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AdaBins: Depth Estimation Using Adaptive Bins

AdaBins: Depth Estimation Using Adaptive Bins

引用

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

作者： Bhat, Shariq Farooq Alhashim, Ibraheem Wonka, Peter KAUST Thuwal Saudi Arabia

ISBN: (纸本)9781665445092

We address the problem of estimating a high quality dense depth map from a single RGB input image. We start out with a baseline encoder-decoder convolutional neural network architecture and pose the question of how the global processing of information can help improve overall depth estimation. To this end, we propose a transformer-based architecture block that divides the depth range into bins whose center value is estimated adaptively per image. The final depth values are estimated as linear combinations of the bin centers. We call our new building block AdaBins. Our results show a decisive improvement over the state-of-the-art on several popular depth datasets across all metrics. We also validate the effectiveness of the proposed block with an ablation study and provide the code and corresponding pre-trained weights of the new state-of-the-art model.

关键词： Measurement Image segmentation Three-dimensional displays Image resolution Estimation computer architecture Transformers

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Post-hoc Uncertainty Calibration for Domain Drift Scenarios

Post-hoc Uncertainty Calibration for Domain Drift Scenarios

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

作者： Tomani, Christian Gruber, Sebastian Erdem, Muhammed Ebrar Cremers, Daniel Buettner, Florian Tech Univ Munich Munich Germany Siemens AG Munich Germany German Canc Consortium Heidelberg Germany German Canc Res Ctr Heidelberg Heidelberg Germany Frankfurt Univ Frankfurt Germany

ISBN: (纸本)9781665445092

We address the problem of uncertainty calibration. While standard deep neural networks typically yield uncalibrated predictions, calibrated confidence scores that are representative of the true likelihood of a prediction can be achieved using post-hoc calibration methods. However, to date, the focus of these approaches has been on in-domain calibration. Our contribution is two-fold. First, we show that existing post-hoc calibration methods yield highly overconfident predictions under domain shift. Second, we introduce a simple strategy where perturbations are applied to samples in the validation set before performing the post-hoc calibration step. In extensive experiments, we demonstrate that this perturbation step results in substantially better calibration under domain shift on a wide range of architectures and modelling tasks.

关键词： Deep learning computer vision Uncertainty Perturbation methods Computational modeling computer architecture Calibration

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LatentPS: Image Editing Using Latent Representations in Diffusion Models

LatentPS: Image Editing Using Latent Representations in Diff...

引用

ieee Winter Applications and computer vision workshops (WACVW)

作者： Zilong Wu Hideki Murata Nayu Takahashi Qiyu Wu Yoshimasa Tsuruoka The University of Tokyo Dentsu Digital

ISBN: (数字)9798331536626

ISBN: (纸本)9798331536633

Generative image models have significantly advanced recently, enabling detailed image generation from textual prompts. However, accurately describing complex image with textual prompts alone is often challenging. To address this, we propose LatentPS, a novel method for image editing that modifies latent representations during the intermediate stages of the diffusion process. LatentPS leverages the properties of the diffusion process to maintain the structure of synthesized images while editing images through simple operations on latent representations directly. We show that LatentPS achieves promising results in various image editing tasks including moving, resizing and pasting.

关键词： Image quality computer vision Shape Image synthesis Image color analysis conferences Diffusion processes Diffusion models Object recognition

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EyePAD++: A Distillation-based approach for joint Eye Authentication and Presentation Attack Detection using Periocular Images

EyePAD++: A Distillation-based approach for joint Eye Authen...

引用

2022 ieee/CVF conference on computer vision and pattern recognition, CVPR 2022

作者： Dhar, Prithviraj Kumar, Amit Kaplan, Kirsten Gupta, Khushi Ranjan, Rakesh Chellappa, Rama Johns Hopkins University United States Meta Reality Labs

