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NTIRE 2024 Challenge on HR Depth from Images of Specular and Transparent Surfaces

NTIRE 2024 Challenge on HR Depth from Images of Specular and...

引用

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

作者： Pierluigi Zama Ramirez Fabio Tosi Luigi Di Stefano Radu Timofte Alex Costanzino Matteo Poggi Samuele Salti Stefano Mattoccia Yangyang Zhang Cailin Wu Zhuangda He Shuangshuang Yin Jiaxu Dong Yangchenxu Liu Hao Jiang Jun Shi Yong A Yixiang Jin Dingzhe Li Bingxin Ke Anton Obukhov Tinafu Wang Nando Metzger Shengyu Huang Konrad Schindler Yachuan Huang Jiaqi Li Junrui Zhang Yiran Wang Zihao Huang Tianqi Liu Zhiguo Cao Pengzhi Li Jui-Lin Wang Wenjie Zhu Hui Geng Yuxin Zhang Long Lan Kele Xu Tao Sun Qisheng Xu Sourav Saini Aashray Gupta Sahaj K. Mistry Aryan Shukla Vinit Jakhetiya Sunil Jaiswal Yuejin Sun Zhuofan Zheng Yi Ning Jen-Hao Cheng Hou-I Liu Hsiang-Wei Huang Cheng-Yen Yang Zhongyu Jiang Yi-Hao Peng Aishi Huang Jenq-Neng Hwang

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper reports on the NTIRE 2024 challenge on HR Depth From images of Specular and Transparent surfaces, held in conjunction with the New Trends in Image Restoration and Enhancement (NTIRE) workshop at CVPR 2024. This challenge aims to advance the research on depth estimation, specifically to address two of the main open issues in the field: high-resolution and non-Lambertian surfaces. The challenge proposes two tracks on stereo and single-image depth estimation, attracting about 120 registered participants. In the final testing stage, 2 and 8 participating teams submitted their models and fact sheets for the two tracks.

关键词： computer vision conferences Computational modeling Estimation Market research pattern recognition Image restoration

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Information Bottleneck Disentanglement for Identity Swapping

Information Bottleneck Disentanglement for Identity Swapping

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Gao, Gege Huang, Huaibo Fu, Chaoyou Li, Zhaoyang He, Ran Univ Chinese Acad Sci Ctr Excellence Brain Sci & Intelligence Technol Sch Artificial Intelligence Natl Lab Pattern RecognitCASIACAS Beijing Peoples R China

ISBN: (纸本)9781665445092

Improving the performance of face forgery detectors often requires more identity-swapped images of higher-quality. One core objective of identity swapping is to generate identity-discriminative faces that are distinct from the target while identical to the source. To this end, properly disentangling identity and identity-irrelevant information is critical and remains a challenging endeavor. In this work, we propose a novel information disentangling and swapping network, called InfoSwap, to extract the most expressive information for identity representation from a pre-trained face recognition model. The key insight of our method is to formulate the learning of disentangled representations as optimizing an information bottleneck tradeoff, in terms of finding an optimal compression of the pretrained latent features. Moreover, a novel identity contrastive loss is proposed for further disentanglement by requiring a proper distance between the generated identity and the target. While the most prior works have focused on using various loss functions to implicitly guide the learning of representations, we demonstrate that our model can provide explicit supervision for learning disentangled representations, achieving impressive performance in generating more identity-discriminative swapped faces.

关键词： computer vision Face recognition Buildings Detectors Benchmark testing Feature extraction Forgery

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CutPaste: Self-Supervised Learning for Anomaly Detection and Localization

CutPaste: Self-Supervised Learning for Anomaly Detection and...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Li, Chun-Liang Sohn, Kihyuk Yoon, Jinsung Pfister, Tomas Google Cloud AI Res Mountain View CA 94043 USA

ISBN: (纸本)9781665445092

We aim at constructing a high performance model for defect detection that detects unknown anomalous patterns of an image without anomalous data. To this end, we propose a two-stage framework for building anomaly detectors using normal training data only. We first learn self-supervised deep representations and then build a generative one-class classifier on learned representations. We learn representations by classifying normal data from the CutPaste, a simple data augmentation strategy that cuts an image patch and pastes at a random location of a large image. Our empirical study on MVTec anomaly detection dataset demonstrates the proposed algorithm is general to be able to detect various types of real-world defects. We bring the improvement upon previous arts by 3.1 AUCs when learning representations from scratch. By transfer learning on pretrained representations on ImageNet, we achieve a new state-of-the-art 96.6 AUC. Lastly, we extend the framework to learn and extract representations from patches to allow localizing defective areas without annotations during training.

