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检索条件"任意字段=2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021"

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SIDOD: A Synthetic Image Dataset for 3D Object Pose recognition with Distractors 32

SIDOD: A Synthetic Image Dataset for 3D Object Pose Recognit...

引用

32nd ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Jalal, Mona Spjut, Josef Boudaoud, Ben Betke, Margrit Boston Univ Boston MA 02215 USA NVIDIA Raleigh NC USA

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125060

We present a new, publicly-available image dataset generated by the NVIDIA Deep Learning Data Synthesizer intended for use in object detection, pose estimation, and tracking applications. This dataset contains 144k stereo image pairs that synthetically combine 18 camera viewpoints of three photorealistic virtual environments with up to 10 objects (chosen randomly from the 21 object models of the YCB dataset [1]) and flying distractors. Object and camera pose, scene lighting, and quantity of objects and distractors were randomized. Each provided view includes RGB, depth, segmentation, and surface normal images, all pixel level. We describe our approach for domain randomization and provide insight into the decisions that produced the dataset.

关键词： Three-dimensional displays Pose estimation Cameras Image segmentation computer vision Training Lighting

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Purposeful Regularization with Reinforcement Learning for Facial Expression recognition In-the-Wild

Purposeful Regularization with Reinforcement Learning for Fa...

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

作者： Hong, SangHwa Seoul Natl Univ Sci & Technol Dept Ind Engn Gongreung Ro 232 Seoul South Korea

ISBN: (纸本)9798350365474

Facial Expression recognition (FER), an essential aspect of emotion analysis through artificial intelligence, is a crucial research area. Although traditional approaches utilizing Convolutional Neural Networks (CNNs) for analyzing human emotions from facial expressions achieve superior accuracy over conventional machine learning methods, overfitting -especially arising severely from data collected in uncontrolled, In-the-wild settings -significantly impedes CNNs performance. This is due to the data scarcity and inherent noise inside In-the-wild data. To address this challenge, this paper introduces a novel regularization method that employs Reinforcement Learning (RL) to adaptively apply regularization hyperparameters appropriate for the evolving state of trained CNNs. Through experiments on various datasets such as CIFAR100, FER2013, and Affect-Net datasets including diverse perspective analysis such as graphical, Grad-CAM and numerical analysis, it is demonstrated that the suggested method can alleviate memorization of noise in training data and promote learning of essential features. The significance of the suggested method lies in its demonstrated remarkable effectiveness in enhancing CNNs' generalization and reducing overfitting.

关键词： FER Overfitting Reinforcement Learning

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Latent-based Diffusion Model for Long-tailed recognition

Latent-based Diffusion Model for Long-tailed Recognition

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

作者： Han, Pengxiao Ye, Changkun Zhou, Jieming Zhang, Jing Hong, Jie Li, Xuesong Australian Natl Univ Canberra ACT Australia CSIRO Canberra ACT Australia

ISBN: (纸本)9798350365474

Long-tailed imbalance distribution is a common issue in practical computer vision applications. Previous works proposed methods to address this problem, which can be categorized into several classes: re-sampling, re-weighting, transfer learning, and feature augmentation. In recent years, diffusion models have shown an impressive generation ability in many sub-problems of deep computer vision. However, its powerful generation has not been explored in long-tailed problems. We propose a new approach, the Latent-based Diffusion Model for Long-tailed recognition (LDMLR), as a feature augmentation method to tackle the issue. First, we encode the imbalanced dataset into features using the baseline model. Then, we train a Denoising Diffusion Implicit Model (DDIM) using these encoded features to generate pseudo-features. Finally, we train the classifier using the encoded and pseudo-features from the previous two steps. The model's accuracy shows an improvement on the CIFAR-LT and ImageNet-LT datasets by using the proposed method.

关键词： diffusion model imbalance distribution long-tailed recognition

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Improving Pairwise Ranking for Multi-label Image Classification 30

Improving Pairwise Ranking for Multi-label Image Classificat...

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

作者： Li, Yuncheng Song, Yale Luo, Jiebo Univ Rochester Rochester NY 14627 USA Yahoo Res New York NY USA

ISBN: (纸本)9781538604571

Learning to rank has recently emerged as an attractive technique to train deep convolutional neural networks for various computer vision tasks. Pairwise ranking, in particular, has been successful in multi-label image classification, achieving state-of-the-art results on various benchmarks. However, most existing approaches use the hinge loss to train their models, which is non-smooth and thus is difficult to optimize especially with deep networks. Furthermore, they employ simple heuristics, such as top-k or thresholding, to determine which labels to include in the output from a ranked list of labels, which limits their use in the real-world setting. In this work, we propose two techniques to improve pairwise ranking based multi-label image classification: (1) we propose a novel loss function for pairwise ranking, which is smooth everywhere and thus is easier to optimize;and (2) we incorporate a label decision module into the model, estimating the optimal confidence thresholds for each visual concept. We provide theoretical analyses of our loss function in the Bayes consistency and risk minimization framework, and show its benefit over existing pairwise ranking formulations. We demonstrate the effectiveness of our approach on three large-scale datasets, VOC2007, NUS-WIDE and MS-COCO, achieving the best reported results in the literature.

