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共 21008 条记录，以下是1431-1440 订阅

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Real-time Hyperspectral Imaging in Hardware via Trained Metasurface Encoders

Real-time Hyperspectral Imaging in Hardware via Trained Meta...

引用

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

作者： Makarenko, Maksim Burguete-Lopez, Arturo Wang, Qizhou Getman, Fedor Giancola, Silvio Ghanem, Bernard Fratalocchi, Andrea King Abdullah Univ Sci & Technol KAUST Thuwal 239556900 Saudi Arabia

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Hyperspectral imaging has attracted significant attention to identify spectral signatures for image classification and automated pattern recognition in computer vision. State-of-the-art implementations of snapshot hyperspectral imaging rely on bulky, non-integrated, and expensive optical elements, including lenses, spectrometers, and filters. These macroscopic components do not allow fast data processing for, e.g. real-time and high-resolution videos. This work introduces Hyplex (TM), a new integrated architecture addressing the limitations discussed above. Hyplex (TM) is a CMOS-compatible, fast hyperspectral camera that replaces bulk optics with nanoscale metasurfaces inversely designed through artificial intelligence. Hyplex (TM) does not require spectrometers but makes use of conventional monochrome cameras, opening up the possibility for real-time and high-resolution hyperspectral imaging at inexpensive costs. Hyplex (TM) exploits a model-driven optimization, which connects the physical metasurfaces layer with modern visual computing approaches based on end-to-end training. We design and implement a prototype version of Hyplex (TM) and compare its performance against the state-of-the-art for typical imaging tasks such as spectral reconstruction and semantic segmentation. In all benchmarks, Hyplex (TM) reports the smallest reconstruction error. We additionally present what is, to the best of our knowledge, the largest publicly available labeled hyperspectral dataset for semantic segmentation.(1)

关键词： Optical filters Image segmentation computer vision Semantics Metasurfaces Cameras Real-time systems

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APES: Articulated Part Extraction from Sprite Sheets

APES: Articulated Part Extraction from Sprite Sheets

引用

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

作者： Xu, Zhan Fisher, Matthew Zhou, Yang Aneja, Deepali Dudhat, Rushikesh Yi, Li Kalogerakis, Evangelos Univ Massachusetts Amherst MA 01003 USA Adobe Res San Jose CA USA Tsinghua Univ Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Rigged puppets are one of the most prevalent representations to create 2D character animations. Creating these puppets requires partitioning characters into independently moving parts. In this work, we present a method to automatically identify such articulated parts from a small set of character poses shown in a sprite sheet, which is an illustration of the character that artists often draw before puppet creation. Our method is trained to infer articulated parts, e.g. head, torso and limbs, that can be reassembled to best reconstruct the given poses. Our results demonstrate significantly better performance than alternatives qualitatively and quantitatively. Our project page https://***/parts/includes our code and data.

关键词： Torso computer vision Codes Animation pattern recognition Sprites (computer)

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IFOR: Iterative Flow Minimization for Robotic Object Rearrangement

IFOR: Iterative Flow Minimization for Robotic Object Rearran...

引用

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

作者： Goyal, Ankit Mousavian, Arsalan Paxton, Chris Chao, Yu-Wei Okorn, Brian Deng, Jia Fox, Dieter NVIDIA Santa Clara CA 95051 USA Princeton Univ Princeton NJ 08544 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Accurate object rearrangement from vision is a crucial problem for a wide variety of real-world robotics applications in unstructured environments. We propose IFOR, Iterative Flow Minimization for Robotic Object Rearrangement, an end-to-end method for the challenging problem of object rearrangement for unknown objects given an RGBD image of the original and final scenes. First, we learn an optical flow model based on RAFT to estimate the relative transformation of the objects purely from synthetic data. This flow is then used in an iterative minimization algorithm to achieve accurate positioning of previously unseen objects. Crucially, we show that our method applies to cluttered scenes, and in the real world, while training only on synthetic data. Videos are available at https://***/***.

关键词： Training computer vision Minimization Iterative algorithms Data models pattern recognition Optical flow

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Mix and Localize: Localizing Sound Sources in Mixtures

Mix and Localize: Localizing Sound Sources in Mixtures

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

作者： Hu, Xixi Chen, Ziyang Owens, Andrew Univ Michigan Ann Arbor MI 48109 USA Univ Texas Austin Austin TX 78712 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We present a method for simultaneously localizing multiple sound sources within a visual scene. This task requires a model to both group a sound mixture into individual sources, and to associate them with a visual signal. Our method jointly solves both tasks at once, using a formulation inspired by the contrastive random walk of Jabri et al. We create a graph in which images and separated sounds correspond to nodes, and train a random walker to transition between nodes from different modalities with high return probability. The transition probabilities for this walk are determined by an audio-visual similarity metric that is learned by our model. We show through experiments with musical instruments and human speech that our model can successfully localize multiple sounds, outperforming other self-supervised methods.

关键词： Measurement Visualization computer vision Computational modeling Instruments Music pattern recognition

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Deep Depth from Focus with Differential Focus Volume

Deep Depth from Focus with Differential Focus Volume

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

作者： Yang, Fengting Huang, Xiaolei Zhou, Zihan Penn State Univ University Pk PA 16802 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Depth-from-focus (DFF) is a technique that infers depth using the focus change of a camera. In this work, we propose a convolutional neural network (CNN) to find the best-focused pixels in a focal stack and infer depth from the focus estimation. The key innovation of the network is the novel deep differential focus volume (DFV). By computing the first-order derivative with the stacked features over different focal distances, DFV is able to capture both the focus and context information for focus analysis. Besides, we also introduce a probability regression mechanism for focus estimation to handle sparsely sampled focal stacks and provide uncertainty estimation to the final prediction. Comprehensive experiments demonstrate that the proposed model achieves state-of-the-art performance on multiple datasets with good generalizability and fast speed.

