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Cali-NCE: Boosting Cross-modal Video Representation Learning with Calibrated Alignment

Cali-NCE: Boosting Cross-modal Video Representation Learning...

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2023 IEEE/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Zhao, Nanxuan Jiao, Jianbo Xie, Weidi Lin, Dahua University of Bath Department of Computer Science United Kingdom University of Birmingham School of Computer Science United Kingdom Shanghai Jiaotong University Cooperative Medianet Innovation Center China Chinese University of Hong Kong Department of Information Engineering Hong Kong

ISBN: (纸本)9798350302493

With the large-scale video-text datasets being collected, learning general visual-textual representation has gained increasing attention. While recent methods are designed with the assumption that the alt-text description naturally conveys the meaning and context of the video in semantics (i.e. well aligned with each other), it is unlikely to be satisfied for the Internet data, which potentially harms the quality of the learned visual-textual representation. To address this challenge, we first revisit three mainstream approaches: correspondence modeling, contrastive learning and predictive coding, demonstrating that a simple co-training strategy with these methods leads to a clear improvement in performance. To further explore the complementary nature of different training strategies, we propose a simple yet effective joint training framework that factorizes the total objective into conditional ones, termed as Cali-NCE 1. Our method first estimates confidence scores for measuring the correspondence between video and text descriptions, and the scores are later used to calibrate the sample weightings during contrastive training. Through extensive experiments, we show that the proposed approach achieves state-of-the-art performance on multiple downstream tasks: text-to-video retrieval, video action recognition, and video retrieval. © 2023 IEEE.

关键词： Semantics

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NTIRE 2023 Challenge on HR Depth from Images of Specular and Transparent Surfaces

NTIRE 2023 Challenge on HR Depth from Images of Specular and...

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2023 IEEE/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Ramirez, Pierluigi Zama Costanzino, Alex Tosi, Fabio Poggi, Matteo Salti, Samuele Mattoccia, Stefano Di Stefano, Luigi Timofte, Radu Cao, Chengzhi Zhang, Fanrui Zhan, Qiang Wang, Kunyu Shi, Jun Zhang, Dafeng Yong, A. Jin, Yixiang Li, Dingzhe Li, Chao Liu, Zhiwen Zhang, Qi Wang, Yixing Li, Liangyan Liang, Runchen Liu, Yangyi Liu, Huan Song, Siyu Chen, Jun Yin, Shi University of Bologna Italy Computer Vision Lab University of Würzburg Germany Computer Vision Lab Eth Zürich Switzerland University of Science and Technology of China China China Vivo China McMaster University Canada Nanjing Tech University China

ISBN: (纸本)9798350302493

This paper reports about the NTIRE 2023 challenge on HR Depth From images of Specular and Transparent surfaces, held in conjunction with the New Trends in Image Restoration and Enhancement workshop (NTIRE) workshop at CVPR 2023. This challenge is held to boost the research on depth estimation, mainly to deal with two of the open issues in the field: high-resolution images and non-Lambertian surfaces characterizing specular and transparent materials. The challenge is divided into two tracks: a stereo track focusing on disparity estimation from rectified pairs and a mono track dealing with single-image depth estimation. The challenge attracted about 100 registered participants for the two tracks. In the final testing stage, 5 participating teams submitted their models and fact sheets, 2 and 3 for the Stereo and Mono tracks, respectively. © 2023 IEEE.

关键词： Image enhancement

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An Ensemble Approach for Facial Behavior Analysis in-the-wild Video

An Ensemble Approach for Facial Behavior Analysis in-the-wil...