关键词： Location awareness Training computer vision Buildings Transfer learning Training data Detectors

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AlphaMatch: Improving Consistency for Semi-supervised Learning with Alpha-divergence

AlphaMatch: Improving Consistency for Semi-supervised Learni...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Gong, Chengyue Wang, Dilin Liu, Qiang Univ Texas Austin Austin TX 78712 USA

ISBN: (纸本)9781665445092

Semi-supervised learning (SSL) is a key approach toward more data-efficient machine learning by jointly leverage both labeled and unlabeled data. We propose AlphaMatch, an efficient SSL method that leverages data augmentations, by efficiently enforcing the label consistency between the data points and the augmented data derived from them. Our key technical contribution lies on: 1) using alpha-divergence to prioritize the regularization on data with high confidence, achieving similar effect as FixMatch [32] but in a more flexible fashion, and 2) proposing an optimization-based, EM-like algorithm to enforce the consistency, which enjoys better convergence than iterative regularization procedures used in recent SSL methods such as FixMatch, UDA, and MixMatch. AlphaMatch is simple and easy to implement, and consistently outperforms prior arts on standard benchmarks, e.g. CIFAR-10, SVHN, CIFAR-100, STL-10. Specifically, we achieve 91.3% test accuracy on CIFAR-10 with just 4 labelled data per class, substantially improving over the previously best 88.7% accuracy achieved by FixMatch.

关键词： Object detection Machine learning Semisupervised learning Benchmark testing Iterative algorithms pattern recognition Machine translation

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Visual Semantic Role Labeling for Video Understanding

Visual Semantic Role Labeling for Video Understanding

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Sadhu, Arka Gupta, Tanmay Yatskar, Mark Nevatia, Ram Kembhavi, Aniruddha Univ Southern Calif Los Angeles CA 90007 USA Univ Penn Philadelphia PA 19104 USA PRIOR Allen Inst AI Seattle WA USA PRIOR AI2 Seattle WA USA

ISBN: (纸本)9781665445092

We propose a new framework for understanding and representing related salient events in a video using visual semantic role labeling. We represent videos as a set of related events, wherein each event consists of a verb and multiple entities that fulfill various roles relevant to that event. To study the challenging task of semantic role labeling in videos or VidSRL, we introduce the VidSitu benchmark, a large scale video understanding data source with 29K 10-second movie clips richly annotated with a verb and semantic-roles every 2 seconds. Entities are co-referenced across events within a movie clip and events are connected to each other via event-event relations. Clips in VidSitu are drawn from a large collection of movies (similar to 3K) and have been chosen to be both complex (similar to 4.2 unique verbs within a video) as well as diverse (similar to 200 verbs have more than 100 annotations each). We provide a comprehensive analysis of the dataset in comparison to other publicly available video understanding benchmarks, several illustrative baselines and evaluate a range of standard video recognition models. Our code and dataset is available at ***.

关键词： Visualization computer vision Annotations Semantics Benchmark testing Motion pictures pattern recognition

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Attention Guidance Distillation Network for Efficient Image Super-Resolution

Attention Guidance Distillation Network for Efficient Image ...

引用

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

作者： Hongyuan Wang Ziyan Wei Qingting Tang Shuli Cheng Liejun Wang Yongming Li School of Computer Science and Technology Xinjiang University Ürümqi China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Over the past decade, neural network-based super-resolution techniques have been developed on a large scale with impressive achievements. Many novel solutions have been proposed, among which lightweight solutions based on convolutional neural networks have been designed for applications in edge devices. To better realize this application, we propose a more lightweight attention guidance distillation network (AGDN). We design the attention guidance distillation block (AGDB) with more efficient space, channel and self-attention as the infrastructure of AGDN. Specifically, multi-level variance-aware spatial attention (MVSA) is designed to better capture structurally information-rich regions with new multi-scale convolution and local variance alignment. Reallocated contrast-aware channel attention (RCCA) is designed to enhance the processing of common information in all channels while redistributing weights across channels. Sparse global self-attention (SGSA) is introduced for selecting the most useful similarity values for image reconstruction. Extensive experiments demonstrate that AGDN strikes a better balance between performance and complexity compared to other models, achieving SOTA performance on several benchmark tests. In addition, our AGDN-S ranks first in the FLOPs track and second in the Parameters track of the NTIRE 2024 Efficient SR Challenge. The code is available at https://***/daydreamer2024/AGDN.

关键词： Runtime Limiting Convolution Superresolution Feature extraction pattern recognition Image restoration

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Counterfactual VQA: A Cause-Effect Look at Language Bias

Counterfactual VQA: A Cause-Effect Look at Language Bias

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Niu, Yulei Tang, Kaihua Zhang, Hanwang Lu, Zhiwu Hua, Xian-Sheng Wen, Ji-Rong Nanyang Technol Univ Singapore Singapore Renmin Univ China Gaoling Sch Artificial Intelligence Beijing Peoples R China Beijing Key Lab Big Data Management & Anal Method Beijing Peoples R China Alibaba Grp Damo Acad Hangzhou Peoples R China

ISBN: (纸本)9781665445092

VQA models may tend to rely on language bias as a shortcut and thus fail to sufficiently learn the multi-modal knowledge from both vision and language. Recent debiasing methods proposed to exclude the language prior during inference. However, they fail to disentangle the "good" language context and "bad" language bias from the whole. In this paper, we investigate how to mitigate language bias in VQA. Motivated by causal effects, we proposed a novel counterfactual inference framework, which enables us to capture the language bias as the direct causal effect of questions on answers and reduce the language bias by subtracting the direct language effect from the total causal effect. Experiments demonstrate that our proposed counterfactual inference framework 1) is general to various VQA backbones and fusion strategies, 2) achieves competitive performance on the language-bias sensitive VQA-CP dataset while performs robustly on the balanced VQA v2 dataset without any augmented data.

