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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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Does Interference Exist When Training a Once-For-All Network?

Does Interference Exist When Training a Once-For-All Network...

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

作者： Shipard, Jordan Wiliem, Arnold Fookes, Clinton Queensland Univ Technol Signal Proc Artificial Intelligence & Vis Technol Brisbane Qld Australia Sentient Vis Syst Port Melbourne Vic Australia

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The Once-For-All (OFA) method offers an excellent pathway to deploy a trained neural network model into multiple target platforms by utilising the supernet-subnet architecture. Once trained, a subnet can be derived from the supernet (both architecture and trained weights) and deployed directly to the target platform with little to no retraining or fine-tuning. To train the subnet population, OFA uses a novel training method called Progressive Shrinking (PS) which is designed to limit the negative impact of interference during training. It is believed that higher interference during training results in lower subnet population accuracies. In this work we take a second look at this interference effect. Surprisingly, we find that interference mitigation strategies do not have a large impact on the overall subnet population performance. Instead, we find the subnet architecture selection bias during training to be a more important aspect. To show this, we propose a simple-yet-effective method called Random Subnet Sampling (RSS), which does not have mitigation on the interference effect. Despite no mitigation, RSS is able to produce a better performing subnet population than PS in four small-to-medium-sized datasets;suggesting that the interference effect does not play a pivotal role in these datasets. Due to its simplicity, RSS provides a 1.9x reduction in training times compared to PS. A 6.1x reduction can also be achieved with a reasonable drop in performance when the number of RSS training epochs are reduced.

关键词： Training computer vision Codes conferences Sociology Neural networks Interference

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Self-Supervised Learning to Guide Scientifically Relevant Categorization of Martian Terrain Images

Self-Supervised Learning to Guide Scientifically Relevant Ca...

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

作者： Panambur, Tejas Chakraborty, Deep Meyer, Melissa Milliken, Ralph Learned-Miller, Erik Parente, Mario UMass Amherst Dept Elect & Comp Engn Amherst MA 01003 USA UMass Amherst Manning Coll Informat & Comp Sci Amherst MA 01003 USA Brown Univ Dept Earth Environm & Planetary Sci Providence RI 02912 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Automatic terrain recognition in Mars rover images is an important problem not just for navigation, but for scientists interested in studying rock types, and by extension, conditions of the ancient Martian paleoclimate and habitability. Existing approaches to label Martian terrain either involve the use of non-expert annotators producing taxonomies of limited granularity (e.g. soil, sand, bedrock, float rock, etc.), or rely on generic class discovery approaches that tend to produce perceptual classes such as rover parts and landscape, which are irrelevant to geologic analysis. Expert-labeled datasets containing granular geological/geomorphological terrain categories are rare or inaccessible to public, and sometimes require the extraction of relevant categorical information from complex annotations. In order to facilitate the creation of a dataset with detailed terrain categories, we present a self-supervised method that can cluster sedimentary textures in images captured from the Mast camera onboard the Curiosity rover (Mars Science Laboratory). We then present a qualitative analysis of these clusters and describe their geologic significance via the creation of a set of granular terrain categories. The precision and geologic validation of these automatically discovered clusters suggest that our methods are promising for the rapid classification of important geologic features and will therefore facilitate our long-term goal of producing a large, granular, and publicly available dataset for Mars terrain recognition. Code and datasets are available at https://***/TejasPanambur/mastcam.

关键词： Space vehicles Mars Image recognition Navigation Databases Taxonomy Self-supervised learning

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Holistic Approach to Measure Sample-level Adversarial Vulnerability and its Utility in Building Trustworthy Systems

Holistic Approach to Measure Sample-level Adversarial Vulner...