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

作者： Hong-Hai Nguyen Van-Thong Huynh Kim, Soo-Hyung Chonnam Natl Univ Dept Artificial Intelligence Convergence Gwangju South Korea

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Human emotions recognization contributes to the development of human-computer interaction. The machines understanding human emotions in the real world will significantly contribute to life in the future. This paper introduces the 3rd Affective Behavior Analysis in-the-wild (ABAW3) 2022 challenge. We focused on solving the problem of the Valence-Arousal (VA) estimation and Action Unit (AU) detection. For valence-arousal estimation, we conducted two stages: creating new features from multimodel and temporal learning to predict valence-arousal. First, we make new features;the Gated Recurrent Unit (GRU) and Transformer are combined using a Regular Networks (RegNet) feature, which is extracted from the image. The next step is the GRU combined with local attention to predict valence-arousal. The Concordance Correlation Coefficient (CCC) was used to evaluate the model. The result achieved 0.450 for valence and 0.445 for arousal on the test set, outperforming the baseline method with a corresponding CCC of 0.180 for valence and 0.170 for arousal. We also performed additional experiments on the action unit task with simple transformer blocks. We achieved a score of 49.04 on the test set in terms of F-1 score, which outperforms the baseline method with a corresponding F1 score of 36.50. Our submission to ABAW3 2022 ranks 3rd for both tasks.

关键词： Human computer interaction Sentiment analysis Gold Estimation Logic gates Feature extraction Transformers

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Towards Automated Polyp Segmentation Using Weakly- and Semi-Supervised Learning and Deformable Transformers

Towards Automated Polyp Segmentation Using Weakly- and Semi-...

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2023 IEEE/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Ren, Guangyu Lazarou, Michalis Yuan, Jing Stathaki, Tania Imperial College London United Kingdom

ISBN: (纸本)9798350302493

Polyp segmentation is a crucial step towards computer-aided diagnosis of colorectal cancer. However, most of the polyp segmentation methods require pixel-wise annotated datasets. Annotated datasets are tedious and time-consuming to produce, especially for physicians who must dedicate their time to their patients. To this end, we propose a novel weakly- and semi-supervised learning polyp segmentation framework that can be trained using only weakly annotated images along with unlabeled images making it very cost-efficient to use. More specifically our contributions are: 1) a novel weakly annotated polyp dataset, 2) a novel sparse foreground loss that suppresses false positives and improves weakly-supervised training, 3) a deformable transformer encoder neck for feature enhancement by fusing information across levels and flexible spatial *** experimental results demonstrate the merits of our ideas on five challenging datasets outperforming some state-of-the-art fully supervised models. Also, our framework can be utilized to fine-tune models trained on natural image segmentation datasets drastically improving their performance for polyp segmentation and impressively demonstrating superior performance to fully supervised fine-tuning. Code can be found in https://***/ic-qialanqian/WS-DefSegNet. © 2023 IEEE.

关键词： computer aided diagnosis

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WEDGE: A multi-weather autonomous driving dataset built from generative vision-language models

WEDGE: A multi-weather autonomous driving dataset built from...

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2023 IEEE/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Marathe, Aboli Ramanan, Deva Walambe, Rahee Kotecha, Ketan Carnegie Mellon University Machine Learning Department PA United States Carnegie Mellon University Robotics Institute PA United States India India

ISBN: (纸本)9798350302493

The open road poses many challenges to autonomous perception, including poor visibility from extreme weather conditions. Models trained on good-weather datasets frequently fail at detection in these out-of-distribution settings. To aid adversarial robustness in perception, we introduce WEDGE (WEather images by DALL-E GEneration): a synthetic dataset generated with a vision-language generative model via prompting. WEDGE consists of 3360 images in 16 extreme weather conditions manually annotated with 16513 bounding boxes, supporting research in the tasks of weather classification and 2D object detection. We have analyzed WEDGE from research standpoints, verifying its effectiveness for extreme-weather autonomous perception. We establish baseline performance for classification and detection with 53.87% test accuracy and 45.41 mAP. Most importantly, WEDGE can be used to fine-tune state-of-the-art detectors, improving SOTA performance on real-world weather benchmarks (such as DAWN) by 4.48 AP for well-generated classes like trucks. WEDGE has been collected under OpenAI's terms 1 of use and is released for public use under the CC BY-NC-SA 4.0 license. The repository for this work and dataset is available at https://***/WEDGE. © 2023 IEEE.

关键词： Benchmarking

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Fast GraspNeXt: A Fast Self-Attention Neural Network Architecture for Multi-task Learning in computer vision Tasks for Robotic Grasping on the Edge

Fast GraspNeXt: A Fast Self-Attention Neural Network Archite...

