a novel approach for 3D layout recovery of indoor environments using single non-central panoramas. We obtain the boundaries of the structural lines of the room from a non-central panorama using deep learning and exploit the properties of non-central projection systems in a new geometrical processing to recover the scaled layout. We solve the problem for Manhattan environments, handling occlusions, and also for Atlanta environments in an unified method. The experiments performed improve the state-of-the-art methods for 3D layout recovery from a single panorama. Our approach is the first work using deep learning with non-central panoramas and recovering the scale of single panorama layouts.

关键词： Deep learning computer vision Three-dimensional displays Sensitivity conferences Layout Neural networks

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Practical Cross-modal Manifold Alignment for Robotic Grounded Language Learning

Practical Cross-modal Manifold Alignment for Robotic Grounde...

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

作者： Nguyen, Andre T. Richards, Luke E. Kebe, Gaoussou Youssouf Raff, Edward Darvish, Kasra Ferraro, Frank Matuszek, Cynthia Booz Allen Hamilton Mclean VA 22102 USA Univ Maryland Baltimore Cty Baltimore MD 21228 USA

ISBN: (纸本)9781665448994

We propose a cross-modality manifold alignment procedure that leverages triplet loss to jointly learn consistent, multi-modal embeddings of language-based concepts of real-world items. Our approach learns these embeddings by sampling triples of anchor, positive, and negative data points from RGB-depth images and their natural language descriptions. We show that our approach can benefit from, but does not require, post-processing steps such as Pro-crustes analysis, in contrast to some of our baselines which require it for reasonable performance. We demonstrate the effectiveness of our approach on two datasets commonly used to develop robotic-based grounded language learning systems, where our approach outperforms four baselines, including a state-of-the-art approach, across five evaluation metrics.

关键词： Manifolds Measurement Learning systems computer vision conferences Natural languages Robot sensing systems

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Instagram Filter Removal on Fashionable Images

Instagram Filter Removal on Fashionable Images

引用

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

作者： Kinli, Furkan Ozcan, Baris Kirac, Furkan Ozyegin Univ Video Vis & Graph Lab Istanbul Turkey

ISBN: (纸本)9781665448994

Social media images are generally transformed by filtering to obtain aesthetically more pleasing appearances. However, CNNs generally fail to interpret both the image and its filtered version as the same in the visual analysis of social media images. We introduce Instagram Filter Removal Network (IFRNet) to mitigate the effects of image filters for social media analysis applications. To achieve this, we assume any filter applied to an image substantially injects a piece of additional style information to it, and we consider this problem as a reverse style transfer problem. The visual effects of filtering can be directly removed by adaptively normalizing external style information in each level of the encoder. Experiments demonstrate that IFRNet outperforms all compared methods in quantitative and qualitative comparisons, and has the ability to remove the visual effects to a great extent. Additionally, we present the filter classification performance of our proposed model, and analyze the dominant color estimation on the images unfiltered by all compared methods.

关键词： computer vision Image color analysis Social networking (online) conferences Multimedia Web sites Estimation Information filters

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DeepObjStyle: Deep Object-based Photo Style Transfer

DeepObjStyle: Deep Object-based Photo Style Transfer

引用

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

作者： Mastan, Indra Deep Raman, Shanmuganathan Indian Inst Technol Gandhinagar Gandhinagar Gujarat India

ISBN: (纸本)9781665448994

One of the major challenges of style transfer is the appropriate image features supervision between the output image and the input images (style and content). An efficient strategy would be to define an object map between the objects of the style and the content images. However, such a mapping is not well established when there are semantic objects of different types and numbers in the style and the content images. It also leads to content mismatch in the style transfer output, which could reduce the visual quality of the results. We propose an object-based style transfer approach, called DeepObjStyle, for the style supervision in the training data-independent framework. DeepObjStyle preserves the semantics of the objects and achieves better style transfer in the challenging scenario when the style and the content images have a mismatch of image features. We also perform style transfer of images containing a word cloud to demonstrate that DeepObjStyle enables an appropriate image features supervision. We validate the results using quantitative comparisons and user studies.

关键词： Training Visualization computer vision conferences Semantics Tag clouds Quality assessment

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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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