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NTIRE 2023 Quality Assessment of Video Enhancement Challenge

NTIRE 2023 Quality Assessment of Video Enhancement Challenge

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Liu, Xiaohong Min, Xiongkuo Sun, Wei Zhang, Yulun Zhang, Kai Timofte, Radu Zhai, Guangtao Gao, Yixuan Cao, Yuqin Kou, Tengchuan Dong, Yunlong Jia, Ziheng Li, Yilin Wu, Wei Hu, Shuming Deng, Sibin Xiao, Pengxiang Chen, Ying Li, Kai Zhao, Kai Yuan, Kun Sun, Ming Cong, Heng Wang, Hao Fu, Lingzhi Zhang, Yusheng Zhang, Rongyu Shi, Hang Xu, Qihang Xiao, Longan Ma, Zhiliang Agarla, Mirko Celona, Luigi Rota, Claudio Schettini, Raimondo Huang, Zhiwei Li, Ya'nan Wang, Xiaotao Lei, Lei Liu, Hongye Hong, Wei Chuang, Ironhead Lin, Allen Guan, Drake Chen, Iris Lou, Kae Huang, Willy Tasi, Yachun Kao, Yvonne Fan, Haotian Kong, Fangyuan Zhou, Shiqi Liu, Hao Lai, Yu Chen, Shanshan Wang, Wenqi Wu, Haoning Chen, Chaofeng Zhu, Chunzheng Guo, Zekun Zhao, Shiling Yin, Haibing Wang, Hongkui Meftah, Hanene Brachemi Fezza, Sid Ahmed Hamidouche, Wassim Déforges, Olivier Shi, Tengfei Mansouri, Azadeh Motamednia, Hossein Bakhtiari, Amir Hossein Aznaveh, Ahmad Mahmoudi Shanghai Jiao Tong University China Eth Zürich Switzerland University of Würzburg Germany Department of Tao Technology Alibaba Group China Kuaishou Technology China Interactive Entertainment Group of Netease Inc Guangzhou China Transsion China Hefei University of Technology China Department of Informatics Systems and Communication University of Milano - Bicocca Italy Xiaomi Inc. China China Ji Liang University China FreeTech Advanced Research Center KKCompany Taiwan ByteDance China Mgtv China Fuzhou University China Shopee Information Technology Co. Ltd. S-Lab Nanyang Technological University Singapore Hunan University China Hangzhou Dianzi University China Insa Rennes Cnrs Ietr - Umr 6164 Rennes France National Higher School of Telecommunications and Ict Oran Algeria Buaa Department of Electrical and Computer Engineering Faculty of Engineering Kharazmi University Tehran Iran High Performance Computing Laboratory School of Computer Science Institute for Research in Fundamental Sciences Tehran Iran Cyberspace Research Institute Shahid Beheshti University Tehran Iran

ISBN: (纸本)9798350302493

This paper reports on the NTIRE 2023 Quality Assessment of Video Enhancement Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at CVPR 2023. This challenge is to address a major challenge in the field of video processing, namely, video quality assessment (VQA) for enhanced videos. The challenge uses the VQA Dataset for Perceptual Video Enhancement (VDPVE), which has a total of 1211 enhanced videos, including 600 videos with color, brightness, and contrast enhancements, 310 videos with deblurring, and 301 deshaked videos. The challenge has a total of 167 registered participants. 61 participating teams submitted their prediction results during the development phase, with a total of 3168 submissions. A total of 176 submissions were submitted by 37 participating teams during the final testing phase. Finally, 19 participating teams submitted their models and fact sheets, and detailed the methods they used. Some methods have achieved better results than baseline methods, and the winning methods have demonstrated superior prediction performance. © 2023 ieee.