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2023 IEEE/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Wong, Alexander Wu, Yifan Abbasi, Saad Nair, Saeejith Chen, Yuhao Shafiee, Mohammad Javad Vision and Image Processing Research Group University of Waterloo Canada Waterloo Artificial Intelligence Institute WaterlooON Canada DarwinAI Corp. WaterlooON Canada

ISBN: (纸本)9798350302493

Multi-task learning has shown considerable promise for improving the performance of deep learning-driven vision systems for the purpose of robotic grasping. However, high architectural and computational complexity can result in poor suitability for deployment on embedded devices that are typically leveraged in robotic arms for real-world manufacturing and warehouse environments. As such, the design of highly efficient multi-task deep neural network architectures tailored for computer vision tasks for robotic grasping on the edge is highly desired for widespread adoption in manufacturing environments. Motivated by this, we propose Fast GraspNeXt, a fast self-attention neural network architecture tailored for embedded multi-task learning in computer vision tasks for robotic grasping. To build Fast GraspNeXt, we leverage a generative network architecture search strategy with a set of architectural constraints customized to achieve a strong balance between multitask learning performance and embedded inference efficiency. Experimental results on the MetaGraspNet benchmark dataset show that the Fast GraspNeXt network design achieves the highest performance (average precision (AP), accuracy, and mean squared error (MSE)) across multiple computer vision tasks when compared to other efficient multi-task network architecture designs, while having only 17.8M parameters (about >5× smaller), 259 GFLOPs (as much as >5× lower) and as much as >3.15× faster on a NVIDIA Jetson TX2 embedded processor. © 2023 IEEE.

关键词： computer vision

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H-Net: Unsupervised Attention-based Stereo Depth Estimation Leveraging Epipolar Geometry

H-Net: Unsupervised Attention-based Stereo Depth Estimation ...

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

作者： Huang, Baoru Zheng, Jian-Qing Giannarou, Stamatia Elson, Daniel S. Imperial Coll London Hamlyn Ctr Robot Surg London England Univ Oxford Kennedy Inst Rheumatol Oxford England Univ Oxford Big Data Inst Oxford England

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Depth estimation from a stereo image pair has become one of the most explored applications in computer vision, with most previous methods relying on fully supervised learning settings. However, due to the difficulty in acquiring accurate and scalable ground truth data, the training of fully supervised methods is challenging. As an alternative, self-supervised methods are becoming more popular to mitigate this challenge. In this paper, we introduce the H-Net, a deep-learning framework for unsupervised stereo depth estimation that leverages epipolar geometry to refine stereo matching. For the first time, a Siamese autoencoder architecture is used for depth estimation which allows mutual information between rectified stereo images to be extracted. To enforce the epipolar constraint, the mutual epipolar attention mechanism has been designed which gives more emphasis to correspondences of features that lie on the same epipolar line while learning mutual information between the input stereo pair. Stereo correspondences are further enhanced by incorporating semantic information to the proposed attention mechanism. More specifically, the optimal transport algorithm is used to suppress attention and eliminate outliers in areas not visible in both cameras. Extensive experiments on KITTI2015 and Cityscapes show that the proposed modules are able to improve the performance of the unsupervised stereo depth estimation methods while closing the gap with the fully supervised approaches.

关键词： Geometry Training computer vision Supervised learning Semantics Estimation computer architecture

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SynthASpoof: Developing Face Presentation Attack Detection Based on Privacy-friendly Synthetic Data

SynthASpoof: Developing Face Presentation Attack Detection B...

