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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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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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v2e: From Video Frames to Realistic DVS Events

v2e: From Video Frames to Realistic DVS Events

引用

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

作者： Hu, Yuhuang Liu, Shih-Chii Delbruck, Tobi Univ Zurich Inst Neuroinformat Zurich Switzerland Swiss Fed Inst Technol Zurich Switzerland

ISBN: (纸本)9781665448994

To help meet the increasing need for dynamic vision sensor (DVS) event camera data, this paper proposes the v2e toolbox that generates realistic synthetic DVS events from intensity frames. It also clarifies incorrect claims about DVS motion blur and latency characteristics in recent literature. Unlike other toolboxes, v2e includes pixel-level Gaussian event threshold mismatch, finite intensity-dependent bandwidth, and intensity-dependent noise. Realistic DVS events are useful in training networks for uncontrolled lighting conditions. The use of v2e synthetic events is demonstrated in two experiments. The first experiment is object recognition with N-Caltech 101 dataset. Results show that pretraining on various v2e lighting conditions improves generalization when transferred on real DVS data for a ResNet model. The second experiment shows that for night driving, a car detector trained with v2e events shows an average accuracy improvement of 40% compared to the YOLOv3 trained on intensity frames.

关键词： Training Visualization Lighting vision sensors Tools Cameras pattern recognition

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Multi-Scale Selective Residual Learning for Non-Homogeneous Dehazing

Multi-Scale Selective Residual Learning for Non-Homogeneous ...

引用

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

作者： Jo, Eunsung Sim, Jae-Young Ulsan Natl Inst Sci & Technol Grad Sch Artificial Intelligence Ulsan South Korea Ulsan Natl Inst Sci & Technol Dept Elect Engn Ulsan South Korea

ISBN: (纸本)9781665448994

As the particles in hazy medium cause the absorption and scattering of light, the images captured under such environment suffer from quality degradation such as low contrast and color distortion. While numerous single image dehazing methods have been proposed to reconstruct clean images from hazy images, non-homogeneous dehazing has been rarely studied. In this paper, we design an end-to-end network to remove non-homogeneous dense haze. We employ the selective residual blocks to adaptively improve the visibility of resulting images, where the input feature and the residual feature are combined with fully trainable weights. Experimental results including the ablation study demonstrate that the proposed method is a promising tool for non-homogeneous dehazing that enhances the contrast of hazy images effectively while restoring colorful appearance faithfully.

关键词： Degradation computer vision Image color analysis conferences Scattering Tools Distortion

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OmniLayout: Room Layout Reconstruction from Indoor Spherical Panoramas

OmniLayout: Room Layout Reconstruction from Indoor Spherical...

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

作者： Rao, Shivansh Kumar, Vikas Kifer, Daniel Giles, C. Lee Mali, Ankur Penn State Univ University Pk PA 16802 USA

ISBN: (纸本)9781665448994

Given a single RGB panorama, the goal of 3D layout reconstruction is to estimate the room layout by predicting the corners, floor boundary, and ceiling boundary. A common approach has been to use standard convolutional networks to predict the corners and boundaries, followed by post-processing to generate the 3D layout. However, the space-varying distortions in panoramic images are not compatible with the translational equivariance property of standard convolutions, thus degrading performance. Instead, we propose to use spherical convolutions. The resulting network, which we call OmniLayout performs convolutions directly on the sphere surface, sampling according to inverse equirectangular projection and hence invariant to equirectangular distortions. Using a new evaluation metric, we show that our network reduces the error in the heavily distorted regions (near the poles) by approximate to 25% when compared to standard convolutional networks. Experimental results show that OmniLayout outperforms the state-of-the-art by approximate to 4% on two different benchmark datasets (PanoContext and Stanford 2D-3D). Code is available at https://***/rshivansh/OmniLayout.

关键词： Convolutional codes Three-dimensional displays Layout Benchmark testing Distortion Predistortion pattern recognition

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S3Net: A Single Stream Structure for Depth Guided Image Relighting

S3Net: A Single Stream Structure for Depth Guided Image Reli...

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

作者： Yang, Hao-Hsiang Chen, Wei-Ting Kuo, Sy-Yen Asustek Comp Inc ASUS Intelligent Cloud Serv Taipei Taiwan Natl Taiwan Univ Grad Inst Elect Engn Taipei Taiwan Natl Taiwan Univ Dept Elect Engn Taipei Taiwan

ISBN: (纸本)9781665448994

Depth guided any-to-any image relighting aims to generate a relit image from the original image and corresponding depth maps to match the illumination setting of the given guided image and its depth map. To the best of our knowledge, this task is a new challenge that has not been addressed in the previous literature. To address this issue, we propose a deep learning-based neural Single Stream Structure network called S3Net for depth guided image relighting. This network is an encoder-decoder model. We concatenate all images and corresponding depth maps as the input and feed them into the model. The decoder part contains the attention module and the enhanced module to focus on the relighting-related regions in the guided images. Experiments performed on challenging benchmark show that the proposed model achieves the 3rd highest SSIM in the NTIRE 2021 Depth Guided Any-to-any Relighting Challenge.

关键词： Training Fuses Lighting Streaming media Feature extraction Decoding pattern recognition

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SoccerNet-v2: A Dataset and Benchmarks for Holistic Understanding of Broadcast Soccer Videos

SoccerNet-v2: A Dataset and Benchmarks for Holistic Understa...

