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UNIST: Unpaired Neural Implicit Shape Translation Network

UNIST: Unpaired Neural Implicit Shape Translation Network

引用

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

作者： Chen, Qimin Merz, Johannes Sanghi, Aditya Shayani, Hooman Mahdavi-Amiri, Ali Zhang, Hao Simon Fraser Univ Burnaby BC Canada Autodesk AI Lab San Rafael CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We introduce UNIST, the first deep neural implicit model for general-purpose, unpaired shape-to-shape translation, in both 2D and 3D domains. Our model is built on autoencoding implicit fields, rather than point clouds which represents the state of the art. Furthermore, our translation network is trained to perform the task over a latent grid representation which combines the merits of both latent-space processing and position awareness, to not only enable drastic shape transforms but also well preserve spatial features and fine local details for natural shape translations. With the same network architecture and only dictated by the input domain pairs, our model can learn both style-preserving content alteration and content-preserving style transfer. We demonstrate the generality and quality of the translation results, and compare them to well-known baselines. Code is available at https://***/unist/.

关键词： Point cloud compression Solid modeling computer vision Three-dimensional displays Shape Computational modeling Transforms

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Adaptive Gating for Single-Photon 3D Imaging

Adaptive Gating for Single-Photon 3D Imaging

引用

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

作者： Po, Ryan Pediredla, Adithya Gkioulekas, Ioannis Carnegie Mellon Univ Robot Inst Pittsburgh PA 15213 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Single-photon avalanche diodes (SPADs) are growing in popularity for depth sensing tasks. However, SPADs still struggle in the presence of high ambient light due to the effects of pile-up. Conventional techniques leverage fixed or asynchronous gating to minimize pile-up effects, but these gating schemes are all non-adaptive, as they are unable to incorporate factors such as scene priors and previous photon detections into their gating strategy. We propose an adaptive gating scheme built upon Thompson sampling. Adaptive gating periodically updates the gate position based on prior photon observations in order to minimize depth errors. Our experiments show that our gating strategy results in significantly reduced depth reconstruction error and acquisition time, even when operating outdoors under strong sunlight conditions.

关键词： computer vision Three-dimensional displays Imaging Logic gates Sensors pattern recognition Task analysis

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Interactive Disentanglement: Learning Concepts by Interacting with their Prototype Representations

Interactive Disentanglement: Learning Concepts by Interactin...

引用

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

作者： Stammer, Wolfgang Memmel, Marius Schramowski, Patrick Kersting, Kristian Tech Univ Darmstadt Comp Sci Dept Darmstadt Germany Tech Univ Darmstadt Ctr Cognit Sci Darmstadt Germany Hessian Ctr AI Hessian AI Darmstadt Germany

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Learning visual concepts from raw images without strong supervision is a challenging task. In this work, we show the advantages of prototype representations for understanding and revising the latent space of neural concept learners. For this purpose, we introduce interactive Concept Swapping Networks (iCSNs), a novel framework for learning concept-grounded representations via weak supervision and implicit prototype representations. iCSNs learn to bind conceptual information to specific prototype slots by swapping the latent representations of paired images. This semantically grounded and discrete latent space facilitates human understanding and human-machine interaction. We support this claim by conducting experiments on our novel data set "Elementary Concept Reasoning" (ECR), focusing on visual concepts shared by geometric objects.(1)

关键词： Deep learning Representation learning Human computer interaction Visualization computer vision Prototypes Cognition

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Audio-driven Neural Gesture Reenactment with Video Motion Graphs

Audio-driven Neural Gesture Reenactment with Video Motion Gr...

引用

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

作者： Zhou, Yang Yang, Jimei Li, Dingzeyu Saito, Jun Aneja, Deepali Kalogerakis, Evangelos Univ Massachusetts Amherst Amherst MA 01003 USA Adobe Res San Jose CA 95113 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Human speech is often accompanied by body gestures including arm and hand gestures. We present a method that reenacts a high-quality video with gestures matching a target speech audio. The key idea of our method is to split and re-assemble clips from a reference video through a novel video motion graph encoding valid transitions between clips. To seamlessly connect different clips in the reenactment, we propose a pose-aware video blending network which synthesizes video frames around the stitched frames between two clips. Moreover, we developed an audio-based gesture searching algorithm to find the optimal order of the reenacted frames. Our system generates reen-actments that are consistent with both the audio rhythms and the speech content. We evaluate our synthesized video quality quantitatively, qualitatively, and with user studies, demonstrating that our method produces videos of much higher quality and consistency with the target audio compared to previous work and baselines. Our project page https://***/yzhou359/vid-reenact includes code and data.