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

A practical eye authentication (EA) system targeted for edge devices needs to perform authentication and be robust to presentation attacks, all while remaining compute and latency efficient. However, existing eye-based frameworks a) perform authentication and Presentation Attack Detection (PAD) independently and b) involve significant pre-processing steps to extract the iris region. Here, we introduce a joint framework for EA and PAD using periocular images. While a deep Multitask Learning (MTL) network can perform both the tasks, MTL suffers from the forgetting effect since the training datasets for EA and PAD are disjoint. To overcome this, we propose Eye Authentication with PAD (EyePAD), a distillation-based method that trains a single network for EA and PAD while reducing the effect of forgetting. To further improve the EA performance, we introduce a novel approach called EyePAD++ that includes training an MTL network on both EA and PAD data, while distilling the 'versatility' of the EyePAD network through an additional distillation step. Our proposed methods outperform the SOTA in PAD and obtain near-SOTA performance in eye-to-eye verification, without any pre-processing. We also demonstrate the efficacy of EyePAD and EyePAD++ in user-to-user verification with PAD across network backbones and image quality. © 2022 ieee.

关键词： Training Performance evaluation Image quality Iris Protocols Machine vision Image edge detection

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FlowStep3D: Model Unrolling for Self-Supervised Scene Flow Estimation

FlowStep3D: Model Unrolling for Self-Supervised Scene Flow E...

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

作者： Kittenplon, Yair Eldar, Yonina C. Raviv, Dan Tel Aviv Univ Tel Aviv Israel Weizmann Inst Sci Rehovot Israel

ISBN: (纸本)9781665445092

Estimating the 3D motion of points in a scene, known as scene flow, is a core problem in computer vision. Traditional learning-based methods designed to learn end-to-end 3D flow often suffer from poor generalization. Here we present a recurrent architecture that learns a single step of an unrolled iterative alignment procedure for refining scene flow predictions. Inspired by classical algorithms, we demonstrate iterative convergence toward the solution using strong regularization. The proposed method can handle sizeable temporal deformations and suggests a slimmer architecture than competitive all-to-all correlation approaches. Trained on FlyingThings3D synthetic data only, our network successfully generalizes to real scans, outperforming all existing methods by a large margin on the KITTI self-supervised benchmark.(1)

关键词： Training Learning systems computer vision Three-dimensional displays Refining Estimation computer architecture

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Intentonomy: a Dataset and Study towards Human Intent Understanding

Intentonomy: a Dataset and Study towards Human Intent Unders...

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

作者： Jia, Menglin Wu, Zuxuan Reiter, Austin Cardie, Claire Belongie, Serge Lim, Ser-Nam Cornell Univ Ithaca NY 14853 USA Facebook AI Menlo Pk CA 94025 USA Fudan Univ Shanghai Peoples R China

ISBN: (纸本)9781665445092

An image is worth a thousand words, conveying information that goes beyond the mere visual content therein. In this paper, we study the intent behind social media images with an aim to analyze how visual information can facilitate recognition of human intent. Towards this goal, we introduce an intent dataset, Intentonomy, comprising 14K images covering a wide range of everyday scenes. These images are manually annotated with 28 intent categories derived from a social psychology taxonomy. We then systematically study whether, and to what extent, commonly used visual information, i.e., object and context, contribute to human motive understanding. Based on our findings, we conduct further study to quantify the effect of attending to object and context classes as well as textual information in the form of hashtags when training an intent classifier. Our results quantitatively and qualitatively shed light on how visual and textual information can produce observable effects when predicting intent.(1)

关键词： Training Visualization computer vision Image recognition Social networking (online) Taxonomy Psychology

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Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts

Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To...

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

作者： Changpinyo, Soravit Sharma, Piyush Ding, Nan Soricut, Radu Google Res Mountain View CA 94043 USA

ISBN: (纸本)9781665445092

The availability of large-scale image captioning and visual question answering datasets has contributed significantly to recent successes in vision-and-language pre-training. However, these datasets are often collected with overrestrictive requirements inherited from their original target tasks (e.g., image caption generation), which limit the resulting dataset scale and diversity. We take a step further in pushing the limits of vision-and-language pretraining data by relaxing the data collection pipeline used in Conceptual Captions 3M (CC3M) [54] and introduce the Conceptual 12M (CC12M), a dataset with 12 million image-text pairs specifically meant to be used for vision-and-language pre-training. We perform an analysis of this dataset and benchmark its effectiveness against CC3M on multiple downstream tasks with an emphasis on long-tail visual recognition. Our results clearly illustrate the benefit of scaling up pre-training data for vision-and-language tasks, as indicated by the new state-of-the-art results on both the neaps and Conceptual Captions benchmarks.(1)

关键词： Visualization computer vision Image recognition Pipelines Benchmark testing Data collection Knowledge discovery

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Linguistic Structures as Weak Supervision for Visual Scene Graph Generation

Linguistic Structures as Weak Supervision for Visual Scene G...