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

作者： Deliege, Adrien Cioppa, Anthony Giancola, Silvio Seikavandi, Meisam J. Dueholm, Jacob, V Nasrollahi, Kamal Ghanem, Bernard Moeslund, Thomas B. Van Droogenbroeck, Marc Univ Liege Liege Belgium KAUST Thuwal Saudi Arabia Aalborg Univ Aalborg Denmark Milestone Syst Brondby Denmark

ISBN: (纸本)9781665448994

Understanding broadcast videos is a challenging task in computer vision, as it requires generic reasoning capabilities to appreciate the content offered by the video editing. In this work, we propose SoccerNet-v2, a novel large-scale corpus of manual annotations for the SoccerNet [24] video dataset, along with open challenges to encourage more research in soccer understanding and broadcast production. Specifically, we release around 300k annotations within SoccerNet's 500 untrimmed broadcast soccer videos. We extend current tasks in the realm of soccer to include action spotting, camera shot segmentation with boundary detection, and we define a novel replay grounding task. For each task, we provide and discuss benchmark results, reproducible with our open-source adapted implementations of the most relevant works in the field. SoccerNet-v2 is presented to the broader research community to help push computer vision closer to automatic solutions for more general video understanding and production purposes.

关键词： computer vision Annotations Grounding Production Benchmark testing Cameras pattern recognition

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Extracurricular Learning: Knowledge Transfer Beyond Empirical Distribution

Extracurricular Learning: Knowledge Transfer Beyond Empirica...

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

作者： Pouransari, Hadi Javaheripi, Mojan Sharma, Vinay Tuzel, Oncel Apple Cupertino CA 95014 USA UCSD La Jolla CA USA

ISBN: (纸本)9781665448994

Knowledge distillation has been used to transfer knowledge learned by a sophisticated model (teacher) to a simpler model (student). This technique is widely used to compress model complexity. However, in most applications the compressed student model suffers from an accuracy gap with its teacher. We propose extracurricular learning, a novel knowledge distillation method, that bridges this gap by (1) modeling student and teacher output distributions;(2) sampling examples from an approximation to the underlying data distribution;and (3) matching student and teacher output distributions over this extended set including uncertain samples. We conduct rigorous evaluations on regression and classification tasks and show that compared to the standard knowledge distillation, extracurricular learning reduces the gap by 46 % to 68 %. This leads to major accuracy improvements compared to the empirical risk minimization-based training for various recent neural network architectures: 16% regression error reduction on the MPIIGaze dataset, +3.4% to +9.1% improvement in top-1 classification accuracy on the CIFAR100 dataset, and +2.9% top-1 improvement on the ImageNet dataset.

关键词： Training Uncertainty Neural networks Estimation Data models pattern recognition Task analysis

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Cross-modal Speaker Verification and recognition: A Multilingual Perspective

Cross-modal Speaker Verification and Recognition: A Multilin...

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

作者： Nawaz, Shah Saeed, Muhammad Saad Morerio, Pietro Mahmood, Arif Gallo, Ignazio Yousaf, Muhammad Haroon Del Bue, Alessio Ist Italiano Tecnol IIT Pattern Anal & Comp Vis PAVIS Genoa Italy Ist Italiano Tecnol IIT Visual Geometry & Modelling VGM Genoa Italy Univ Insubria Varese VA Italy Univ Engn & Technol Taxila Rawalpindi Punjab India Informat Technol Univ Lahore Pakistan

ISBN: (纸本)9781665448994

Recent years have seen a surge in finding association between faces and voices within a cross-modal biometric application along with speaker recognition. Inspired from this, we introduce a challenging task in establishing association between faces and voices across multiple languages spoken by the same set of persons. The aim of this paper is to answer two closely related questions: "Is face-voice association language independent?" and "Can a speaker be recognized irrespective of the spoken language?". These two questions are important to understand effectiveness and to boost development of multilingual biometric systems. To answer these, we collected a Multilingual Audio-Visual dataset, containing human speech clips of 154 identities with 3 language annotations extracted from various videos uploaded online. Extensive experiments on the two splits of the proposed dataset have been performed to investigate and answer these novel research questions that clearly point out the relevance of the multilingual problem.

关键词： Training Annotations Face recognition Pipelines Speech recognition Speaker recognition Task analysis

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Sewer-ML: A Multi-Label Sewer Defect Classification Dataset and Benchmark

Sewer-ML: A Multi-Label Sewer Defect Classification Dataset ...

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

作者： Haurum, Joakim Bruslund Moeslund, Thomas B. Aalborg Univ Visual Anal & Percept VAP Lab Aalborg Denmark

ISBN: (纸本)9781665445092

Perhaps surprisingly sewerage infrastructure is one of the most costly infrastructures in modern society. Sewer pipes are manually inspected to determine whether the pipes are defective. However, this process is limited by the number of qualified inspectors and the time it takes to inspect a pipe. Automatization of this process is therefore of high interest. So far, the success of computer vision approaches for sewer defect classification has been limited when compared to the success in other fields mainly due to the lack of public datasets. To this end, in this work we present a large novel and publicly available multi-label classification dataset for image-based sewer defect classification called Sewer-ML. The Sewer-ML dataset consists of 1.3 million images annotated by professional sewer inspectors from three different utility companies across nine years. Together with the dataset, we also present a benchmark algorithm and a novel metric for assessing performance. The benchmark algorithm is a result of evaluating 12 state-of-the-art algorithms, six from the sewer defect classification domain and six from the multi-label classification domain, and combining the best performing algorithms. The novel metric is a class-importance weighted F2 score, F2CIW, reflecting the economic impact of each class, used together with the normal pipe F1 score, F1Normal. The benchmark algorithm achieves an F2CIW score of 55.11% and F1Normal score of 90.94%, leaving ample room for improvement on the SewerML dataset. The code, models, and dataset are available at the project page http://***/sewer-ml

关键词： Measurement Economics computer vision Codes Biological system modeling Benchmark testing Inspection

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