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

作者： Nayak, Gaurav Kumar Rawal, Ruchit Lal, Rohit Patil, Himanshu Chakraborty, Anirban Indian Inst Sci Bangalore Karnataka India

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Adversarial attack perturbs an image with an imperceptible noise, leading to incorrect model prediction. Recently, a few works showed inherent bias associated with such attack (robustness bias), where certain subgroups in a dataset (e.g. based on class, gender, etc.) are less robust than others. This bias not only persists even after adversarial training, but often results in severe performance discrepancies across these subgroups. Existing works characterize the subgroup's robustness bias by only checking individual sample's proximity to the decision boundary. In this work, we argue that this measure alone is not sufficient and validate our argument via extensive experimental analysis. It has been observed that adversarial attacks often corrupt the high-frequency components of the input image. We, therefore, propose a holistic approach for quantifying adversarial vulnerability of a sample by combining these different perspectives, i.e., degree of model's reliance on high-frequency features and the (conventional) sample-distance to the decision boundary. We demonstrate that by reliably estimating adversarial vulnerability at the sample level using the proposed holistic metric, it is possible to develop a trustworthy system where humans can be alerted about the incoming samples that are highly likely to be misclassified at test time. This is achieved with better precision when our holistic metric is used over individual measures. To further corroborate the utility of the proposed holistic approach, we perform knowledge distillation in a limited-sample setting. We observe that the student network trained with the subset of samples selected using our combined metric performs better than both the competing baselines, viz., where samples are selected randomly or based on their distances to the decision boundary.

关键词： Measurement Training Knowledge engineering computer vision conferences Predictive models Reliability engineering

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Self-Supervised Voxel-Level Representation Rediscovers Subcellular Structures in Volume Electron Microscopy

Self-Supervised Voxel-Level Representation Rediscovers Subce...

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

作者： Han, Hongqing Dmitrieva, Mariia Sauer, Alexander Tam, Ka Ho Rittscher, Jens Univ Oxford Inst Biomed Engn Dept Engn Sci Oxford England

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Making sense of large volumes of biological imaging data without human annotation often relies on unsupervised representation learning. Although efforts have been made to representing cropped-out microscopy images of single cells and single molecules, a more robust and general model that effectively maps every voxel in a whole cell volume onto a latent space is still lacking. Here, we use variational auto-encoder and metric learning to obtain a voxel-level representation, and explore using it for unsupervised segmentation. To our knowledge we are the first to present self-supervised voxel-level representation and subsequent unsupervised segmentation results for a complete cell. We improve upon earlier work by proposing an innovative approach to separate latent space into a semantic subspace and a transformational subspace, and only use the semantic representation for segmentation. We show that in the learned semantic representation the major subcellular components are visually distinguishable and the semantic subspace is more transformation-invariant than another sample latent subspace of equal dimension. For unsupervised segmentation we found that our model manages to automatically rediscover and separate the major classes with errors demonstrating spatial patterns, and further dissect the class not specified by reference segmentation into areas with consistent textures. Our segmentation outperforms a baseline by a large margin.

关键词： Representation learning Measurement Solid modeling Image segmentation computer vision conferences Biological system modeling

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A Lightweight Network for High Dynamic Range Imaging

A Lightweight Network for High Dynamic Range Imaging

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

作者： Yan, Qingsen Zhang, Song Chen, Weiye Liu, Yuhang Zhang, Zhen Zhang, Yanning Shi, Javen Qinfeng Gong, Dong Univ Adelaide Australian Inst Machine Learning Adelaide SA Australia Xidian Univ Sch Artificial Intelligence Xian Peoples R China Xidian Univ Guangzhou Inst Technol Xian Peoples R China Northwestern Polytech Univ Sch Comp Sci Xian Peoples R China Univ New South Wales Sch Comp Sci & Engn Sydney NSW Australia