关键词： Technological innovation computer vision Uncertainty Computational modeling Estimation computer architecture pattern recognition

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GenDR: A Generalized Differentiable Renderer

GenDR: A Generalized Differentiable Renderer

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

作者： Petersen, Felix Goldluecke, Bastian Borgelt, Christian Deussen, Oliver Univ Konstanz Constance Germany Univ Salzburg Salzburg Austria

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In this work, we present and study a generalized family of differentiable renderers. We discuss from scratch which components are necessary for differentiable rendering and formalize the requirements for each component. We instantiate our general differentiable renderer, which generalizes existing differentiable renderers like SoftRas and DIB-R, with an array of different smoothing distributions to cover a large spectrum of reasonable settings. We evaluate an array of differentiable renderer instantiations on the popular ShapeNet 3D reconstruction benchmark and analyze the implications of our results. Surprisingly, the simple uniform distribution yields the best overall results when averaged over 13 classes;in general, however, the optimal choice of distribution heavily depends on the task.

关键词： computer vision Three-dimensional displays Smoothing methods Benchmark testing Rendering (computer graphics) pattern recognition Task analysis

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It's Time for Artistic Correspondence in Music and Video

It's Time for Artistic Correspondence in Music and Video

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

作者： Suris, Didac Vondrick, Carl Russell, Bryan Salamon, Justin Columbia Univ New York NY 10027 USA Adobe Res San Jose CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We present an approach for recommending a music track for a given video, and vice versa, based on both their temporal alignment and their correspondence at an artistic level. We propose a self-supervised approach that learns this correspondence directly from data, without any need of human annotations. In order to capture the high-level concepts that are required to solve the task, we propose modeling the long-term temporal context of both the video and the music signals, using Transformer networks for each modality. Experiments show that this approach strongly outperforms alternatives that do not exploit the temporal context. The combination of our contributions improve retrieval accuracy up to 10x over prior state of the art. This strong improvement allows us to introduce a wide range of analyses and applications. For instance, we can condition music retrieval based on visually defined attributes.

关键词： computer vision Annotations Transformers pattern recognition Multiple signal classification Task analysis Context modeling

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What to look at and where: Semantic and Spatial Refined Transformer for detecting human-object interactions

What to look at and where: Semantic and Spatial Refined Tran...

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

作者： Iftekhar, A. S. M. Chen, Hao Kundu, Kaustav Li, Xinyu Tighe, Joseph Modolo, Davide AWS AI Labs Seattle WA 98101 USA Univ Calif Santa Barbara Santa Barbara CA 93106 USA Amazon Seattle WA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We propose a novel one-stage Transformer-based semantic and spatial refined transformer (SSRT) to solve the Human-Object Interaction detection task, which requires to localize humans and objects, and predicts their interactions. Differently from previous Transformer-based HOI approaches, which mostly focus at improving the design of the decoder outputs for the final detection, SSRT introduces two new modules to help select the most relevant object-action pairs within an image and refine the queries' representation using rich semantic and spatial features. These enhancements lead to state-of-the-art results on the two most popular HOI benchmarks: V-COCO and HICO-DET.

关键词： computer vision Image analysis Semantics Benchmark testing Transformers Feature extraction pattern recognition

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More than Words: In-the-Wild Visually-Driven Prosody for Text-to-Speech

More than Words: In-the-Wild Visually-Driven Prosody for Tex...

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

作者： Hassid, Michael Ramanovich, Michelle Tadmor Shillingford, Brendan Wang, Miaosen Jia, Ye Remez, Tal Google Res Mountain View CA 94043 USA DeepMind London England

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In this paper we present VDTTS, a Visually-Driven Text-to-Speech model. Motivated by dubbing, VDTTS takes ad-vantage of video frames as an additional input alongside text, and generates speech that matches the video signal. We demonstrate how this allows VDTTS to, unlike plain TTS models, generate speech that not only has prosodic variations like natural pauses and pitch, but is also synchronized to the input video. Experimentally, we show our model produces well-synchronized outputs, approaching the video-speech synchronization quality of the ground-truth, on several challenging benchmarks including "in-the-wild" content from VoxCeleb2. Supplementary demo videos demonstrating video-speech synchronization, robustness to speaker ID swapping, and prosody, presented at the project page.(1)

关键词： computer vision Machine learning Benchmark testing Robustness pattern recognition Synchronization

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Exploiting Temporal Relations on Radar Perception for Autonomous Driving

Exploiting Temporal Relations on Radar Perception for Autono...

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

作者： Li, Peizhao Wang, Pu Berntorp, Karl Liu, Hongfu Brandeis Univ Waltham MA 02453 USA Mitsubishi Elect Res Labs Cambridge MA USA MERL Cambridge MA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We consider the object recognition problem in autonomous driving using automotive radar sensors. Comparing to Lidar sensors, radar is cost-effective and robust in all-weather conditions for perception in autonomous driving. However, radar signals suffer from low angular resolution and precision in recognizing surrounding objects. lb enhance the capacity of automotive radar, in this work, we exploit the temporal information from successive ego-centric bird-eye-view radar image frames for radar object recognition. We leverage the consistency of an object's existence and attributes (size, orientation, etc.), and propose a temporal relational layer to explicitly model the relations between objects within successive radar images. In both object detection and multiple object tracking, we show the superiority of our method compared to several baseline approaches.

关键词： Radar Object detection Radar imaging Sensors pattern recognition Object recognition Object tracking

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