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Post-hoc Uncertainty Calibration for Domain Drift Scenarios

Post-hoc Uncertainty Calibration for Domain Drift Scenarios

引用

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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DeepLM: Large-scale Nonlinear Least Squares on Deep Learning Frameworks using Stochastic Domain Decomposition

DeepLM: Large-scale Nonlinear Least Squares on Deep Learning...

引用

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

作者： Huang, Jingwei Huang, Shan Sun, Mingwei Huawei Technol Rieman Lab Shenzhen Guangdong Peoples R China Wuhan Univ Wuhan Hubei Peoples R China

ISBN: (纸本)9781665445092

We propose a novel approach for large-scale nonlinear least squares problems based on deep learning frameworks. Nonlinear least squares are commonly solved with the Levenberg-Marquardt (LM) algorithm for fast convergence. We implement a general and efficient LM solver on a deep learning framework by designing a new backward jacobian network to enable automatic sparse jacobian matrix computation. Furthermore, we introduce a stochastic domain decomposition approach that enables batched optimization and preserves convergence for large problems. We evaluate our method by solving bundle adjustment as a fundamental problem. Experiments show that our optimizer significantly outperforms the state-of-the-art solutions and existing deep learning solvers considering quality, efficiency, and memory. Our stochastic domain decomposition enables distributed optimization, consumes little memory and time, and achieves similar quality compared to a global solver. As a result, our solver effectively solves nonlinear least squares on an extremely large scale. Our code will be available based on Pytorch(1) and Mindspore(2).

关键词： Deep learning Jacobian matrices Bundle adjustment computer vision Codes Memory management pattern recognition

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FlowStep3D: Model Unrolling for Self-Supervised Scene Flow Estimation

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

引用

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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Manifold DivideMix: A Semi-Supervised Contrastive Learning Framework for Severe Label Noise

Manifold DivideMix: A Semi-Supervised Contrastive Learning F...

引用

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

作者： Fahimeh Fooladgar Minh Nguyen Nhat To Parvin Mousavi Purang Abolmaesumi Department of Electrical and Computer Engineering University of British Columbia CA School of Computing Queen’s University CA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Deep neural networks have proven to be highly effective when large amounts of data with clean labels are available. However, their performance degrades when training data contains noisy labels, leading to poor generalization on the test set. Real-world datasets contain noisy label samples that either have similar visual semantics to other classes (in-distribution) or have no semantic relevance to any class (out-of-distribution) in the dataset. Most state-of-the-art methods leverage ID labeled noisy samples as unlabeled data for semi-supervised learning, but OOD labeled noisy samples cannot be used in this way because they do not belong to any class within the dataset. Hence, in this paper, we propose incorporating the information from all the training data by leveraging the benefits of self-supervised training. Our method aims to extract a meaningful and generalizable embedding space for each sample regardless of its label. Then, we employ a simple yet effective K-nearest neighbor method to remove portions of out-of-distribution samples. By discarding these samples, we propose an iterative "Manifold DivideMix" algorithm to find clean and noisy samples, and train our model in a semi-supervised way. In addition, we propose "MixEMatch", a new algorithm for the semi-supervised step that involves mixup augmentation at the input and final hidden representations of the model. This will extract better representations by interpolating both in the input and manifold spaces. Extensive experiments on multiple synthetic-noise image benchmarks and real-world web-crawled datasets demonstrate the effectiveness of our proposed framework. Code is available at https://***/Fahim-F/ManifoldDivideMix.

关键词： Manifolds Representation learning Noise Semantics Training data Clustering algorithms Contrastive learning

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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...

引用

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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Unsupervised Part Segmentation through Disentangling Appearance and Shape

Unsupervised Part Segmentation through Disentangling Appeara...

引用

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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Linguistic Structures as Weak Supervision for Visual Scene Graph Generation

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

引用

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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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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DepthVoting: A Few-Shot Point Cloud Classification Model Incorporating a Projection-Based Voting Mechanism

DepthVoting: A Few-Shot Point Cloud Classification Model Inc...

引用

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

作者： Yunhui Zhu Jiajing Chen Senem Velipasalar Electrical Engineering and Computer Science Dept. Syracuse University Syracuse NY USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Despite the significant progress in few-shot 2D image classification, few-shot 3D point cloud classification remains relatively under-explored, particularly in addressing the challenges posed by missing points in 3D point clouds. Most existing methods for few-shot 3D point cloud classification are point-based, and thus, highly sensitive to missing points. Despite recent attempts, such as ViewNet, which introduce projection-based backbones to increase robustness against missing points, the reliance on max pooling, to extract information from multiple images simultaneously, makes them prone to information loss. To address these limitations, we introduce DepthVoting, a novel projection-based approach, for few-shot 3D point cloud classification. Instead of extracting features from multiple projection images simultaneously, DepthVoting captures features from pairs of projection images (obtained from opposite view angles) separately, enhancing the extraction of more comprehensive information. These features are sent to multiple few-shot heads, which share parameters. To further refine predictions, DepthVoting incorporates a voting mechanism, allowing contribution and incorporating information from different pairs. We conduct extensive experiments on three datasets, namely ModelNet40, ModelNet40-C, and ScanObjectNN, along with cross-validation. Our proposed method consistently outperforms the state-of-the-art baselines on all datasets in terms of average accuracy with even higher margins on the challenging ScanObjectNN dataset.

关键词： Point cloud compression Solid modeling Three-dimensional displays Head Accuracy Feature extraction Data augmentation

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