关键词： Image enhancement

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TAME: Task Agnostic Continual Learning using Multiple Experts

TAME: Task Agnostic Continual Learning using Multiple Expert...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Haoran Zhu Maryam Majzoubi Arihant Jain Anna Choromanska New York University Google

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The goal of lifelong learning is to continuously learn from non-stationary distributions, where the non-stationarity is typically imposed by a sequence of distinct tasks. Prior works have mostly considered idealistic settings, where the identity of tasks is known at least at training. In this paper we focus on a fundamentally harder, so-called task-agnostic, setting where the task identities are not known and the learning machine needs to infer them from the observations. Our algorithm, which we call TAME (Task-Agnostic continual learning using Multiple Experts), automatically detects the shift in data distributions and switches between task expert networks in an online manner. At training, the strategy for switching between tasks hinges on an extremely simple observation that for each new coming task there occurs a statistically-significant deviation in the value of the loss function that marks the onset of this new task. At inference, the switching between experts is governed by the selector network that forwards the test sample to its relevant expert network. The selector network is trained on a small subset of data drawn uniformly at random. We control the growth of the task expert networks as well as selector network by employing pruning. Our experimental results show the efficacy of our approach on benchmark continual learning data sets, outperforming the previous task-agnostic methods and even the techniques that admit task identities at both training and testing, while at the same time using a comparable model size.

关键词： Training Continuing education computer vision conferences Computational modeling Switches Fasteners

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Condensation-Net: Memory-Efficient Network Architecture With Cross-Channel Pooling Layers and Virtual Feature Maps

Condensation-Net: Memory-Efficient Network Architecture With...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Tse-Wei Chen Motoki Yoshinaga Hongxing Gao Wei Tao Dongchao Wen Junjie Liu Kinya Osa Masami Kato Device Technology Development Headquarters Canon Inc. Tokyo Japan Canon Information Technology (Beijing) Co. Ltd. Haidian Beijing China

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Lightweight convolutional neural networks is an important research topic in the field of embedded vision. To implement image recognition tasks on a resource-limited hardware platform, it is necessary to reduce the memory size and the computational cost. The contribution of this paper is stated as follows. First, we propose an algorithm to process a specific network architecture (Condensation-Net) without increasing the maximum memory storage for feature maps. The architecture for virtual feature maps saves 26.5% of memory bandwidth by calculating the results of cross-channel pooling before storing the feature map into the memory. Second, we show that cross-channel pooling can improve the accuracy of object detection tasks, such as face detection, because it increases the number of filter weights. Compared with Tiny-YOLOv2, the improvement of accuracy is 2.0% for quantized networks and 1.5% for full-precision networks when the false-positive rate is 0.1. Last but not the least, the analysis results show that the overhead to support the cross-channel pooling with the proposed hardware architecture is negligible small. The extra memory cost to support Condensation-Net is 0.2% of the total size, and the extra gate count is only 2.1% of the total size.

关键词： Hardware computer architecture Network architecture Training Microsoft Windows Feature extraction Convolutional neural networks

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Probing Conceptual Understanding of Large Visual-Language Models

Probing Conceptual Understanding of Large Visual-Language Mo...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Madeline Schiappa Raiyaan Abdullah Shehreen Azad Jared Claypoole Michael Cogswell Ajay Divakaran Yogesh Rawat Center for Research in Computer Vision University of Central Florida SRI International

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In recent years large visual-language (V+L) models have achieved great success in various downstream tasks. However, it is not well studied whether these models have a conceptual grasp of the visual content. In this work we focus on conceptual understanding of these large V+L models. To facilitate this study, we propose novel benchmarking datasets for probing three different aspects of content understanding, 1) relations, 2) composition, and 3) context. Our probes are grounded in cognitive science and help determine if a V+L model can, for example, determine if snow garnished with a man is implausible, or if it can identify beach furniture by knowing it is located on a beach. We experimented with many recent state-of-the-art V+L models and observe that these models mostly fail to demonstrate a conceptual understanding. This study reveals several interesting insights such as that cross-attention helps learning conceptual understanding, and that CNNs are better with texture and patterns, while Transformers are better at color and shape. We further utilize some of these insights and investigate a simple finetuning technique that rewards the three conceptual understanding measures with promising initial results. The proposed benchmarks will drive the community to delve deeper into conceptual understanding and foster advancements in the capabilities of large V+L models. The code and dataset is available at: https://***/vlm-robustness

关键词： Training Visualization Shape Snow Color Benchmark testing Transformers

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Leveraging Confident Points for Accurate Depth Refinement on Embedded Systems

Leveraging Confident Points for Accurate Depth Refinement on...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Fabio Tosi Matteo Poggi Stefano Mattoccia Department of Computer Science and Engineering (DISI) University of Bologna Italy