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

作者： Ye, Keren Kovashka, Adriana Univ Pittsburgh Dept Comp Sci Pittsburgh PA 15260 USA

ISBN: (纸本)9781665445092

Prior work in scene graph generation requires categorical supervision at the level of triplets-subjects and objects, and predicates that relate them, either with or without bounding box information. However, scene graph generation is a holistic task: thus holistic, contextual supervision should intuitively improve performance. In this work, we explore how linguistic structures in captions can benefit scene graph generation. Our method captures the information provided in captions about relations between individual triplets, and context for subjects and objects (e.g. visual properties are mentioned). Captions are a weaker type of supervision than triplets since the alignment between the exhaustive list of human-annotated subjects and objects in triplets, and the nouns in captions, is weak. However, given the large and diverse sources of multimodal data on the web (e.g. blog posts with images and captions), linguistic supervision is more scalable than crowdsourced triplets. We show extensive experimental comparisons against prior methods which leverage instance- and image-level supervision, and ablate our method to show the impact of leveraging phrasal and sequential context, and techniques to improve localization of subjects and objects.

关键词： Location awareness Visualization computer vision Blogs Linguistics pattern recognition Noise measurement

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Unsupervised Part Segmentation through Disentangling Appearance and Shape

Unsupervised Part Segmentation through Disentangling Appeara...

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

作者： Liu, Shilong Zhang, Lei Yang, Xiao Su, Hang Zhu, Jun Tsinghua Univ Dept Comp Sci & Tech BNRist Ctr Inst AITsinghua Bosch Joint ML Ctr Beijing 100084 Peoples R China Microsoft Corp Redmond WA 98052 USA

ISBN: (纸本)9781665445092

We study the problem of unsupervised discovery and segmentation of object parts, which, as an intermediate local representation, are capable of finding intrinsic object structure and providing more explainable recognition results. Recent unsupervised methods have greatly relaxed the dependency on annotated data which are costly to obtain, but still rely on additional information such as object segmentation mask or saliency map. To remove such a dependency and further improve the part segmentation performance, we develop a novel approach by disentangling the appearance and shape representations of object parts followed with reconstruction losses without using additional object mask information. To avoid degenerated solutions, a bottleneck block is designed to squeeze and expand the appearance representation, leading to a more effective disentanglement between geometry and appearance. Combined with a self-supervised part classification loss and an improved geometry concentration constraint, we can segment more consistent parts with semantic meanings. Comprehensive experiments on a wide variety of objects such as face, bird, and PASCAL VOC objects demonstrate the effectiveness of the proposed method.

关键词： Geometry computer vision Shape Annotations Face recognition Semantics Neural networks

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DeepSurfels: Learning Online Appearance Fusion

DeepSurfels: Learning Online Appearance Fusion

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

作者： Mihajlovic, Marko Weder, Silvan Pollefeys, Marc Oswald, Martin R. Swiss Fed Inst Technol Dept Comp Sci Zurich Switzerland Microsoft Mixed Real Zurich Switzerland AI Zurich Lab Zurich Switzerland

ISBN: (纸本)9781665445092

We present DeepSurfels, a novel hybrid scene representation for geometry and appearance information. DeepSurfels combines explicit and neural building blocks to jointly encode geometry and appearance information. In contrast to established representations, DeepSurfels better represents high-frequency textures, is well-suited for online updates of appearance information, and can be easily combined with machine learning methods. We further present an end-to-end trainable online appearance fusion pipeline that fuses information from RGB images into the proposed scene representation and is trained using self-supervision imposed by the reprojection error with respect to the input images. Our method compares favorably to classical texture mapping approaches as well as recent learning-based techniques. Moreover, we demonstrate lower runtime, improved generalization capabilities, and better scalability to larger scenes compared to existing methods.

关键词： Geometry Runtime Shape Scalability Pipelines Machine learning Rendering (computer graphics)

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