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2023 IEEE/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Fang, Meiling Huber, Marco Damer, Naser Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany TU Darmstadt Department of Computer Science Darmstadt Germany

ISBN: (纸本)9798350302493

Recently, significant progress has been made in face presentation attack detection (PAD), which aims to secure face recognition systems against presentation attacks, owing to the availability of several face PAD datasets. However, all available datasets are based on privacy and legally-sensitive authentic biometric data with a limited number of subjects. To target these legal and technical challenges, this work presents the first synthetic-based face PAD dataset, named SynthASpoof, as a large-scale PAD development dataset. The bona fide samples in SynthASpoof are synthetically generated and the attack samples are collected by presenting such synthetic data to capture systems in a real attack scenario. The experimental results demonstrate the feasibility of using SynthASpoof for the development of face PAD. Moreover, we boost the performance of such a solution by incorporating the domain generalization tool MixStyle into the PAD solutions. Additionally, we showed the viability of using synthetic data as a supplement to enrich the diversity of limited authentic training data and consistently enhance PAD performances. The SynthASpoof dataset, containing 25,000 bona fide and 78,800 attack samples, the implementation, and the pre-trained weights are made publicly available https://***/meilfang/***. © 2023 IEEE.

关键词： Face recognition

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HiMODE: A Hybrid Monocular Omnidirectional Depth Estimation Model

HiMODE: A Hybrid Monocular Omnidirectional Depth Estimation ...

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

作者： Junayed, Masum Shah Sadeghzadeh, Arezoo Islam, Md Baharul Wong, Lai-Kuan Aydin, Tarkan Bahcesehir Univ Istanbul Turkey Amer Univ Malta Cospicua Malta Multimedia Univ Cyberjaya Malaysia

ISBN: (纸本)9781665487399

Monocular omnidirectional depth estimation is receiving considerable research attention due to its broad applications for sensing 360 degrees surroundings. Existing approaches in this field suffer from limitations in recovering small object details and data lost during the ground-truth depth map acquisition. In this paper, a novel monocular omnidirectional depth estimation model, namely HiMODE is proposed based on a hybrid CNN+Transformer (encoder-decoder) architecture whose modules are efficiently designed to mitigate distortion and computational cost, without performance degradation. Firstly, we design a feature pyramid network based on the HNet block to extract high-resolution features near the edges. The performance is further improved, benefiting from a self and cross attention layer and spatial/temporal patches in the Transformer encoder and decoder, respectively. Besides, a spatial residual block is employed to reduce the number of parameters. By jointly passing the deep features extracted from an input image at each backbone block, along with the raw depth maps predicted by the transformer encoder-decoder, through a context adjustment layer, our model can produce resulting depth maps with better visual quality than the ground-truth. Comprehensive ablation studies demonstrate the significance of each individual module. Extensive experiments conducted on three datasets;Stanford3D, Matterport3D, and SunCG, demonstrate that HiMODE can achieve state-of-the-art performance for 360 degrees monocular depth estimation. Complete project code and supplementary materials are available at https://***/himode5008/HiMODE.

关键词： Visualization Three-dimensional displays Estimation Lighting computer architecture Feature extraction Transformers

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Efficient Multi-Lens Bokeh Effect Rendering and Transformation

Efficient Multi-Lens Bokeh Effect Rendering and Transformati...

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2023 IEEE/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Seizinger, Tim Conde, Marcos V. Kolmet, Manuel Bishop, Tom E. Timofte, Radu University of Würzburg Computer Vision Lab CAIDAS & IFI Germany Glass Imaging Inc. CA United States

ISBN: (纸本)9798350302493

Many advancements of mobile cameras aim to reach the visual quality of professional DSLR cameras. Great progress was shown over the last years in optimizing the sharp regions of an image and in creating virtual portrait effects with artificially blurred backgrounds. Bokeh is the aesthetic quality of the blur in out-of-focus areas of an image. This is a popular technique among professional photographers, and for this reason, a new goal in computational photography is to optimize the Bokeh effect *** paper introduces EBokehNet, a efficient state-of-the-art solution for Bokeh effect transformation and rendering. Our method can render Bokeh from an all-in-focus image, or transform the Bokeh of one lens to the effect of another lens without harming the sharp foreground regions in the image. Moreover we can control the shape and strength of the effect by feeding the lens properties i.e. type (Sony or Canon) and aperture, into the neural network as an additional input. Our method is a winning solution at the NTIRE 2023 Lens-to-Lens Bokeh Effect Transformation Challenge, and state-of-the-art at the EBB benchmark. © 2023 IEEE.

关键词： Color photography

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