关键词： Fasteners Visualization computer vision Neural networks Adaptation models

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Optimizing Over Radial Kernels on Compact Manifolds 27

Optimizing Over Radial Kernels on Compact Manifolds

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Jayasumana, Sadeep Hartley, Richard Salzmann, Mathieu Li, Hongdong Harandi, Mehrtash Australian Natl Univ Canberra ACT Australia NICTA Canberra ACT Australia

ISBN: (纸本)9781479951178

We tackle the problem of optimizing over all possible positive definite radial kernels on Riemannian manifolds for classification. Kernel methods on Riemannian manifolds have recently become increasingly popular in computer vision. However, the number of known positive definite kernels on manifolds remain very limited. Furthermore, most kernels typically depend on at least one parameter that needs to be tuned for the problem at hand. A poor choice of kernel, or of parameter value, may yield significant performance drop-off. Here, we show that positive definite radial kernels on the unit n-sphere, the Grassmann manifold and Kendall's shape manifold can be expressed in a simple form whose parameters can be automatically optimized within a support vector machine framework. We demonstrate the benefits of our kernel learning algorithm on object, face, action and shape recognition.

关键词： Grassmann MKL Riemannian manifolds kernel methods kernels on manifolds shape analysis Manifolds Seeds shape analysis Kernel Positive definite one-parameter kernel method Radial shape recognition

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Discovering and Mitigating Visual Biases through Keyword Explanation

Discovering and Mitigating Visual Biases through Keyword Exp...

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

作者： Kim, Younghyun Mo, Sangwoo Kim, Minkyu Lee, Kyungmin Lee, Jaeho Shin, Jinwoo Korea Adv Inst Sci & Technol Daejeon South Korea Univ Michigan Ann Arbor MI 48109 USA KRAFTON Seongnam South Korea POSTECH Pohang South Korea

ISBN: (纸本)9798350353006

Addressing biases in computer vision models is crucial for real-world AI deployments. However, mitigating visual biases is challenging due to their unexplainable nature, often identified indirectly through visualization or sample statistics, which necessitates additional human supervision for interpretation. To tackle this issue, we propose the Bias-to-Text (B2T) framework, which interprets visual biases as keywords. Specifically, we extract common keywords from the captions of mispredicted images to identify potential biases in the model. We then validate these keywords by measuring their similarity to the mispredicted images using a vision-language scoring model. The keyword explanation form of visual bias offers several advantages, such as a clear group naming for bias discovery and a natural extension for debiasing using these group names. Our experiments demonstrate that B2T can identify known biases, such as gender bias in CelebA, background bias in Waterbirds, and distribution shifts in ImageNet-R/C. Additionally, B2T uncovers novel biases in larger datasets, such as Dollar Street and ImageNet. For example, we discovered a contextual bias between "bee" and "flower" in ImageNet. We also highlight various applications of B2T keywords, including debiased training, CLIP prompting, and model comparison.(1)

关键词： bias and fairness explainable AI vision-language model

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DeepACO: A Robust Deep Learning-based Automatic Checkout System

DeepACO: A Robust Deep Learning-based Automatic Checkout Sys...

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

作者： Long Hoang Pham Duong Nguyen-Ngoc Tran Huy-Hung Nguyen Tai Huu-Phuong Tran Hyung-Joon Jeon Hyung-Min Jeon Jae Wook Jeon Sungkyunkwan Univ Dept Elect & Comp Engn Seoul South Korea

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The retail industry has seen an increasing growth of artificial intelligence and computer vision applications. Of the various topics, automatic checkout (ACO) in retail stores or supermarkets has emerged as one of the critical tasks in this area. Several problems stem from real-world scenarios such as object occlusion, blurring from scanning motion, and similarity in scanned items. Moreover, the challenge also comes from the difficulty of collecting training images that reflect the realistic checkout scenarios due to continuous updates of the products. This paper proposes a deep learning-based automatic checkout system (DeepACO) to recognize, localize, track, and count products as they move along a retail check-out conveyor belt. The DeepACO follows the detect-and-track approach, i.e., applying trackers on detected bounding boxes. It also provides a completed pipeline for generating large training datasets under various environments from synthetic data. The proposed system has been evaluated on the 2022 AI City Challenge Track 4 benchmark. Compared to other state-of-the-art solutions, it has shown outstanding results, achieving top-2 on the test-set A with the F1 score of 0.4783.

