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检索条件"任意字段=1994 IEEE Computer-Society Conference on Computer Vision and Pattern Recognition"

共 22907 条记录，以下是4581-4590 订阅

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UC²: Universal Cross-lingual Cross-modal vision-and-Language Pre-training

UC<SUP>2</SUP>: Universal Cross-lingual Cross-modal Vision-a...

引用

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

作者： Zhou, Mingyang Zhou, Luowei Wang, Shuohang Cheng, Yu Li, Linjie Yu, Zhou Liu, Jingjing Univ Calif Davis Davis CA 95616 USA Microsoft Dynamics 365 AI Res Redmond WA USA

ISBN: (纸本)9781665445092

vision-and-language pre-training has achieved impressive success in learning multimodal representations between vision and language. To generalize this success to non-English languages, we introduce UC2, the first machine translation-augmented framework for cross-lingual cross-modal representation learning. To tackle the scarcity problem of multilingual captions for image datasets, we first augment existing English-only datasets with other languages via machine translation (MT). Then we extend the standard Masked Language Modeling and Image-Text Matching training objectives to multilingual setting, where alignment between different languages is captured through shared visual context (i.e., using image as pivot). To facilitate the learning of a joint embedding space of images and all languages of interest, we further propose two novel pre-training tasks, namely Masked Region-to-Token Modeling (MRTM) and Visual Translation Language Modeling (VTLM), leveraging MT-enhanced translated data. Evaluation on multilingual image-text retrieval and multilingual visual question answering benchmarks demonstrates that our proposed framework achieves new state of the art on diverse non-English benchmarks while maintaining comparable performance to monolingual pre-trained models on English tasks.

关键词： Training Visualization Benchmark testing Knowledge discovery Data models pattern recognition Machine translation

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Scene Text Retrieval via Joint Text Detection and Similarity Learning

Scene Text Retrieval via Joint Text Detection and Similarity...

引用

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

作者： Wang, Hao Bai, Xiang Yang, Mingkun Zhu, Shenggao Wang, Jing Liu, Wenyu Huazhong Univ Sci & Technol Wuhan Hubei Peoples R China Huawei Cloud & AI Shenzhen Peoples R China

ISBN: (纸本)9781665445092

Scene text retrieval aims to localize and search all text instances from an image gallery, which are the same or similar with a given query text. Such a task is usually realized by matching a query text to the recognized words, outputted by an end-to-end scene text spotter. In this paper, we address this problem by directly learning a cross-modal similarity between a query text and each text instance from natural images. Specifically, we establish an end-to-end trainable network, jointly optimizing the procedures of scene text detection and cross-modal similarity learning. In this way, scene text retrieval can be simply performed by ranking the detected text instances with the learned similarity. Experiments on three benchmark datasets demonstrate our method consistently outperforms the state-of-the-art scene text spotting/retrieval approaches. In particular, the proposed framework of joint detection and similarity learning achieves significantly better performance than separated methods. Code is available at: https://***/lanfeng4659/STR-TDSL.

关键词： computer vision Codes Text recognition Benchmark testing Task analysis

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Enhancing the Transferability of Adversarial Attacks with Stealth Preservation

Enhancing the Transferability of Adversarial Attacks with St...

引用

ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Xinwei Zhang Tianyuan Zhang Yitong Zhang Shuangcheng Liu School of Computer Science and Engineering Beihang University Beijing China State Key Lab of Software Development Environment Beihang University Beijing China Shen Yuan Honors College Beihang University Beijing China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Deep neural networks are susceptible to attacks from adversarial examples in recent years. Especially, the black-box attacks cause a more serious threat to practical applications. However, while most existing black-box attacks have achieved a high success rate in deceiving models, they have not focused on the stealthiness of adversarial examples, often exhibiting suspicious visual appearances. To address this issue, this paper proposes the Mask Momentum Iterative Attack (MMIA), which introduces a masking mechanism and adopts an optimal perturbation strategy to identify regions of an image most vulnerable to attacks. This approach effectively ensures the transferability and stealthiness of adversarial examples. Simultaneously, by integrating image enhancement techniques and temporal and spatial momentum terms into the iterative process of the attack, we prevent the attack from getting stuck in local optima, further improving the transferability of adversarial examples. To enhance the success rate of black-box attacks, we apply MMIA to a model ensemble using a joint optimization strategy. We demonstrate that adversarially trained models with a strong defense ability are also susceptible to our black-box attacks. We conduct extensive experiments on classification tasks using common vision models, and our results significantly demonstrate the superiority of our method over state-of-the-art approaches when considering both transferability and stealthiness.

关键词： Visualization Fuses Computational modeling Perturbation methods Closed box pattern recognition Iterative methods

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H3Net: Irregular Posture Detection by Understanding Human Character and Core Structures

H3Net: Irregular Posture Detection by Understanding Human Ch...

