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

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Tags2Parts: Discovering Semantic Regions from Shape Tags 31

Tags2Parts: Discovering Semantic Regions from Shape Tags

引用

31st ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Muralikrishnan, Sanjeev Kim, Vladimir G. Chaudhuri, Siddhartha Indian Inst Technol Bombay Maharashtra India Adobe Res Bangalore Karnataka India

ISBN: (纸本)9781538664209

We propose a novel ntethod for discovering shape gions that strongly correlate with, user-prescribed, tags. For example, given a collection of chairs tagged as either "has armrest" or "lacks armrest", our system car redly highlights the armrest regions as the main distinctive parts between the two chair types. To obtain point wisepredictions from shape-wise tags we develop a novel neural network architecture that is trained with tag classification loss, but is designed to rely on segmentation to predict the tag, Our network is inspired by U-Net, but we replicate shallow U structures several times with new skip connections and pooling layers, and call the resulting architecture WU-Net, We test our method on segmentation benchmarks and show that even, with weak supervision of whole shape lags, our method can infer meaningful semantic 'regions, without ever observing shape segmentations. Farther, once trained, the model can process shapes for which the tag is entirely unknown. A. a bonus, our architecture is directly operational under full supervision and performs strongly on standard benchmarks. We validate our method through. experiments with many variant architectures and prior baselines, and demonstrate several applications,

关键词： Shape computer architecture Image segmentation Convolution Three-dimensional displays Semantics Training

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Neural 3D Mesh Renderer 31

Neural 3D Mesh Renderer

引用

31st ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Kato, Hiroharu Ushiku, Yoshitaka Harada, Tatsuya Univ Tokyo Tokyo Japan RIKEN Wako Saitama Japan

ISBN: (纸本)9781538664209

For modeling the 3D world behind 2D images, which 3D representation is most appropriate? A polygon mesh is a promising candidate for its compactness and geometric properties. However, it is not straightforward to model a polygon mesh from 2D images using neural networks because the conversion from a mesh to an image, or rendering, involves a discrete operation called rasterization, which prevents back-propagation. Therefore, in this work, we propose an approximate gradient for rasterization that enables the integration of rendering into neural networks. Using this renderer, we perform single-image 3D mesh reconstruction with silhouette image supervision and our system outperforms the existing voxel-based approach. Additionally, we perform gradient-based 3D mesh editing operations, such as 2D-to-3D style transfer and 3D DeepDream, with 2D supervision for the first time. These applications demonstrate the potential of the integration of a mesh renderer into neural networks and the effectiveness of our proposed renderer.

关键词： Three-dimensional displays Face Rendering (computer graphics) Two dimensional displays Neural networks Solid modeling Image color analysis

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TrackFormer: Multi-Object Tracking with Transformers

TrackFormer: Multi-Object Tracking with Transformers

引用

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

作者： Meinhardt, Tim Kirillov, Alexander Leal-Taixe, Laura Feichtenhofer, Christoph Tech Univ Munich Munich Germany Facebook AI Res FAIR Menlo Pk CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

The challenging task of multi-object tracking (MOT) requires simultaneous reasoning about track initialization, identity, and spatio-temporal trajectories. We formulate this task as a frame-to-frame set prediction problem and introduce TrackFormer, an end-to-end trainable MOT approach based on an encoder-decoder Transformer architecture. Our model achieves data association between frames via attention by evolving a set of track predictions through a video sequence. The Transformer decoder initializes new tracks from static object queries and autoregressively follows existing tracks in space and time with the conceptually new and identity preserving track queries. Both query types benefit from self- and encoder-decoder attention on global frame-level features, thereby omitting any additional graph optimization or modeling of motion and/or appearance. TrackFormer introduces a new tracking-by-attention paradigm and while simple in its design is able to achieve state-of-the-art performance on the task of multi-object tracking (MOT17) and segmentation (MOTS20). The code is available at https:///***/timmeinhardt/trackformer

关键词： Tracking Motion segmentation Video sequences computer architecture Predictive models Transformers Trajectory

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Deep Hashing Network for Unsupervised Domain Adaptation 30

Deep Hashing Network for Unsupervised Domain Adaptation

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30th ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Venkateswara, Hemanth Eusebio, Jose Chakraborty, Shayok Panchanathan, Sethuraman Arizona State Univ Ctr Cognit Ubiquitous Comp Tempe AZ 85287 USA

