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The Temporal Opportunist: Self-Supervised Multi-Frame Monocular Depth

The Temporal Opportunist: Self-Supervised Multi-Frame Monocu...

引用

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

作者： Watson, Jamie Mac Aodha, Oisin Prisacariu, Victor Brostow, Gabriel Firman, Michael Niantic San Francisco CA 94111 USA Univ Edinburgh Edinburgh Midlothian Scotland Univ Oxford Oxford England UCL London England

ISBN: (纸本)9781665445092

Self-supervised monocular depth estimation networks are trained to predict scene depth using nearby frames as a supervision signal during training. However, for many applications, sequence information in the form of video frames is also available at test time. The vast majority of monocular networks do not make use of this extra signal, thus ignoring valuable information that could be used to improve the predicted depth. Those that do, either use computationally expensive test-time refinement techniques or off-the-shelf recurrent networks, which only indirectly make use of the geometric information that is inherently available. We propose ManyDepth, an adaptive approach to dense depth estimation that can make use of sequence information at test time, when it is available. Taking inspiration from multi-view stereo, we propose a deep end-to-end cost volume based approach that is trained using self-supervision only. We present a novel consistency loss that encourages the network to ignore the cost volume when it is deemed unreliable, e.g. in the case of moving objects, and an augmentation scheme to cope with static cameras. Our detailed experiments on both KITTI and Cityscapes show that we outperform all published self-supervised baselines, including those that use single or multiple frames at test time.

关键词： Training computer vision Costs Adaptive systems Computational modeling Estimation Cameras

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How to Calibrate Your Event Camera

How to Calibrate Your Event Camera

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

作者： Muglikar, Manasi Gehrig, Mathias Gehrig, Daniel Scaramuzza, Davide Univ Zurich Dept Informat Zurich Switzerland Univ Zurich Dept Neuroinformat Zurich Switzerland Swiss Fed Inst Technol Zurich Switzerland

ISBN: (纸本)9781665448994

We propose a generic event camera calibration framework using image reconstruction. Instead of relying on blinking LED patterns or external screens, we show that neural-network-based image reconstruction is well suited for the task of intrinsic and extrinsic calibration of event cameras. The advantage of our proposed approach is that we can use standard calibration patterns that do not rely on active illumination. Furthermore, our approach enables the possibility to perform extrinsic calibration between frame-based and event-based sensors without additional complexity. Both simulation and real-world experiments indicate that calibration through image reconstruction is accurate under common distortion models and a wide variety of distortion parameters.

关键词： Computational modeling Robot vision systems Cameras Distortion Calibration Sensors pattern recognition

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Object-Aware Distillation Pyramid for Open-Vocabulary Object Detection

Object-Aware Distillation Pyramid for Open-Vocabulary Object...

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

作者： Wang, Luting Liu, Yi Du, Penghui Ding, Zihan Liao, Yue Qi, Qiaosong Chen, Biaolong Liu, Si Beihang Univ Inst Artificial Intelligence Beijing Peoples R China Alibaba Grp Hangzhou Peoples R China Beihang Univ Hangzhou Innovat Inst Beijing Peoples R China

ISBN: (纸本)9798350301298

Open-vocabulary object detection aims to provide object detectors trained on a fixed set of object categories with the generalizability to detect objects described by arbitrary text queries. Previous methods adopt knowledge distillation to extract knowledge from Pretrained vision-and-Language Models (PVLMs) and transfer it to detectors. However, due to the non-adaptive proposal cropping and single-level feature mimicking processes, they suffer from information destruction during knowledge extraction and inefficient knowledge transfer. To remedy these limitations, we propose an Object-Aware Distillation Pyramid (OADP) framework, including an Object-Aware Knowledge Extraction (OAKE) module and a Distillation Pyramid (DP) mechanism. When extracting object knowledge from PVLMs, the former adaptively transforms object proposals and adopts object-aware mask attention to obtain precise and complete knowledge of objects. The latter introduces global and block distillation for more comprehensive knowledge transfer to compensate for the missing relation information in object distillation. Extensive experiments show that our method achieves significant improvement compared to current methods. Especially on the MS-COCO dataset, our OADP framework reaches 35.6 mAP(50)(N), surpassing the current state-of-the-art method by 3.3 mAP(50)(N). Code is released at https://***/LutingWang/OADP.

关键词： detection recognition: Categorization retrieval

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Encapsulating the impact of transfer learning, domain knowledge and training strategies in deep-learning based architecture: A biometric based case study 31

Encapsulating the impact of transfer learning, domain knowle...