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Despite the notable progress in stereo disparity estimation, algorithms are still prone to errors in challenging conditions. Thus, heuristic disparity refinement techniques are usually deployed to improve accuracy. Moreover, state-of-the-art methods rely on complex CNNs requiring power hungry GPUs not suited for many practical applications constrained by limited computing resources. In this paper, we propose a novel technique for disparity refinement leveraging on confidence measures and a novel, automatic learning-based selection method to discard outliers. Then, a non-local strategy infers missing disparities by analyzing the closest reliable points. This framework is very fast and does not require any hand-tuned thresholding. We assess the performance of our Non-Local Anchoring (NLA), standalone refinement techniques and methods leveraging on confidence measures inside the stereo algorithm. Our evaluation with two popular stereo algorithms shows that our proposal significantly ameliorates their accuracy on Middlebury v3 and KITTI 2015 datasets. Moreover, since our method relies only on cues computed in the disparity domain, it is suited even for COTS stereo cameras coupled with embedded systems, e.g. nVidia Jetson TX2.

关键词： Reliability Proposals Pipelines Machine learning Embedded systems Forestry Task analysis

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Block Selective Reprogramming for On-device Training of vision Transformers

Block Selective Reprogramming for On-device Training of Visi...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Sreetama Sarkar Souvik Kundu Kai Zheng Peter A. Beerel Universiy of Southern California Los Angeles USA Intel Labs San Diego USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The ubiquity of vision transformers (ViTs) for various edge applications, including personalized learning, has created the demand for on-device fine-tuning. However, training with the limited memory and computation power of edge devices remains a significant challenge. In particular, the memory required for training is much higher than that needed for inference, primarily due to the need to store activations across all layers in order to compute the gradients needed for weight updates. Previous works have explored reducing this memory requirement via frozen-weight training as well storing the activations in a compressed format. However, these methods are deemed inefficient due to their inability to provide training or inference speedup. In this paper, we first investigate the limitations of existing on-device training methods aimed at reducing memory and compute requirements. We then present block selective reprogramming (BSR) in which we fine-tune only a fraction of total blocks of a pre-trained model and selectively drop tokens based on self-attention scores of the frozen layers. To show the efficacy of BSR, we present extensive evaluations on ViT-B and DeiT-S with five different datasets. Compared to the existing alternatives, our approach simultaneously reduces training memory by up to 1.4× and compute cost by up to 2× while maintaining similar accuracy. We also showcase results for Mixture-of-Expert (MoE) models, demonstrating the effectiveness of our approach in multitask learning scenarios. Code will be available at: https://***/sreetamasarkar/BSR.

关键词： Training computer vision Costs Quantization (signal) Codes Computational modeling conferences

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Exploring AI-Based Satellite Pose Estimation: from Novel Synthetic Dataset to In-Depth Performance Evaluation

Exploring AI-Based Satellite Pose Estimation: from Novel Syn...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Fabien Gallet Christophe Marabotto Thomas Chambon Institut de Recherche Technologique Saint Exupéry

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

vision-based pose estimation using deep learning offers a promising cost effective and versatile solution for relative satellite navigation purposes. Using such a solution in closed loop to control spacecraft position is challenging from validation and performance verification viewpoint, because of the complex specification and development process. The validation task entails bridging the gap between the dataset and real-world data. In particular, modelling of Sun power and spectrum, Earth albedo, and atmospheric absence effects, is costly to replicate on ground. This article suggests a novel approach to produce synthetic space scene images. Fine statistical balancing is ensured to train and assess pose estimation solutions. A physically based camera model is used. Synthetic images incorporate realistic light flux, radiometric properties, and texture scatterings. The dataset comprises 120000 images supplemented with masks, distance maps, celestial body positions, and precise camera parameters (dataset publicly available https://***/space_rendezvous/ created in the frame of a project called RAPTOR: Robotic and Artificial intelligence Processing Test On Representative target). An analysis method using a dedicated metric library has been developed to help the assessment of the solution performance and robustness. A deeper comprehension of algorithm behavior through distribution law fitting and outlier identification is then facilitated. Finally, it is shown that implementing Region-of-Interest (RoI) training can drastically increase the performance of the Convolutional Neural Networks (CNNs) for long-range satellite pose estimation tasks.

