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SGCN:Sparse Graph Convolution Network for Pedestrian Trajectory Prediction

SGCN:Sparse Graph Convolution Network for Pedestrian Traject...

引用

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

作者： Shi, Liushuai Wang, Le Long, Chengjiang Zhou, Sanping Zhou, Mo Niu, Zhenxing Hua, Gang Xi An Jiao Tong Univ Sch Software Engn Xian Peoples R China Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian Peoples R China JD Finance Amer Corp Mountain View CA USA Alibaba Grp Machine Intelligence Lab Hangzhou Peoples R China Wormpex AI Res Bellevue WA USA

ISBN: (纸本)9781665445092

Pedestrian trajectory prediction is a key technology in autopilot, which remains to be very challenging due to complex interactions between pedestrians. However, previous works based on dense undirected interaction suffer from modeling superfluous interactions and neglect of trajectory motion tendency, and thus inevitably result in a considerable deviance from the reality. To cope with these issues, we present a Sparse Graph Convolution Network (SGCN) for pedestrian trajectory prediction. Specifically, the SGCN explicitly models the sparse directed interaction with a sparse directed spatial graph to capture adaptive interaction pedestrians. Meanwhile, we use a sparse directed temporal graph to model the motion tendency, thus to facilitate the prediction based on the observed direction. Finally, parameters of a bi-Gaussian distribution for trajectory prediction are estimated by fusing the above two sparse graphs. We evaluate our proposed method on the ETH and UCY datasets, and the experimental results show our method outperforms comparative state-of-the-art methods by 9% in Average Displacement Error (ADE) and 13% in Final Displacement Error (FDE). Notably, visualizations indicate that our method can capture adaptive interactions between pedestrians and their effective motion tendencies.

关键词： Legged locomotion Adaptation models Visualization computer vision Convolution Predictive models Trajectory

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Learning Compositional Radiance Fields of Dynamic Human Heads

Learning Compositional Radiance Fields of Dynamic Human Head...

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

作者： Wang, Ziyan Bagautdinov, Timur Lombardi, Stephen Simon, Tomas Saragih, Jason Hodgins, Jessica Zollhofer, Michael Carnegie Mellon Univ Pittsburgh PA 15213 USA Facebook AI Res Menlo Pk CA USA Facebook Real Labs Res Menlo Pk CA 94025 USA

ISBN: (纸本)9781665445092

Photorealistic rendering of dynamic humans is an important capability for telepresence systems, virtual shopping, special effects in movies, and interactive experiences such as games. Recently, neural rendering methods have been developed to create high-fidelity models of humans and objects. Some of these methods do not produce results with high-enough fidelity for driveable human models (Neural Volumes) whereas others have extremely long rendering times (NeRF). We propose a novel compositional 3D representation that combines the best of previous methods to produce both higher-resolution and faster results. Our representation bridges the gap between discrete and continuous volumetric representations by combining a coarse 3D-structure-aware grid of animation codes with a continuous learned scene function that maps every position and its corresponding local animation code to a view-dependent emitted radiance and local volume density. Differentiable volume rendering is employed to compute photo-realistic novel views of the human head and upper body as well as to train our novel representation end-to-end using only 2D supervision. In addition, we show that the learned dynamic radiance field can be used to synthesize novel unseen expressions based on a global animation code. Our approach achieves state-of-the-art results for synthesizing novel views of dynamic human heads and the upper body. See our project page' for more results.

关键词： Interpolation Codes Telepresence Three-dimensional displays Aerospace electronics Rendering (computer graphics) Animation

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VA3: Virtually Assured Amplification Attack on Probabilistic Copyright Protection for Text-to-Image Generative Models

VA3: Virtually Assured Amplification Attack on Probabilistic...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Xiang Li Qianli Shen Kenji Kawaguchi National University of Singapore

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

The booming use of text-to-image generative models has raised concerns about their high risk of producing copyright-infringing content. While probabilistic copyright protection methods provide a probabilistic guarantee against such infringement, in this paper, we introduce Virtually Assured Amplification Attack (VA3), a novel online attack framework that exposes the vulnerabilities of these protection mechanisms. The proposed framework significantly amplifies the probability of generating infringing content on the sustained interactions with generative models and a non-trivial lower-bound on the success probability of each engagement. Our theoretical and experimental results demonstrate the effectiveness of our approach under various scenarios. These findings highlight the potential risk of implementing probabilistic copyright protection in practical applications of text-to-image generative models. Code is available at https://***/South7X/VA3.