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

作者： Singh, Avantika Nigam, Aditya Indian Inst Technol Mandi Mandi India

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

In this paper, efforts have been made to analyze the impact of training strategies, transfer learning and domain knowledge on two biometric-based problems namely: three class oculus classification and fingerprint sensor classification. For analyzing these problems we have considered deep-learning based architecture and evaluated our results on benchmark contact-lens datasets like IIIT-D, ND, IIT-K ( our model is publicly available) and on fingerprint datasets like FVC-2002, FVC-2004, FVC-2006, IIITD-MOLF, IIT-K. In-depth feature analysis of various proposed deep-learning models has been done in order to infer that indeed training in different ways along with transfer learning and domain knowledge plays a vital role in deciding the learning ability of any network.

关键词： Lenses Iris recognition Training Biological system modeling Knowledge engineering pattern recognition Analytical models

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SymDNN: Simple & Effective Adversarial Robustness for Embedded Systems

SymDNN: Simple & Effective Adversarial Robustness for Embedd...

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

作者： Dey, Swarnava Dasgupta, Pallab Chakrabarti, Partha P. Indian Inst Technol Kharagpur Kharagpur 721302 W Bengal India

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We propose SymDNN, a Deep Neural Network (DNN) inference scheme, to segment an input image into small patches, replace those patches with representative symbols, and use the reconstructed image for CNN inference. This approach of deconstruction of images, and the reconstruction from cluster centroids trained on clean images, enhances robustness against adversarial attacks. The input transform used in SymDNN is learned from very large datasets, making it difficult to approximate for adaptive adversarial attacks. For example, SymDNN achieves 23% and 42% robust accuracy at L-infinity attack strengths of 8/255 and 4/255 respectively, against BPDA under a complete white box setting, where most input processing based defenses break completely. SymDNN is not a future-proof adversarial defense that can defend any attack, but it is one of the few readily usable defenses in resource-limited embedded systems that defends against a wide range of attacks.

关键词： computer vision Adaptation models Visualization Embedded systems Computational modeling Symbols Transforms

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Rethinking Class Relations: Absolute-relative Supervised and Unsupervised Few-shot Learning

Rethinking Class Relations: Absolute-relative Supervised and...

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

作者： Zhang, Hongguang Koniusz, Piotr Jian, Songlei Li, Hongdong Torr, Philip H. S. AMS Syst Engn Inst Beijing Peoples R China Australian Natl Univ Canberra ACT Australia Data61 CSIRO Sydney NSW Australia Univ Oxford Oxford England Natl Univ Def Technol Changsha Hunan Peoples R China

ISBN: (纸本)9781665445092

The majority of existing few-shot learning methods describe image relations with binary labels. However, such binary relations are insufficient to teach the network complicated real-world relations, due to the lack of decision smoothness. Furthermore, current few-shot learning models capture only the similarity via relation labels, but they are not exposed to class concepts associated with objects, which is likely detrimental to the classification performance due to underutilization of the available class labels. For instance, children learn the concept of tiger from a few of actual examples as well as from comparisons of tiger to other animals. Thus, we hypothesize that both similarity and class concept learning must be occurring simultaneously. With these observations at hand, we study the fundamental problem of simplistic class modeling in current few-shot learning methods. We rethink the relations between class concepts, and propose a novel Absolute-relative Learning paradigm to fully take advantage of label information to refine the image an relation representations in both supervised and unsupervised scenarios. Our proposed paradigm improves the performance of several state-of-the-art models on publicly available datasets.

关键词： Training Learning systems computer vision Protocols Annotations Animals Semantics

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Modeling Relationships in Referential Expressions with Compositional Modular Networks 30

Modeling Relationships in Referential Expressions with Compo...

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30th ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Hu, Ronghang Rohrbach, Marcus Andreas, Jacob Darrell, Trevor Saenko, Kate Univ Calif Berkeley Berkeley CA 94720 USA Boston Univ Boston MA 02215 USA

ISBN: (纸本)9781538604571

People often refer to entities in an image in terms of their relationships with other entities. For example, the black cat sitting under the table refers to both a black cat entity and its relationship with another table entity. Understanding these relationships is essential for interpreting and grounding such natural language expressions. Most prior work focuses on either grounding entire referential expressions holistically to one region, or localizing relationships based on a fixed set of categories. In this paper we instead present a modular deep architecture capable of analyzing referential expressions into their component parts, identifying entities and relationships mentioned in the input expression and grounding them all in the scene. We call this approach Compositional Modular Networks (CMNs): a novel architecture that learns linguistic analysis and visual inference end-to-end. Our approach is built around two types of neural modules that inspect local regions and pairwise interactions between regions. We evaluate CMNs on multiple referential expression datasets, outperforming state-of-the-art approaches on all tasks.

关键词： Visualization Grounding Feature extraction Natural languages computer vision Cats

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φ-SfT: Shape-from-Template with a Physics-Based Deformation Model

φ-SfT: Shape-from-Template with a Physics-Based Deformation...