引用

ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Seungha Noh Kangmin Bae Yuseok Bae Byong-Dai Lee Kyonggi University Suwon South Korea ETRI Daejeon South Korea

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper proposes H 3 Net that considers detecting people in irregular postures by utilizing human structures and characters. To handle both features, we introduce two attention modules: 1) Human Structure Attention Module (HSAM), which is introduced to focus on the spatial aspects of a person, and 2) Human Character Attention Module (HCAM), which is designed to address the issue of repetitive appearance. HSAM effectively handles both foreground and background information about a human instance and utilizes keypoints to provide additional guidance to predict irregular postures. Meanwhile, HCAM employs ID information obtained from the tracking head, enriching the posture prediction with high-level semantic information. Furthermore, gathering images of people in irregular postures is a challenging task. Therefore, many conventional datasets consist of images with the same actors simulating varying postures in distinct images. To address this problem, we propose a Human ID Dependent Posture (HID 2 ) loss that handles repeated instances. The HID 2 loss generates a regularization term by considering duplicated instances to reduce bias. Our experiments demonstrate the effectiveness of H 3 Net compared to existing algorithms on irregular posture datasets. Furthermore, we show the qualitative results using color-coded masks and bounding boxes. We also provide ablation studies to highlight the significance of our proposed methods.

关键词： computer vision Head conferences Semantics pattern recognition

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AlphaMatch: Improving Consistency for Semi-supervised Learning with Alpha-divergence

AlphaMatch: Improving Consistency for Semi-supervised Learni...

引用

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

作者： Gong, Chengyue Wang, Dilin Liu, Qiang Univ Texas Austin Austin TX 78712 USA

ISBN: (纸本)9781665445092

Semi-supervised learning (SSL) is a key approach toward more data-efficient machine learning by jointly leverage both labeled and unlabeled data. We propose AlphaMatch, an efficient SSL method that leverages data augmentations, by efficiently enforcing the label consistency between the data points and the augmented data derived from them. Our key technical contribution lies on: 1) using alpha-divergence to prioritize the regularization on data with high confidence, achieving similar effect as FixMatch [32] but in a more flexible fashion, and 2) proposing an optimization-based, EM-like algorithm to enforce the consistency, which enjoys better convergence than iterative regularization procedures used in recent SSL methods such as FixMatch, UDA, and MixMatch. AlphaMatch is simple and easy to implement, and consistently outperforms prior arts on standard benchmarks, e.g. CIFAR-10, SVHN, CIFAR-100, STL-10. Specifically, we achieve 91.3% test accuracy on CIFAR-10 with just 4 labelled data per class, substantially improving over the previously best 88.7% accuracy achieved by FixMatch.

关键词： Object detection Machine learning Semisupervised learning Benchmark testing Iterative algorithms pattern recognition Machine translation

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Deep Burst Super-Resolution

Deep Burst Super-Resolution

引用

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

作者： Bhat, Goutam Danelljan, Martin Van Gool, Luc Timofte, Radu Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland

ISBN: (纸本)9781665445092

While single-image super-resolution (SISR) has attracted substantial interest in recent years, the proposed approaches are limited to learning image priors in order to add high frequency details. In contrast, multi-frame super-resolution (MFSR) offers the possibility of reconstructing rich details by combining signal information from multiple shifted images. This key advantage, along with the increasing popularity of burst photography, have made MFSR an important problem for real-world applications. We propose a novel architecture for the burst super-resolution task. Our network takes multiple noisy RAW images as input, and generates a denoised, super-resolved RGB image as output. This is achieved by explicitly aligning deep embeddings of the input frames using pixel-wise optical flow. The information from all frames are then adaptively merged using an attention-based fusion module. In order to enable training and evaluation on real-world data, we additionally introduce the BurstSR dataset, consisting of smartphone bursts and high-resolution DSLR ground-truth. We perform comprehensive experimental analysis, demonstrating the effectiveness of the proposed architecture.

关键词： Training Photography Superresolution computer architecture pattern recognition Noise measurement Task analysis

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Fast and Accurate Single-Image Depth Estimation on Mobile Devices, Mobile AI 2021 Challenge: Report

Fast and Accurate Single-Image Depth Estimation on Mobile De...

引用

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

作者： Ignatov, Andrey Malivenko, Grigory Plowman, David Shukla, Samarth Timofte, Radu Zhang, Ziyu Wang, Yicheng Huang, Zilong Luo, Guozhong Yu, Gang Fu, Bin Wang, Yiran Li, Xingyi Shi, Min Xian, Ke Cao, Zhiguo Du, Jin-Hua Wu, Pei-Lin Ge, Chao Yao, Jiaoyang Tu, Fangwen Li, Bo Yoo, Jung Eun Seo, Kwanggyoon Xu, Jialei Li, Zhenyu Liu, Xianming Jiang, Junjun Chen, Wei-Chi Joya, Shayan Fan, Huanhuan Kang, Zhaobing Li, Ang Feng, Tianpeng Liu, Yang Sheng, Chuannan Yin, Jian Benavides, Fausto T. Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Raspberry Pi Trading Ltd Cambridge England AI Witchlabs Zurich Switzerland Tencent GY Lab Shenzhen Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Sch Artificial Intelligence & Automat Wuhan Peoples R China Chinese Acad Sci Inst Automat Nanjing Artificial Intelligence Chip Res Beijing Peoples R China Black Sesame Technol Inc Singapore Singapore Korea Adv Inst Sci & Technol Visual Media Lab Daejeon South Korea Harbin Inst Technol Harbin Peoples R China Peng Cheng Lab Shenzhen Peoples R China Natl Cheng Kung Univ Multimedia & Comp Vis Lab Tainan Taiwan Samsung Res UK Cambridge England OPPO Res Inst Beijing Peoples R China Swiss Fed Inst Technol Zurich Switzerland