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Multi-frame high dynamic range (HDR) reconstruction methods try to expand the range of illuminance with differently exposed images. They suffer from ghost artifacts when camera jittering or object moving. Several methods can generate high-quality HDR images with high computational complexity, but the inference process is too slow. However, the network with small parameters will produce unsatisfactory results. To balance the quality and computational complexity, we propose a lightweight network for HDR imaging that has small parameters and fast speed. Specifically, following AHDRNet, we employ a spatial attention module to detect the misaligned regions to avoid ghost artifacts. Considering the missing details in over/under- exposure regions, we propose a dual attention module for selectively retaining information to force the fusion network to learn more details for degenerated regions. Furthermore, we employ an encoder-decoder structure with a lightweight block to achieve the fusion process. As a result, the high-quality content and features can be reconstructed after the attention module. Finally, we fuse high-resolution features and the encoder-decoder features into the HDR imaging results. Experimental results demonstrate that the proposed method performs favorably against the state-of-the-art methods, achieving a PSNR of 39.05 and a PSNR-mu of 37.27 with 156.12 GMAcs in NTIRE 2022 HDR Challenge (Track 2 Fidelity).

关键词： computer vision Fuses conferences Force Dynamic range Reconstruction algorithms Cameras

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MANIQA: Multi-dimension Attention Network for No-Reference Image Quality Assessment

MANIQA: Multi-dimension Attention Network for No-Reference I...

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

作者： Yang, Sidi Wu, Tianhe Shi, Shuwei Lao, Shanshan Gong, Yuan Cao, Mingdeng Wang, Jiahao Yang, Yujiu Tsinghua Univ Tsinghua Shenzhen Int Grad Sch Shenzhen Peoples R China Tsinghua Univ Shenzhen Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

No-Reference Image Quality Assessment (NR-IQA) aims to assess the perceptual quality of images in accordance with human subjective perception. Unfortunately, existing NR-IQA methods are far from meeting the needs of predicting accurate quality scores on GAN-based distortion images. To this end, we propose Multi-dimension Attention Network for no-reference Image Quality Assessment (MANIQA) to improve the performance on GAN-based distortion. We firstly extract features via ViT, then to strengthen global and local interactions, we propose the Transposed Attention Block (TAB) and the Scale Swin Transformer Block (SSTB). These two modules apply attention mechanisms across the channel and spatial dimension, respectively. In this multi-dimensional manner, the modules cooperatively increase the interaction among different regions of images globally and locally. Finally, a dual branch structure for patch-weighted quality prediction is applied to predict the final score depending on the weight of each patch's score. Experimental results demonstrate that MANIQA outperforms state-of-the-art methods on four standard datasets (LIVE, TID2013, CSIQ, and KADID-10K) by a large margin. Besides, our method ranked first place in the final testing phase of the NTIRE 2022 Perceptual Image Quality Assessment Challenge Track 2: No-Reference. Codes and models are available at https://***/IIGROUP/MANIQA.

关键词： Image quality Databases Distortion Feature extraction Transformers Quality assessment pattern recognition

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The 6th AI City Challenge

The 6th AI City Challenge

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

作者： Naphade, Milind Wang, Shuo Anastasiu, David C. Tang, Zheng Chang, Ming-Ching Yao, Yue Zheng, Liang Rahman, Mohammed Shaiqur Venkatachalapathy, Archana Sharma, Anuj Feng, Qi Ablavsky, Vitaly Sclaroff, Stan Chakraborty, Pranamesh Li, Alice Li, Shangru Chellappa, Rama NVIDIA Corp Santa Clara CA 95051 USA Santa Clara Univ Santa Clara CA 95053 USA SUNY Albany Albany NY 12222 USA Australian Natl Univ Canberra ACT Australia Indian Inst Technol Kanpur Kanpur Uttar Pradesh India Iowa State Univ Ames IA USA Boston Univ Boston MA 02215 USA Univ Washington Seattle WA 98195 USA Johns Hopkins Univ Baltimore MD 21218 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The 6th edition of the AI City Challenge specifically focuses on problems in two domains where there is tremendous unlocked potential at the intersection of computer vision and artificial intelligence: Intelligent Traffic Systems (ITS), and brick and mortar retail businesses. The four challenge tracks of the 2022 AI City Challenge received participation requests from 254 teams across 27 countries. Track 1 addressed city-scale multi-target multi-camera (MTMC) vehicle tracking. Track 2 addressed natural-language-based vehicle track retrieval. Track 3 was a brand new track for naturalistic driving analysis, where the data were captured by several cameras mounted inside the vehicle focusing on driver safety, and the task was to classify driver actions. Track 4 was another new track aiming to achieve retail store automated checkout using only a single view camera. We released two leader boards for submissions based on different methods, including a public leader board for the contest, where no use of external data is allowed, and a general leader board for all submitted results. The top performance of participating teams established strong baselines and even outperformed the state-of-the-art in the proposed challenge tracks.