关键词： computer vision Codes Text to image Closed box Copyright protection Probabilistic logic pattern recognition

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HOOD: Hierarchical Graphs for Generalized Modelling of Clothing Dynamics

HOOD: Hierarchical Graphs for Generalized Modelling of Cloth...

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conference on computer vision and pattern recognition (cvpr)

作者： Artur Grigorev Michael J. Black Otmar Hilliges Department of Computer Science ETH Zurich Max Planck Institute for Intelligent Systems Tübingen

We propose a method that leverages graph neural networks, multi-level message passing, and unsupervised training to enable efficient prediction of realistic clothing dynamics. Whereas existing methods based on linear blend skinning must be trained for specific garments, our method, called HOOD, is agnostic to body shape and applies to tight-fitting garments as well as loose, free-flowing clothing. Furthermore, HOOD handles changes in topology (e.g., garments with buttons or zippers) and material properties at inference time. As one key contribution, we propose a hierarchical message-passing scheme that efficiently propagates stiff stretching modes while preserving local detail. We empirically show that HOOD outperforms strong baselines quantitatively and that its results are perceived as more realistic than state-of-the-art methods.

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Beyond VQA: Generating Multi-word Answers and Rationales to Visual Questions

Beyond VQA: Generating Multi-word Answers and Rationales to ...

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

作者： Dua, Radhika Kancheti, Sai Srinivas Balasubramanian, Vineeth N. Indian Inst Technol Hyderabad Hyderabad India

ISBN: (纸本)9781665448994

Visual Question Answering is a multi-modal task that aims to measure high-level visual understanding. Contemporary VQA models are restrictive in the sense that answers are obtained via classification over a limited vocabulary (in the case of open-ended VQA), or via classification over a set of multiple-choice-type answers. In this work, we present a completely generative formulation where a multi-word answer is generated for a visual query. To take this a step forward, we introduce a new task: ViQAR (Visual Question Answering and Reasoning), wherein a model must generate the complete answer and a rationale that seeks to justify the generated answer. We propose an end-to-end architecture to solve this task and describe how to evaluate it. We show that our model generates strong answers and rationales through qualitative and quantitative evaluation, as well as through a human Turing Test.

关键词： Deep learning Visualization Vocabulary Computational modeling conferences computer architecture Knowledge discovery

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Perceptual Image Quality Assessment with Transformers

Perceptual Image Quality Assessment with Transformers

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

作者： Cheon, Manri Yoon, Sung-Jun Kang, Byungyeon Lee, Junwoo LG Elect Seoul South Korea

ISBN: (纸本)9781665448994

In this paper, we propose an image quality transformer (IQT) that successfully applies a transformer architecture to a perceptual full-reference image quality assessment (IQA) task. Perceptual representation becomes more important in image quality assessment. In this context, we extract the perceptual feature representations from each of input images using a convolutional neural network (CNN) backbone. The extracted feature maps are fed into the transformer encoder and decoder in order to compare a reference and distorted images. Following an approach of the transformer-based vision models [18, 55], we use extra learnable quality embedding and position embedding. The output of the transformer is passed to a prediction head in order to predict a final quality score. The experimental results show that our proposed model has an outstanding performance for the standard IQA datasets. For a large-scale IQA dataset containing output images of generative model, our model also shows the promising results. The proposed IQT was ranked first among 13 participants in the NTIRE 2021 perceptual image quality assessment challenge [23]. Our work will be an opportunity to further expand the approach for the perceptual IQA task.

关键词： Image quality Measurement Image resolution Head Feature extraction Quality assessment pattern recognition

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Auto-X3D: Ultra-Efficient Video Understanding via Finer-Grained Neural Architecture Search 22

Auto-X3D: Ultra-Efficient Video Understanding via Finer-Grai...

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22nd ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Jiang, Yifan Gong, Xinyu Wu, Junru Shi, Humphrey Yan, Zhicheng Wang, Zhangyang Univ Texas Austin Austin TX 78712 USA Texas A&M Univ College Stn TX 77843 USA Univ Oregon Eugene OR 97403 USA Facebook AI Menlo Pk CA USA Picsart AI Res PAIR Eugene OR USA

ISBN: (纸本)9781665409155

Efficient video architecture is the key to deploying video recognition systems on devices with limited computing resources. Unfortunately, existing video architectures are often computationally intensive and not suitable for such applications. The recent X3D work presents a new family of efficient video models by expanding a hand-crafted image architecture along multiple axes, such as space, time, width, and depth. Although operating in a conceptually large space, X3D searches one axis at a time, and merely explored a small set of 30 architectures in total, which does not sufficiently explore the space. This paper bypasses existing 2D architectures, and directly searched for 3D architectures in a fine-grained space, where block type, filter number, expansion ratio and attention block are jointly searched. A probabilistic neural architecture search method is adopted to efficiently search in such a large space. Evaluations on Kinetics and Something-Something-V2 benchmarks confirm our AutoX3D models outperform existing ones in accuracy up to 1.3% under similar FLOPs, and reduce the computational cost up to x1.74 when reaching similar performance.