ISBN: (纸本)9781538604571

In recent years, deep neural networks have emerged as a dominant machine learning tool for a wide variety of application domains. However, training a deep neural network requires a large amount of labeled data, which is an expensive process in terms of time, labor and human expertise. Domain adaptation or transfer learning algorithms address this challenge by leveraging labeled data in a different, but related source domain, to develop a model for the target domain. Further, the explosive growth of digital data has posed a fundamental challenge concerning its storage and retrieval. Due to its storage and retrieval efficiency, recent years have witnessed a wide application of hashing in a variety of computer vision applications. In this paper, we first introduce a new dataset, Office-Home, to evaluate domain adaptation algorithms. The dataset contains images of a variety of everyday objects from multiple domains. We then propose a novel deep learning framework that can exploit labeled source data and unlabeled target data to learn informative hash codes, to accurately classify unseen target data. To the best of our knowledge, this is the first research effort to exploit the feature learning capabilities of deep neural networks to learn representative hash codes to address the domain adaptation problem. Our extensive empirical studies on multiple transfer tasks corroborate the usefulness of the framework in learning efficient hash codes which outperform existing competitive baselines for unsupervised domain adaptation.

关键词： Neural networks Adaptation models Machine learning computer vision Training Data models

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Long-Short Temporal Contrastive Learning of Video Transformers

Long-Short Temporal Contrastive Learning of Video Transforme...

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

作者： Wang, Jue Bertasius, Gedas Tran, Du Torresani, Lorenzo Facebook AI Res Menlo Pk CA 94025 USA Univ N Carolina Chapel Hill NC USA Dartmouth Hanover NH USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Video transformers have recently emerged as a competitive alternative to 3D CNNs for video understanding. However, due to their large number of parameters and reduced inductive biases, these models require supervised pretraining on large-scale image datasets to achieve top performance. In this paper, we empirically demonstrate that self-supervised pretraining of video transformers on video-only datasets can lead to action recognition results that are on par or better than those obtained with supervised pretraining on large-scale image datasets, even massive ones such as ImageNet-21K Since transformer-based models are effective at capturing dependencies over extended temporal spans, we propose a simple learning procedure that forces the model to match a long-term view to a short-term view of the same video. Our approach, named Long-Short Temporal Contrastive Learning (LSTCL), enables video transformers to learn an effective clip-level representation by predicting temporal context captured from a longer temporal extent. To demonstrate the generality of our findings, we implement and validate our approach under three different self-supervised contrastive learning frameworks (MoCo v3, BYOL, SimSiam) using two distinct video-transformer architectures, including an improved variant of the Swin Transformer augmented with space-time attention. We conduct a thorough ablation study and show that LSTCL achieves competitive performance on multiple video benchmarks and represents a convincing alternative to supervised image-based pretraining.

关键词： Representation learning computer vision Three-dimensional displays Image recognition computer architecture Benchmark testing Transformers

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Hybrid framework for surface registration and deformable models

Hybrid framework for surface registration and deformable mod...

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Proceedings of the 1997 ieee computer society conference on computer vision and pattern recognition

作者： Montagnat, Johan Delingette, Herve INRIA Sophia-Antipolis France

In computer vision, two complementary approaches have been widely used to perform object reconstruction and registration. The deformable model framework locally applies internal and external forces to fit 3D data. The non-rigid registration framework iteratively computes the best global transformation that minimizes the distance between a template and the data. In this paper, first we show that applying a global transformation on a surface model, is equivalent to applying an external force on a deformable model without any regularizing force. Second we propose a hybrid framework that combines the registration framework and the deformable models framework. Our hybrid deformation approach allows us to control the scale at which the model is deformed. This is clearly beneficial for performing both reconstruction and registration tasks. We show examples of this approach on active contours and deformable surfaces. Furthermore, a global transformation based on axial symmetry is introduced.

关键词： computer vision

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Recurrent vision Transformers for Object Detection with Event Cameras

Recurrent Vision Transformers for Object Detection with Even...

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

作者： Gehrig, Mathias Scaramuzza, Davide Univ Zurich Robot & Percept Grp Zurich Switzerland