ISBN: (纸本)9781665448994

Depth estimation is an important computer vision problem with many practical applications to mobile devices. While many solutions have been proposed for this task, they are usually very computationally expensive and thus are not applicable for on-device inference. To address this problem, we introduce the first Mobile AI challenge, where the target is to develop an end-to-end deep learning-based depth estimation solutions that can demonstrate a nearly real-time performance on smartphones and IoT platforms. For this, the participants were provided with a new large-scale dataset containing RGB-depth image pairs obtained with a dedicated stereo ZED camera producing high-resolution depth maps for objects located at up to 50 meters. The runtime of all models was evaluated on the popular Raspberry Pi 4 platform with a mobile ARM-based Broadcom chipset. The proposed solutions can generate VGA resolution depth maps at up to 10 FPS on the Raspberry Pi 4 while achieving high fidelity results, and are compatible with any Android or Linux-based mobile devices. A detailed description of all models developed in the challenge is provided in this paper.

关键词： Meters computer vision Image resolution conferences Estimation Real-time systems pattern recognition

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NeuralFusion: Online Depth Fusion in Latent Space

NeuralFusion: Online Depth Fusion in Latent Space

引用

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

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

ISBN: (纸本)9781665445092

We present a novel online depth map fusion approach that learns depth map aggregation in a latent feature space. While previous fusion methods use an explicit scene representation like signed distance functions (SDFs), we propose a learned feature representation for the fusion. The key idea is a separation between the scene representation used for the fusion and the output scene representation, via an additional translator network. Our neural network architecture consists of two main parts: a depth and feature fusion sub-network, which is followed by a translator sub-network to produce the final surface representation (e.g. TSDF) for visualization or other tasks. Our approach is an online process, handles high noise levels, and is particularly able to deal with gross outliers common for photometric stereo-based depth maps. Experiments on real and synthetic data demonstrate improved results compared to the state of the art, especially in challenging scenarios with large amounts of noise and outliers. The source code will be made available at https://***/weders/NeuralFusion.

关键词： Shape Superresolution Neural networks Real-time systems Encoding pattern recognition Task analysis

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MIST: Multiple Instance Spatial Transformer

MIST: Multiple Instance Spatial Transformer

引用

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

作者： Angles, Baptiste Jin, Yuhe Kornblith, Simon Tagliasacchi, Andrea Yi, Kwang Moo Univ Victoria Victoria BC Canada Univ British Columbia Vancouver BC Canada Google Res Mountain View CA USA

ISBN: (纸本)9781665445092

We propose a deep network that can be trained to tackle image reconstruction and classification problems that involve detection of multiple object instances, without any supervision regarding their whereabouts. The network learns to extract the most significant K patches, and feeds these patches to a task-specific network - e.g., auto-encoder or classifier - to solve a domain specific problem. The challenge in training such a network is the non-differentiable top-K selection process. To address this issue, we lift the training optimization problem by treating the result of top-K selection as a slack variable, resulting in a simple, yet effective, multi-stage training. Our method is able to learn to detect recurring structures in the training dataset by learning to reconstruct images. It can also learn to localize structures when only knowledge on the occurrence of the object is provided, and in doing so it outperforms the state-of-the-art.

关键词： Training Location awareness Transformers Image decomposition pattern recognition Proposals Task analysis

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Deep Graph Matching under Quadratic Constraint

Deep Graph Matching under Quadratic Constraint

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

作者： Gao, Quankai Wang, Fudong Xue, Nan Yu, Jin-Gang Xia, Gui-Song Wuhan Univ Wuhan Peoples R China South China Univ Technol Guangzhou Peoples R China

ISBN: (纸本)9781665445092

Recently, deep learning based methods have demonstrated promising results on the graph matching problem, by relying on the descriptive capability of deep features extracted on graph nodes. However, one main limitation with existing deep graph matching (DGM) methods lies in their ignorance of explicit constraint of graph structures, which may lead the model to be trapped into local minimum in training. In this paper, we propose to explicitly formulate pairwise graph structures as a quadratic constraint incorporated into the DGM framework. The quadratic constraint minimizes the pairwise structural discrepancy between graphs, which can reduce the ambiguities brought by only using the extracted CNN features. Moreover, we present a differentiable implementation to the quadratic constrained-optimization such that it is compatible with the unconstrained deep learning optimizer. To give more precise and proper supervision, a well-designed false matching loss against class imbalance is proposed, which can better penalize the false negatives and false positives with less overfitting. Exhaustive experiments demonstrate that our method achieves competitive performance on real-world datasets. The code is available at: https://***/zerg-Overmind/QC-DGM.

关键词： Deep learning Training computer vision Codes Feature extraction pattern matching

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