关键词： computer vision Codes Linguistics Robustness Cognition pattern recognition

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Divide and Conquer Boosting for Enhanced Traffic Safety Description and Analysis with Large vision Language Model

Divide and Conquer Boosting for Enhanced Traffic Safety Desc...

引用

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

作者： Khai Trinh Xuan Khoi Nguyen Nguyen Bach Hoang Ngo Vu Dinh Xuan Minh-Hung An Quang-Vinh Dinh Ho Chi Minh City University of Technology VNU-HCM Vietnam Vietnam National University Ho Chi Minh City University of Science VNU-HCM Vietnam University of Information Technology VNU-HCM Vietnam FPT Telecom Vietnam AI Lab - AI Vietnam

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The increasing complexity of traffic dynamics has underscored the necessity for advanced traffic safety description and analysis, challenging the efficacy of current methodologies in comprehensively understanding and predicting safety conditions from transportation videos. This paper addresses these challenges by analyzing specific segments crucial for precise traffic safety descriptions. Through this examination, we introduce an innovative preprocessing method named "segment extraction", facilitating the creation of a novel segment-based training dataset. Additionally, we present a practical two-stage training framework specifically tailored for this dataset. This framework is designed to produce accurate descriptions of traffic safety by incorporating the unique attributes of our segment-based training datasets. Leveraging these contributions, our method achieved a notable 2nd rank with a score of 32.8877 in the AI City Challenge Track2 test set: Traffic Safety Description and Analysis 2024. The source code for the proposed approaches is openly accessible at https://***/AIVIETNAMResearch/AI-CIty-2024-Track2

关键词： Training Pedestrians Source coding Urban areas Data preprocessing Transportation Data models

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Towards Good Practices for Efficiently Annotating Large-Scale Image Classification Datasets

Towards Good Practices for Efficiently Annotating Large-Scal...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Liao, Yuan-Hong Kar, Amlan Fidler, Sanja Univ Toronto Toronto ON Canada Vector Inst Toronto ON Canada NVIDIA Santa Clara CA USA

ISBN: (纸本)9781665445092

Data is the engine of modern computer vision, which necessitates collecting large-scale datasets. This is expensive, and guaranteeing the quality of the labels is a major challenge. In this paper, we investigate efficient annotation strategies for collecting multi-class classification labels for a large collection of images. While methods that exploit learnt models for labeling exist, a surprisingly prevalent approach is to query humans for a fixed number of labels per datum and aggregate them, which is expensive. Building on prior work on online joint probabilistic modeling of human annotations and machine-generated beliefs, we propose modifications and best practices aimed at minimizing human labeling effort. Specifically, we make use of advances in self-supervised learning, view annotation as a semi-supervised learning problem, identify and mitigate pitfalls and ablate several key design choices to propose effective guidelines for labeling. Our analysis is done in a more realistic simulation that involves querying human labelers, which uncovers issues with evaluation using existing worker simulation methods. Simulated experiments on a 125k image subset of the ImageNet100 show that it can be annotated to 80% top-1 accuracy with 0.35 annotations per image on average, a 2.7x and 6.7x improvement over prior work and manual annotation, respectively.(1)

关键词： computer vision Analytical models Annotations Manuals Semisupervised learning Probabilistic logic pattern recognition

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Generalized Few-Shot Object Detection without Forgetting

Generalized Few-Shot Object Detection without Forgetting

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Fan, Zhibo Ma, Yuchen Li, Zeming Sun, Jian Megvii Technol Beijing Peoples R China

ISBN: (纸本)9781665445092

Recently few-shot object detection is widely adopted to deal with data-limited situations. While most previous works merely focus on the performance on few-shot categories, we claim that detecting all classes is crucial as test samples may contain any instances in realistic applications, which requires the few-shot detector to learn new concepts without forgetting. Through analysis on transfer learning based methods, some neglected but beneficial properties are utilized to design a simple yet effective few-shot detector, Retentive R-CNN. It consists of Bias-Balanced RPN to debias the pretrained RPN and Re-detector to find few-shot class objects without forgetting previous knowledge. Extensive experiments on few-shot detection benchmarks show that Retentive R-CNN significantly outperforms state-of-the-art methods on overall performance among all settings as it can achieve competitive results on few-shot classes and does not degrade the base class performance at all. Our approach has demonstrated that the long desired never-forgetting learner is available in object detection.

关键词： Measurement computer vision Transfer learning Object detection Detectors Benchmark testing Reliability engineering

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