ISBN: (纸本)9798350301298

We present Recurrent vision Transformers (RVTs), a novel backbone for object detection with event cameras. Event cameras provide visual information with sub-millisecond latency at a high-dynamic range and with strong robustness against motion blur. These unique properties offer great potential for low-latency object detection and tracking in time-critical scenarios. Prior work in event-based vision has achieved outstanding detection performance but at the cost of substantial inference time, typically beyond 40 milliseconds. By revisiting the high-level design of recurrent vision backbones, we reduce inference time by a factor of 6 while retaining similar performance. To achieve this, we explore a multi-stage design that utilizes three key concepts in each stage: first, a convolutional prior that can be regarded as a conditional positional embedding. Second, local and dilated global self-attention for spatial feature interaction. Third, recurrent temporal feature aggregation to minimize latency while retaining temporal information. RVTs can be trained from scratch to reach state-of-the-art performance on event-based object detection - achieving an mAP of 47.2% on the Gen1 automotive dataset. At the same time, RVTs offer fast inference (< 12 ms on a T4 GPU) and favorable parameter efficiency (5x fewer than prior art). Our study brings new insights into effective design choices that can be fruitful for research beyond event-based vision. Code: https://***/uzh-rpg/RVT

关键词： detection recognition: Categorization retrieval

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A consensus structure inference algorithm

引用

Proceedings of the 1991 ieee computer society conference on computer vision and pattern recognition

作者： Chiu, David K.Y. Dept of Comput & Inf Sci Univ of Guelph Canada

ISBN: (纸本)0818621486

The notion of consensus structure, a special form of random graph, is formally defined. The consensus structure represents an extended higher-order representation of the random n-tuple for statistical and structural pattern recognition. An algorithm inferring the consensus structure from a random n-tuple is designed based on the detection of statistical interdependency under certain structural constraints. In applying structural constraints, a circular diagram indicating variable interaction is used to extract global structural features in a macromolecular modeling problem.

关键词： pattern recognition

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Distilled Person Re-identification: Towards a More Scalable System 32

Distilled Person Re-identification: Towards a More Scalable ...

引用

32nd ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Wu, Ancong Zheng, Wei-Shi Guo, Xiaowei Lai, Jian-Huang Sun Yat Sen Univ Sch Elect & Informat Technol Guangzhou Guangdong Peoples R China Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou Guangdong Peoples R China Minist Educ Key Lab Machine Intelligence & Adv Comp Guangzhou Guangdong Peoples R China Guangdong Prov Key Lab Informat Secur Guangzhou Guangdong Peoples R China Tencent YouTu Lab Shenzhen Guangdong Peoples R China

ISBN: (纸本)9781728132938

Person re-identification (Re-ID), for matching pedestrians across non-overlapping camera views, has made great progress in supervised learning with abundant labelled data. However, the scalability problem is the bottleneck for applications in large-scale systems. We consider the scalability problem of Re-ID from three aspects: (1) low labelling cost by reducing label amount, (2) low extension cost by reusing existing knowledge and (3) low testing computation cost by using lightweight models. The requirements render scalable Re-ID a challenging problem. To solve these problems in a unified system, we propose a Multi-teacher Adaptive Similarity Distillation Framework, which requires only a few labelled identities of target domain to transfer knowledge from multiple teacher models to a user-specified lightweight student model without accessing source domain data. We propose the Log-Euclidean Similarity Distillation Loss for Re-ID and further integrate the Adaptive Knowledge Aggregator to select effective teacher models to transfer target-adaptive knowledge. Extensive evaluations show that our method can extend with high scalability and the performance is comparable to the state-of-the-art unsupervised and semi-supervised Re-ID methods.

关键词： And Body Pose Categorization Face Gesture recognition: Detection Retrieval vision Applications and Systems

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Extracting Triangular 3D Models, Materials, and Lighting From Images

Extracting Triangular 3D Models, Materials, and Lighting Fro...

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

作者： Munkberg, Jacob Chen, Wenzheng Hasselgren, Jon Evans, Alex Shen, Tianchang Muller, Thomas Gao, Jun Fidler, Sanja NVIDIA Santa Clara CA 95051 USA Univ Toronto Toronto ON Canada Vector Inst Toronto ON Canada

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We present an efficient method for joint optimization of topology, materials and lighting from multi-view image observations. Unlike recent multi-view reconstruction approaches, which typically produce entangled 3D representations encoded in neural networks, we output triangle meshes with spatially-varying materials and environment lighting that can be deployed in any traditional graphics engine unmodified. We leverage recent work in differentiable rendering, coordinate-based networks to compactly represent volumetric texturing, alongside differentiable marching tetrahedrons to enable gradient-based optimization directly on the surface mesh. Finally, we introduce a differentiable formulation of the split sum approximation of environment lighting to efficiently recover all-frequency lighting. Experiments show our extracted models used in advanced scene editing, material decomposition, and high quality view interpolation, all running at interactive rates in triangle-based renderers (rasterizers and path tracers).

关键词： Graphics Solid modeling Three-dimensional displays Computational modeling Lighting Rendering (computer graphics) Topology

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