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

作者： Kairanda, Navami Tretschk, Edith Elgharib, Mohamed Theobalt, Christian Golyanik, Vladislav Max Planck Inst Informat SIC Saarbrucken Germany Saarland Univ SIC Saarbrucken Germany

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Shape-from-Template (SIT) methods estimate 3D surface deformations from a single monocular RGB camera while assuming a 3D state known in advance (a template). This is an important yet challenging problem due to the under-constrained nature of the monocular setting. Existing SfT techniques predominantly use geometric and simplified deformation models, which often limits their reconstruction abilities. In contrast to previous works, this paper proposes a new SfT approach explaining 2D observations through physical simulations accounting for forces and material properties. Our differentiable physics simulator regularises the surface evolution and optimises the material elastic properties such as bending coefficients, stretching stiffness and density. We use a differentiable renderer to minimise the dense reprojection error between the estimated 3D states and the input images and recover the deformation parameters using an adaptive gradient-based optimisation. For the evaluation, we record with an RGB-D camera challenging real surfaces exposed to physical forces with various material properties and textures. Our approach significantly reduces the 3D reconstruction error compared to multiple competing methods.

关键词： Deformable models Surface reconstruction Three-dimensional displays Cameras Rendering (computer graphics) Data models Surface texture

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Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challenges

Towards Semantic Segmentation of Urban-Scale 3D Point Clouds...

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

作者： Hu, Qingyong Yang, Bo Khalid, Sheikh Xiao, Wen Trigoni, Niki Markham, Andrew Univ Oxford Oxford England Hong Kong Polytech Univ Hong Kong Peoples R China Sensat Ltd London England Newcastle Univ Newcastle Upon Tyne Tyne & Wear England

ISBN: (纸本)9781665445092

An essential prerequisite for unleashing the potential of supervised deep learning algorithms in the area of 3D scene understanding is the availability of large-scale and richly annotated datasets. However, publicly available datasets are either in relative small spatial scales or have limited semantic annotations due to the expensive cost of data acquisition and data annotation, which severely limits the development of fine-grained semantic understanding in the context of 3D point clouds. In this paper, we present an urban-scale photogrammetric point cloud dataset with nearly three billion richly annotated points, which is three times the number of labeled points than the existing largest photogrammetric point cloud dataset. Our dataset consists of large areas from three UK cities, covering about 7.6 km(2) of the city landscape. In the dataset, each 3D point is labeled as one of 13 semantic classes. We extensively evaluate the performance of state-of-the-art algorithms on our dataset and provide a comprehensive analysis of the results. In particular;we identify several key challenges towards urban-scale point cloud understanding. The dataset is available at https://***/QingyongHu/SensatUrban.

关键词： Deep learning computer vision Three-dimensional displays Costs Annotations Semantics Urban areas

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OW-DETR: Open-world Detection Transformer

OW-DETR: Open-world Detection Transformer

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

作者： Gupta, Akshita Narayan, Sanath Joseph, K. J. Khan, Salman Khan, Fahad Shahbaz Shah, Mubarak Incept Inst Artificial Intelligence Abu Dhabi U Arab Emirates IIT Hyderabad Hyderabad India Australian Natl Univ Canberra ACT Australia Mohamed Bin Zayed Univ Artificial Intelligence Abu Dhabi U Arab Emirates Linkoping Univ CVL Linkoping Sweden Univ Cent Florida Orlando FL 32816 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Open-world object detection (OWOD) is a challenging computer vision problem, where the task is to detect a known set of object categories while simultaneously identifying unknown objects. Additionally, the model must incrementally learn new classes that become known in the next training episodes. Distinct from standard object detection, the OWOD setting poses significant challenges for generating quality candidate proposals on potentially unknown objects, separating the unknown objects from the background and detecting diverse unknown objects. Here, we introduce a novel end-to-end transformer-based framework, OW-DETR, for open-world object detection. The proposed OW-DETR comprises three dedicated components namely, attention-driven pseudo-labeling, novelty classification and objectness scoring to explicitly address the aforementioned OWOD challenges. Our OW-DETR explicitly encodes multi-scale contextual information, possesses less inductive bias, enables knowledge transfer from known classes to the unknown class and can better discriminate between unknown objects and background. Comprehensive experiments are performed on two benchmarks: MS-COCO and PASCAL VOC. The extensive ablations reveal the merits of our proposed contributions. Further, our model outperforms the recently introduced OWOD approach, ORE, with absolute gains ranging from 1.8% to 3.3% in terms of unknown recall on MS-COCO. In the case of incremental object detection, OW-DETR outperforms the state-of-theart for all settings on PASCAL VOC. Our code is available at https://***/akshitac8/OW-DETR.

关键词： Training computer vision Object detection Transformers pattern recognition Proposals Object recognition

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