关键词： Training Space vehicles computer vision Atmospheric modeling Pose estimation Satellite broadcasting Robot vision systems

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De-noised vision-language Fusion Guided by Visual Cues for E-commerce Product Search

De-noised Vision-language Fusion Guided by Visual Cues for E...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Zhizhang Hu Shasha Li Ming Du Arnab Dhua Douglas Gray University of California Merced Amazon Visual Search & AR

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In e-commerce applications, vision-language multimodal transformer models play a pivotal role in product search. The key to successfully training a multimodal model lies in the alignment quality of image-text pairs in the dataset. However, the data in practice is often automatically collected with minimal manual intervention. Hence the alignment of image-text pairs is far from ideal. In e-commerce, this misalignment can stem from noisy and redundant non-visual-descriptive text attributes in the product description. To address this, we introduce the MultiModal alignment-guided Learned Token Pruning (MM-LTP) method. MM-LTP employs token pruning, conventionally used for computational efficiency, to perform online text cleaning during multimodal model training. By enabling the model to discern and discard unimportant tokens, it is able to train with implicitly cleaned image-text pairs. We evaluate MM-LTP using a benchmark multimodal e-commerce dataset comprising over 710,000 unique Amazon products. Our evaluation hinges on visual search, a prevalent e-commerce feature. Through MM-LTP, we demonstrate that refining text tokens enhances the paired image branch's training, which leads to significantly improved visual search performance.

关键词： Training Visualization Computational modeling Refining Manuals Fasteners Transformers

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Event detection in coarsely annotated sports videos via parallel multi receptive field 1D convolutions

Event detection in coarsely annotated sports videos via para...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Kanav Vats Mehrnaz Fani Pascale Walters David A. Clausi John Zelek University of Waterloo Waterloo Ontario Canada

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

In problems such as sports video analytics, it is difficult to obtain accurate frame level annotations and exact event duration because of the lengthy videos and sheer volume of video data. This issue is even more pronounced in fast-paced sports such as ice hockey. Obtaining annotations on a coarse scale can be much more practical and time efficient. We propose the task of event detection in coarsely annotated videos. We introduce a multi-tower temporal convolutional network architecture for the proposed task. The network, with the help of multiple receptive fields, processes information at various temporal scales to account for the uncertainty with regard to the exact event location and duration. We demonstrate the effectiveness of the multi-receptive field architecture through appropriate ablation studies. The method is evaluated on two tasks - event detection in coarsely annotated hockey videos in the NHL dataset and event spotting in soccer on the SoccerNet dataset. The two datasets lack frame-level annotations and have very distinct event frequencies. Experimental results demonstrate the effectiveness of the network by obtaining a 55% average F1 score on the NHL dataset and by achieving competitive performance compared to the state of the art on the SoccerNet dataset. We believe our approach will help develop more practical pipelines for event detection in sports video.

关键词： Videos Poles and towers Event detection Games Task analysis computer vision

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Learning Furniture Compatibility with Graph Neural Networks

Learning Furniture Compatibility with Graph Neural Networks

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Luisa F. Polanía Mauricio Flores Matthew Nokleby Yiran Li Target Corporation Sunnyvale California USA

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

We propose a graph neural network (GNN) approach to the problem of predicting the stylistic compatibility of a set of furniture items from images. While most existing results are based on siamese networks which evaluate pairwise compatibility between items, the proposed GNN architecture exploits relational information among groups of items. We present two GNN models, both of which comprise a deep CNN that extracts a feature representation for each image, a gated recurrent unit (GRU) network that models interactions between the furniture items in a set, and an aggregation function that calculates the compatibility score. In the first model, a generalized contrastive loss function that promotes the generation of clustered embeddings for items belonging to the same furniture set is introduced. Also, in the first model, the edge function between nodes in the GRU and the aggregation function are fixed in order to limit model complexity and allow training on smaller datasets; in the second model, the edge function and aggregation function are learned directly from the data. We demonstrate state-of-the art accuracy for compatibility prediction and "fill in the blank" tasks on the Bonn and Singapore furniture datasets. We further introduce a new dataset, called the Target Furniture Collections dataset, which contains over 6000 furniture items that have been hand-curated by stylists to make up 1632 compatible sets. We also demonstrate superior prediction accuracy on this dataset.

关键词： Feature extraction Computational modeling Data models Neural networks computer vision Logic gates Task analysis

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