关键词： computer vision Computational modeling Search methods computer architecture X3D Benchmark testing Probabilistic logic

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Tree Instance Segmentation with Temporal Contour Graph

Tree Instance Segmentation with Temporal Contour Graph

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conference on computer vision and pattern recognition (cvpr)

作者： Adnan Firoze Cameron Wingren Raymond A. Yeh Bedrich Benes Daniel Aliaga Department of Computer Science Purdue University Department of Forestry and Natural Resources Purdue University

We present a novel approach to perform instance segmentation and counting for densely packed self-similar trees using a top-view RGB image sequence. We propose a solution that leverages pixel content, shape, and self-occlusion. First, we perform an initial over-segmentation of the image sequence and aggregate structural characteristics into a contour graph with temporal information incorporated. Second, using a graph convolutional network and its inherent local messaging passing abilities, we merge adjacent tree crown patches into a final set of tree crowns. Per various studies and comparisons, our method is superior to all prior methods and results in high-accuracy instance segmentation and counting despite the trees being tightly packed. Finally, we provide various forest image sequence datasets suitable for subsequent benchmarking and evaluation captured at different altitudes and leaf conditions.

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Coordinate Attention for Efficient Mobile Network Design

Coordinate Attention for Efficient Mobile Network Design

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

作者： Hou, Qibin Zhou, Daquan Feng, Jiashi Natl Univ Singapore Singapore Singapore SEA AI Lab Singapore Singapore

ISBN: (纸本)9781665445092

Recent studies on mobile network design have demonstrated the remarkable effectiveness of channel attention (e.g., the Squeeze-and-Excitation attention) for lifting model performance, but they generally neglect the positional information, which is important for generating spatially selective attention maps. In this paper, we propose a novel attention mechanism for mobile networks by embedding positional information into channel attention, which we call "coordinate attention". Unlike channel attention that transforms a feature tensor to a single feature vector via 2D global pooling, the coordinate attention factorizes channel attention into two 1D feature encoding processes that aggregate features along the two spatial directions, respectively. In this way, long-range dependencies can be captured along one spatial direction and meanwhile precise positional information can be preserved along the other spatial direction. The resulting feature maps are then encoded separately into a pair of direction-aware and position-sensitive attention maps that can be complementarily applied to the input feature map to augment the representations of the objects of interest. Our coordinate attention is simple and can be flexibly plugged into classic mobile networks, such as MobileNetV2, MobileNeXt, and EfficientNet with nearly no computational overhead. Extensive experiments demonstrate that our coordinate attention is not only beneficial to ImageNet classification but more interestingly, behaves better in down-stream tasks, such as object detection and semantic segmentation. Code is available at https://***/Andrew-Qibin/CoordAttention.

关键词： Image segmentation computer vision Tensors Computational modeling Semantics Object detection Transforms

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The Untapped Potential of Off-the-Shelf Convolutional Neural Networks 22

The Untapped Potential of Off-the-Shelf Convolutional Neural...

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22nd ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Inkawhich, Matthew Inkawhich, Nathan Davis, Eric Li, Hai Chen, Yiran Duke Univ Durham NC 27706 USA SRC Inc Syracuse NY USA

ISBN: (纸本)9781665409155

Over recent years, a myriad of novel convolutional network architectures have been developed to advance state-of-the-art performance on challenging recognition tasks. As computational resources improve, a great deal of effort has been placed on efficiently scaling up existing designs and generating new architectures with Neural Architecture Search (NAS) algorithms. While network topology has proven to be a critical factor for model performance, we show that significant gains are being left on the table by keeping topology static at inference-time. Due to challenges such as scale variation, we should not expect static models configured to perform well across a training dataset to be optimally configured to handle all test data. In this work, we expose the exciting potential of inference-time-dynamic models. We show that by allowing just four layers to dynamically change configuration at inference-time, off-the-shelf models like ResNet-50 have an upper bound accuracy of over 95% on ImageNet. This level of performance currently exceeds that of models with over 20x more parameters and significantly more complex training procedures. While this upper bound of performance may be practically difficult to achieve for a real dynamic model, it indicates a significant source of untapped potential for current models.

关键词： Training Upper bound Convolution computer architecture Network architecture Inference algorithms Data models

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