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检索条件"任意字段=2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024"

共 11890 条记录，以下是441-450 订阅

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Exploring Self-attention for Image recognition

Exploring Self-attention for Image Recognition

引用

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

作者： Zhao, Hengshuang Jia, Jiaya Koltun, Vladlen CUHK Hong Kong Peoples R China Intel Labs Santa Clara CA USA

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171685

Recent work has shown that self-attention can serve as a basic building block for image recognition models. We explore variations of self-attention and assess their effectiveness for image recognition. We consider two forms of self-attention. One is pairwise self-attention, which generalizes standard dot-product attention and is fundamentally a set operator. The other is patchwise self-attention, which is strictly more powerful than convolution. Our pairwise self-attention networks match or outperform their convolutional counterparts, and the patchwise models substantially outperform the convolutional baselines. We also conduct experiments that probe the robustness of learned representations and conclude that self-attention networks may have significant benefits in terms of robustness and generalization.

关键词： Convolution Image recognition Computational modeling computer vision Robustness Tensile stress Standards

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SharinGAN: Combining Synthetic and Real Data for Unsupervised Geometry Estimation

SharinGAN: Combining Synthetic and Real Data for Unsupervise...

引用

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

作者： Koutilya, P. N. V. R. Zhou, Hao Jacobs, David Univ Maryland College Pk MD 20742 USA Amazon AWS Seattle WA USA

ISBN: (纸本)9781728171685

We propose a novel method for combining synthetic and real images when training networks to determine geometric information from a single image. We suggest a method for mapping both image types into a single, shared domain. This is connected to a primary network for end-to-end training. Ideally, this results in images from two domains that present shared information to the primary network. Our experiments demonstrate significant improvements over the state-of-the-art in two important domains, surface normal estimation of human faces and monocular depth estimation for outdoor scenes, both in an unsupervised setting.

关键词： computer vision

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CAPE: CAM as a Probabilistic Ensemble for Enhanced DNN Interpretation

CAPE: CAM as a Probabilistic Ensemble for Enhanced DNN Inter...

引用

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

作者： Chowdhury, Townim Faisal Liao, Kewen Vu Minh Hieu Phan To, Minh-Son Xie, Yutong Hung, Kevin Rose, David van den Hengel, Anton Verjans, Johan W. Liao, Zhibin Univ Adelaide Australian Inst Machine Learning Adelaide SA Australia Australian Catholic Univ Fitzroy Vic Australia Flinders Univ S Australia Adelaide SA Australia Cent Adelaide Local Hlth Network SA Pathol Adelaide SA Australia

ISBN: (纸本)9798350353006

Deep Neural Networks (DNNs) are widely used for visual classification tasks, but their complex computation process and black-box nature hinder decision transparency and interpretability. Class activation maps (CAMs) and recent variants provide ways to visually explain the DNN decision-making process by displaying 'attention' heatmaps of the DNNs. Nevertheless, the CAM explanation only offers relative attention information, that is, on an attention heatmap, we can interpret which image region is more or less important than the others. However, these regions cannot be meaningfully compared across classes, and the contribution of each region to the model's class prediction is not revealed. To address these challenges that ultimately lead to better DNN Interpretation, in this paper, we propose CAPE, a novel reformulation of CAM that provides a unified and probabilistically meaningful assessment of the contributions of image regions. We quantitatively and qualitatively compare CAPE with state-of-the-art CAM methods on CUB and ImageNet benchmark datasets to demonstrate enhanced interpretability. We also test on a cytology imaging dataset depicting a challenging Chronic Myelomono-cytic Leukemia (CMML) diagnosis problem. Code is available at: https://***/AIML-MED/CAPE.