关键词： computer vision Codes Encoding Quality assessment pattern recognition Video recording

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Coupled Iterative Refinement for 6D Multi-Object Pose Estimation

Coupled Iterative Refinement for 6D Multi-Object Pose Estima...

引用

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

作者： Lipson, Lahav Teed, Zachary Goyal, Ankit Deng, Jia Princeton Univ Princeton NJ 08544 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We address the task of 6D multi-object pose: given a set of known 3D objects and an RGB or RGB-D input image, we detect and estimate the 6D pose of each object. We propose a new approach to 6D object pose estimation which consists of an end-to-end differentiable architecture that makes use of geometric knowledge. Our approach iteratively refines both pose and correspondence in a tightly coupled manner, allowing us to dynamically remove outliers to improve accuracy. We use a novel differentiable layer to perform pose refinement by solving an optimization problem we refer to as Bidirectional Depth-Augmented Perspective-N-Point (BD-PnP). Our method achieves state-of-the-art accuracy on standard 6D Object Pose benchmarks. Code is available at https://***/princeton-vl/Coupled-Iterative-Refinement.

关键词： computer vision Three-dimensional displays Codes Pose estimation Benchmark testing Iterative methods Task analysis

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OMNIVORE: A Single Model for Many Visual Modalities

OMNIVORE: A Single Model for Many Visual Modalities

引用

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

作者： Girdhar, Rohit Singh, Mannat Ravi, Nikhila van der Maaten, Laurens Joulin, Armand Misra, Ishan Meta AI Menlo Pk CA 94025 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Prior work has studied different visual modalities in isolation and developed separate architectures for recognition of images, videos, and 3D data. Instead, in this paper, we propose a single model which excels at classifying images, videos, and single-view 3D data using exactly the same model parameters. Our 'OMNIVORE' model leverages the flexibility of transformer-based architectures and is trained jointly on classification tasks from different modalities. OMNIVORE is simple to train, uses off-the-shelf standard datasets, and performs at-par or better than modality-specific models of the same size. A single OMNIVORE model obtains 86.0% on ImageNet, 84.1% on Kinetics, and 67.1% on SUN RGB-D. After finetuning, our models outperform prior work on a variety of vision tasks and generalize across modalities. OMNIVORE 's shared visual representation naturally enables cross-modal recognition without access to correspondences between modalities. We hope our results motivate researchers to model visual modalities together.

关键词： Visualization Solid modeling Three-dimensional displays Computational modeling computer architecture Transformers Data models

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vision-Language Pre-Training for Boosting Scene Text Detectors

Vision-Language Pre-Training for Boosting Scene Text Detecto...

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

作者： Song, Sibo Wan, Jianqiang Yang, Zhibo Tang, Jun Cheng, Wenqing Bai, Xiang Yao, Cong Alibaba Grp DAMO Acad Hangzhou Peoples R China Huazhong Univ Sci & Technol Wuhan Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Recently, vision-language joint representation learning has proven to be highly effective in various scenarios. In this paper, we specifically adapt vision-language joint learning for scene text detection, a task that intrinsically involves cross-modal interaction between the two modalities: vision and language, since text is the written form of language. Concretely, we propose to learn contextualized, joint representations through vision-language pretraining, for the sake of enhancing the performance of scene text detectors. Towards this end, we devise a pre-training architecture with an image encoder, a text encoder and a cross-modal encoder, as well as three pretext tasks: imagetext contrastive learning (ITC), masked language modeling (MLM) and word-in-image prediction (WIP). The pretrained model is able to produce more informative representations with richer semantics, which could readily benefit existing scene text detectors (such as EAST and PSENet) in the down-stream text detection task. Extensive experiments on standard benchmarks demonstrate that the proposed paradigm can significantly improve the performance of various representative text detectors, outperforming previous pre-training approaches. The code and pre-trained models will be publicly released.