关键词： Training computer vision Image resolution Tracking Pipelines Urban areas Benchmark testing

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Best of Both Worlds: Multimodal Contrastive Learning with Tabular and Imaging Data

Best of Both Worlds: Multimodal Contrastive Learning with Ta...

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

作者： Hager, Paul Menten, Martin J. Rueckert, Daniel Tech Univ Munich Munich Germany Klinikum Rechts Der Isar Munich Germany Imperial Coll London London England

ISBN: (纸本)9798350301298

Medical datasets and especially biobanks, often contain extensive tabular data with rich clinical information in addition to images. In practice, clinicians typically have less data, both in terms of diversity and scale, but still wish to deploy deep learning solutions. Combined with increasing medical dataset sizes and expensive annotation costs, the necessity for unsupervised methods that can pretrain multimodally and predict unimodally has risen. To address these needs, we propose the first self-supervised contrastive learning framework that takes advantage of images and tabular data to train unimodal encoders. Our solution combines SimCLR and SCARF, two leading contrastive learning strategies, and is simple and effective. In our experiments, we demonstrate the strength of our framework by predicting risks of myocardial infarction and coronary artery disease (CAD) using cardiac MR images and 120 clinical features from 40,000 UK Biobank subjects. Furthermore, we show the generalizability of our approach to natural images using the DVM car advertisement dataset. We take advantage of the high interpretability of tabular data and through attribution and ablation experiments find that morphometric tabular features, describing size and shape, have outsized importance during the contrastive learning process and improve the quality of the learned embeddings. Finally, we introduce a novel form of supervised contrastive learning, label as a feature (LaaF), by appending the ground truth label as a tabular feature during multimodal pretraining, outperforming all supervised contrastive baselines.

关键词： cell microscopy Medical and biological vision

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Video Dehazing via a Multi-Range Temporal Alignment Network with Physical Prior

Video Dehazing via a Multi-Range Temporal Alignment Network ...

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

作者： Xu, Jiaqi Hu, Xiaowei Zhu, Lei Dou, Qi Dai, Jifeng Qiao, Yu Heng, Pheng-Ann Chinese Univ Hong Kong Hong Kong Peoples R China Shanghai Artificial Intelligence Lab Shanghai Peoples R China Hong Kong Univ Sci & Technol Guangzhou Guangzhou Peoples R China Hong Kong Univ Sci & Technol Hong Kong Peoples R China Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9798350301298

Video dehazing aims to recover haze-free frames with high visibility and contrast. This paper presents a novel framework to effectively explore the physical haze priors and aggregate temporal information. Specifically, we design a memory-based physical prior guidance module to encode the prior-related features into long-range memory. Besides, we formulate a multi-range scene radiance recovery module to capture space-time dependencies in multiple space-time ranges, which helps to effectively aggregate temporal information from adjacent frames. Moreover, we construct the first large-scale outdoor video dehazing benchmark dataset, which contains videos in various real-world scenarios. Experimental results on both synthetic and real conditions show the superiority of our proposed method.

关键词： Low-level vision

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Revisiting the "Video" in Video-Language Understanding

Revisiting the "Video" in Video-Language Understanding

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

作者： Buch, Shyamal Eyzaguirre, Cristobal Gaidon, Adrien Wu, Jiajun Li Fei-Fei Niebles, Juan Carlos Stanford Univ Stanford CA 94305 USA Toyota Res Inst Stanford CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

What makes a video task uniquely suited for videos, beyond what can be understood from a single image? Building on recent progress in self-supervised image-language models, we revisit this question in the context of video and language tasks. We propose the atemporal probe (ATP), a new model for video-language analysis which provides a stronger bound on the baseline accuracy of multimodal models constrained by image-level understanding. By applying this model to standard discriminative video and language tasks, such as video question answering and text-to-video retrieval, we characterize the limitations and potential of current video-language benchmarks. We find that understanding of event temporality is often not necessary to achieve strong or state-of-the-art performance, even compared with recent large-scale video-language models and in contexts intended to benchmark deeper video-level understanding. We also demonstrate how ATP can improve both video-language dataset and model design. We describe a technique for leveraging ATP to better disentangle dataset subsets with a higher concentration of temporally challenging data, improving benchmarking efficacy for causal and temporal understanding. Further, we show that effectively integrating ATP into full video-level temporal models can improve efficiency and state-of-the-art accuracy.(1).

关键词： Analytical models Image recognition Buildings Benchmark testing Question answering (information retrieval) pattern recognition Task analysis

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