关键词： class activation maps deep neural network interpretability explainable AI image recognition visual classification

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PatchVAE: Learning Local Latent Codes for recognition

PatchVAE: Learning Local Latent Codes for Recognition

引用

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

作者： Gupta, Kamal Singh, Saurabh Shrivastava, Abhinav Univ Maryland College Pk MD 20742 USA Google Res Mountain View CA USA

ISBN: (纸本)9781728171685

Unsupervised representation learning holds the promise of exploiting large amounts of unlabeled data to learn general representations. A promising technique for unsupervised learning is the framework of Variational Auto-encoders (VAEs). However, unsupervised representations learned by VAEs are significantly outperformed by those learned by supervised learning for recognition. Our hypothesis is that to learn useful representations for recognition the model needs to be encouraged to learn about repeating and consistent patterns in data. Drawing inspiration from the mid-level representation discovery work, we propose PatchVAE, that reasons about images at patch level. Our key contribution is a bottleneck formulation that encourages mid-level style representations in the VAE framework. Our experiments demonstrate that representations learned by our method perform much better on the recognition tasks compared to those learned by vanilla VAEs.

关键词： computer vision

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Tangent Space Backpropagation for 3D Transformation Groups

Tangent Space Backpropagation for 3D Transformation Groups

引用

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

作者： Teed, Zachary Deng, Jia Princeton Univ Princeton NJ 08544 USA

ISBN: (纸本)9781665445092

We address the problem of performing backpropagation for computation graphs involving 3D transformation groups SO(3), SE(3), and Sim(3). 3D transformation groups are widely used in 3D vision and robotics, but they do not form vector spaces and instead lie on smooth manifolds. The standard backpropagation approach, which embeds 3D transformations in Euclidean spaces, suffers from numerical difficulties. We introduce a new library, which exploits the group structure of 3D transformations and performs backpropagation in the tangent spaces of manifolds. We show that our approach is numerically more stable, easier to implement, and beneficial to a diverse set of tasks.

关键词： Backpropagation Manifolds computer vision Three-dimensional displays Libraries pattern recognition Task analysis

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Learning Deep Classifiers Consistent with Fine-Grained Novelty Detection

Learning Deep Classifiers Consistent with Fine-Grained Novel...

引用

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

作者： Cheng, Jiacheng Vasconcelos, Nuno Univ Calif San Diego Dept Elect & Comp Engn San Diego CA 92103 USA

ISBN: (纸本)9781665445092

The problem of novelty detection in fine-grained visual classification (FGVC) is considered. An integrated understanding of the probabilistic and distance-based approaches to novelty detection is developed within the framework of convolutional neural networks (CNNs). It is shown that softmax CNN classifiers are inconsistent with novelty detection, because their learned class-conditional distributions and associated distance metrics are unidentifiable. A new regularization constraint, the class-conditional Gaussianity loss, is then proposed to eliminate this unidentifiability, and enforce Gaussian class-conditional distributions. This enables training Novelty Detection Consistent Classifiers (NDCCs) that are jointly optimal for classification and novelty detection. Empirical evaluations show that NDCCs achieve significant improvements over the state-of-the-art on both small- and large-scale FGVC datasets.

关键词： Measurement Training Visualization computer vision Probabilistic logic pattern recognition Convolutional neural networks

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Exploring and Utilizing pattern Imbalance

Exploring and Utilizing Pattern Imbalance

引用

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

作者： Mei, Shibin Zhao, Chenglong Yuan, Shengchao Ni, Bingbing Shanghai Jiao Tong Univ Shanghai 200240 Peoples R China

ISBN: (纸本)9798350301298

In this paper, we identify pattern imbalance from several aspects, and further develop a new training scheme to avert pattern preference as well as spurious correlation. In contrast to prior methods which are mostly concerned with category or domain granularity, ignoring the potential finer structure that existed in datasets, we give a new definition of seed category as an appropriate optimization unit to distinguish different patterns in the same category or domain. Extensive experiments on domain generalization datasets of diverse scales demonstrate the effectiveness of the proposed method.