关键词： Representation learning computer vision Text recognition Computational modeling Semantics Detectors computer architecture

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Robust outlier detection by de-biasing VAE likelihoods

Robust outlier detection by de-biasing VAE likelihoods

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

作者： Chauhan, Kushal Mohan, Barath U. Shenoy, Pradeep Gupta, Manish Sridharan, Devarajan Google Res Mountain View CA USA Indian Inst Sci Ctr Neurosci & Comp Sci & Automat Bengaluru India

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Deep networks often make confident, yet, incorrect, predictions when tested with outlier data that is far removed from their training distributions. Likelihoods computed by deep generative models (DGMs) are a candidate metric for outlier detection with unlabeled data. Yet, previous studies have shown that DGM likelihoods are unreliable and can be easily biased by simple transformations to input data. Here, we examine outlier detection with variational autoencoders (VAEs), among the simplest of DGMs. We propose novel analytical and algorithmic approaches to ameliorate key biases with VAE likelihoods. Our bias corrections are sample-specific, computationally inexpensive, and readily computed for various decoder visible distributions. Next, we show that a well-known image preprocessing technique - contrast stretching - extends the effectiveness of bias correction to further improve outlier detection. Our approach achieves state-of-the-art accuracies with nine grayscale and natural image datasets, and demonstrates significant advantages - both with speed and performance - over four recent, competing approaches. In summary, lightweight remedies suffice to achieve robust outlier detection with VAEs.

关键词： Training Measurement computer vision Computational modeling Gray-scale Data models pattern recognition

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Targeted Supervised Contrastive Learning for Long-Tailed recognition

Targeted Supervised Contrastive Learning for Long-Tailed Rec...

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

作者： Li, Tianhong Cao, Peng Yuan, Yuan Fan, Lijie Yang, Yuzhe Feris, Rogerio Indyk, Piotr Katabi, Dina MIT CSAIL Cambridge MA 02139 USA MIT IBM Watson AI Lab Cambridge MA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Real-world data often exhibits long tail distributions with heavy class imbalance, where the majority classes can dominate the training process and alter the decision boundaries of the minority classes. Recently, researchers have investigated the potential of supervised contrastive learning for long-tailed recognition, and demonstrated that it provides a strong performance gain. In this paper, we show that while supervised contrastive learning can help improve performance, past baselines suffer from poor uniformity brought in by imbalanced data distribution. This poor uniformity manifests in samples from the minority class having poor separability in the feature space. To address this problem, we propose targeted supervised contrastive learning (TSC), which improves the uniformity of the feature distribution on the hypersphere. TSC first generates a set of targets uniformly distributed on a hypersphere. It then makes the features of different classes converge to these distinct and uniformly distributed targets during training. This forces all classes, including minority classes, to maintain a uniform distribution in the feature space, improves class boundaries, and provides better generalization even in the presence of long-tail data. Experiments on multiple datasets show that TSC achieves state-of-the-art performance on long-tailed recognition tasks.

关键词： Training computer vision Target recognition Tail Performance gain pattern recognition Task analysis

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RADU: Ray-Aligned Depth Update Convolutions for ToF Data Denoising

RADU: Ray-Aligned Depth Update Convolutions for ToF Data Den...

引用

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

作者： Schelling, Michael Hermosilla, Pedro Ropinski, Timo Ulm Univ Ulm Germany

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Time-of-Flight (ToF) cameras are subject to high levels of noise and distortions due to Multi-Path-Interference (MPI). While recent research showed that 2D neural networks are able to outperform previous traditional State-of-the-Art (SOTA) methods on correcting ToF-Data, little research on learning-based approaches has been done to make direct use of the 3D information present in depth images. In this paper, we propose an iterative correcting approach operating in 3D space, that is designed to learn on 2.5D data by enabling 3D point convolutions to correct the points' positions along the view direction. As labeled real world data is scarce for this task, we further train our network with a self-training approach on unlabeled real world data to account for real world statistics. We demonstrate that our method is able to outperform SOTA methods on several datasets, including two real world datasets and a new large-scale synthetic data set introduced in this paper.

关键词： Point cloud compression computer vision Three-dimensional displays Neural networks Noise reduction Distortion Cameras

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