关键词： computer vision Mortar Urban areas Focusing Cameras Safety pattern recognition

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Few-Shot Image Classification Benchmarks are Too Far From Reality: Build Back Better with Semantic Task Sampling

Few-Shot Image Classification Benchmarks are Too Far From Re...

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

作者： Bennequin, Etienne Tami, Myriam Toubhans, Antoine Hudelot, Celine Univ Paris Saclay Cent Supelec Gif Sur Yvette France Sicara Paris France

ISBN: (纸本)9781665487399

Every day, a new method is published to tackle Few-Shot Image Classification, showing better and better performances on academic benchmarks. Nevertheless, we observe that these current benchmarks do not accurately represent the real industrial use cases that we encountered. In this work, through both qualitative and quantitative studies, we expose that the widely used benchmark tieredImageNet is strongly biased towards tasks composed of very semantically dissimilar classes e.g. bathtub, cabbage, pizza, schipperke, and cardoon. This makes tieredImageNet (and similar benchmarks) irrelevant to evaluate the ability of a model to solve real-life use cases usually involving more fine-grained classification. We mitigate this bias using semantic information about the classes of tieredImageNet and generate an improved, balanced benchmark. Going further, we also introduce a new benchmark for Few-Shot Image Classification using the Danish Fungi 2020 dataset. This benchmark proposes a wide variety of evaluation tasks with various fine-graininess. Moreover, this benchmark includes many-way tasks (e.g. composed of 100 classes), which is a challenging setting yet very common in industrial applications. Our experiments bring out the correlation between the difficulty of a task and the semantic similarity between its classes, as well as a heavy performance drop of state-of-the-art methods on many-way few-shot classification, raising questions about the scaling abilities of these methods. We hope that our work will encourage the community to further question the quality of standard evaluation processes and their relevance to real-life applications.

关键词： Fungi Training Limiting Shape Semantics Benchmark testing pattern recognition

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Deep Normalized Cross-Modal Hashing with Bi-Direction Relation Reasoning

Deep Normalized Cross-Modal Hashing with Bi-Direction Relati...

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

作者： Sun, Changchang Latapie, Hugo Liu, Gaowen Yan, Yan IIT Dept Comp Sci Chicago IL 60616 USA Cisco Res Emerging Technol & Incubat San Jose CA USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Due to the continuous growth of large-scale multi-modal data and increasing requirements for retrieval speed, deep cross-modal hashing has gained increasing attention recently. Most of existing studies take a similarity matrix as supervision to optimize their models, and the inner product between continuous surrogates of hash codes is utilized to depict the similarity in the Hamming space. However, all of them merely consider the relevant information to build the similarity matrix, ignoring the contribution of the irrelevant one, i.e., the categories that samples do not belong to. Therefore, they cannot effectively alleviate the effect of dissimilar samples. Moreover, due to the modality distribution difference, directly utilizing continuous surrogates of hash codes to calculate similarity may induce suboptimal retrieval performance. To tackle these issues, in this paper, we propose a novel deep normalized cross-modal hashing scheme with bi-direction relation reasoning, named Bi NCMH. Specifically, we build the multi-level semantic similarity matrix by considering bi-direction relation, i.e., consistent and inconsistent relation. It hence can holistically characterize relations among instances. Besides, we execute feature normalization on continuous surrogates of hash codes to eliminate the deviation caused by modality gap, which further reduces the negative impact of binarization on retrieval performance. Extensive experiments on two cross-modal benchmark datasets demonstrate the superiority of our model over several state-of-the-art baselines.

关键词： computer vision Codes conferences Computational modeling Semantics Bidirectional control Benchmark testing

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