关键词： Datasets and evaluation

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Language-driven Grasp Detection

Language-driven Grasp Detection

引用

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

作者： An Dinh Vuong Minh Nhat Vu Baoru Huang Nghia Nguyen Hieu Le Thieu Vo Anh Nguyen FPT Software AI Ctr Hanoi Vietnam TU Wien Automat Control Inst Vienna Austria Imperial Coll London London England Ton Duc Thang Univ Ho Chi Minh City Vietnam Univ Liverpool Liverpool Merseyside England

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353006

Grasp detection is a persistent and intricate challenge with various industrial applications. Recently, many methods and datasets have been proposed to tackle the grasp detection problem. However, most of them do not consider using natural language as a condition to detect the grasp poses. In this paper, we introduce Grasp-Anything++, a new language- driven grasp detection dataset featuring 1M samples, over 3M objects, and upwards of 10M grasping instructions. We utilize foundation models to create a large-scale scene corpus with corresponding images and grasp prompts. We approach the language-driven grasp detection task as a conditional generation problem. Drawing on the success of diffusion models in generative tasks and given that language plays a vital role in this task, we propose a new language-driven grasp detection method based on diffusion models. Our key contribution is the contrastive training objective, which explicitly contributes to the denoising process to detect the grasp pose given the language instructions. We illustrate that our approach is theoretically supportive. The intensive experiments show that our method outperforms state-of-the-art approaches and allows real-world robotic grasping. Finally, we demonstrate our large-scale dataset enables zero-short grasp detection and is a challenging benchmark for future work.

关键词： Training computer vision Noise reduction Natural languages Grasping Benchmark testing Diffusion models

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Towards Language-Driven Video Inpainting via Multimodal Large Language Models

Towards Language-Driven Video Inpainting via Multimodal Larg...

引用

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

作者： Wu, Jianzong Li, Xiangtai Si, Chenyang Zhou, Shangchen Yang, Jingkang Zhang, Jiangning Li, Yining Chen, Kai Tong, Yunhai Liu, Ziwei Loy, Chen Change Peking Univ Natl Key Lab Gen Artificial Intelligence Beijing Peoples R China Nanyang Technol Univ S Lab Singapore Singapore Shanghai AI Lab Shanghai Peoples R China PKU Wuhan Inst Artificial Intelligence Wuhan Peoples R China Zhejiang Univ Hangzhou Peoples R China

ISBN: (纸本)9798350353006

We introduce a new task - language-driven video inpainting, which uses natural language instructions to guide the inpainting process. This approach overcomes the limitations of traditional video inpainting methods that de-pend on manually labeled binary masks, a process often tedious and labor-intensive. We present the Remove Objects from Videos by Instructions (ROVI) dataset, containing 5,650 videos and 9,091 inpainting results, to support training and evaluation for this task. We also propose a novel diffusion-based language-driven video inpainting framework, the first end-to-end baseline for this task, integrating Multimodal Large Language Models to understand and execute complex language-based inpainting requests effectively. Our comprehensive results showcase the dataset's versatility and the model's effectiveness in various language-instructed inpainting scenarios. We have made datasets, code, and models publicly available at https://***/jianzongwu/Language-Driven-Video-Inpainting.

关键词： computer vision Multimodal Learning Text-to-Video Generation Video Inpainting

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Global Transport for Fluid Reconstruction with Learned Self-Supervision

Global Transport for Fluid Reconstruction with Learned Self-...

引用

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

作者： Franz, Erik Solenthaler, Barbara Thuerey, Nils Tech Univ Munich Munich Germany Swiss Fed Inst Technol Zurich Switzerland

ISBN: (纸本)9781665445092

We propose a novel method to reconstruct volumetric flows from sparse views via a global transport formulation. Instead of obtaining the space-time function of the observations, we reconstruct its motion based on a single initial state. In addition we introduce a learned self-supervision that constrains observations from unseen angles. These visual constraints are coupled via the transport constraints and a differentiable rendering step to arrive at a robust end-to-end reconstruction algorithm. This makes the reconstruction of highly realistic flow motions possible, even from only a single input view We show with a variety of synthetic and real flows that the proposed global reconstruction of the transport process yields an improved reconstruction of the fluid motion.

关键词： Visualization computer vision Fluids Reconstruction algorithms Rendering (computer graphics) pattern recognition

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