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Sports Field Registration via Keypoints-aware Label Condition

Sports Field Registration via Keypoints-aware Label Conditio...

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

作者： Chu, Yen-Jui Su, Jheng-Wei Hsiao, Kai-Wen Lien, Chi-Yu Fan, Shu-Ho Hu, Min-Chun Lee, Ruen-Rone Yao, Chih-Yuan Chu, Hung-Kuo Natl Tsing Hua Univ Hsinchu Taiwan Delta Res Ctr Hsinchu Taiwan Natl Taiwan Univ Sci & Technol Taipei Taiwan

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We propose a novel deep learning framework for sports field registration. The typical algorithmic flow for sports field registration involves extracting field-specific features (e.g., corners, lines, etc.) from field image and estimating the homography matrix between a 2D field template and the field image using the extracted features. Unlike previous methods that strive to extract sparse field features from field images with uniform appearance, we tackle the problem differently. First, we use a grid of uniformly distributed keypoints as our field-specific features to increase the likelihood of having sufficient field features under various camera poses. Then we formulate the keypoints detection problem as an instance segmentation with dynamic filter learning. In our model, the convolution filters are generated dynamically, conditioned on the field image and associated keypoint identity, thus improving the robustness of prediction results. To extensively evaluate our method, we introduce a new soccer dataset, called TS-WorldCup, with detailed field markings on 3812 time-sequence images from 43 videos of SoccerWorld Cup 2014 and 2018. The experimental results demonstrate that our method outperforms state-of-the-arts on the TS-WorldCup dataset in both quantitative and qualitative evaluations. Both the code and dataset are available online(1).

关键词： Image segmentation Predictive models Feature extraction Robustness pattern recognition Sparse matrices Task analysis

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SwiniPASSR: Swin Transformer based Parallax Attention Network for Stereo Image Super-Resolution

SwiniPASSR: Swin Transformer based Parallax Attention Networ...

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

作者： Jin, Kai Wei, Zeqiang Yang, Angulia Guo, Sha Gao, Mingzhi Zhou, Xiuzhuang Guo, Guodong Bigo Technol Pte Ltd Beijing Peoples R China Beijing Univ Posts & Telecommun Smart Med Innovat Lab Beijing Peoples R China Peking Univ Inst Digital Media Beijing Peoples R China Baidu Res Inst Deep Learning Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

With binocular cameras being widely accepted, the study of stereo image super resolution (Stereo SR) has received increasing attention. Different from single image super resolution (SISR) setting, it is more challenging for utilizing both intra-view and cross-view information. Although prior convolution-based works have achieved admirable progress, few attempts have explored the possibility of the Transformer-based architecture for stereo image SR, which has demonstrated promising performance in several visual tasks. In this paper, we propose a novel approach namely SwiniPASSR, which adopts Swin Transformer as the backbone, meanwhile incorporating it with the Bi-directional Parallax Attention Module (biPAM) to maximize auxiliary information given by the binocular mechanism. Even Transformer and parallax attention mechanism (PAM) have been separately proved usefulness by prior studies, we find that simply integrating convolution-based PAM with Transformer or directly optimizing for stereo SR problem was may not achieve desirable result. We therefore introduced a conversion layer to resolve integration and adopted progressive training strategy to learn disparity correspondence through progressively enlarged receptive fields. Both extensive experiments and ablation studies demonstrate the effectiveness of our proposed SwiniPASSR. In particular, in the NTIRE 2022: Stereo Image Super-Resolution Challenge, we report 23.71dB PSNR and 0.7295 SSIM performance which ranked 2nd place on the leaderboard.

关键词： Training Convolutional codes Visualization Superresolution Estimation Transformers pattern recognition

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Characterizing Target-absent Human Attention

Characterizing Target-absent Human Attention

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

作者： Chen, Yupei Yang, Zhibo Chakraborty, Souradeep Mondal, Sounak Ahn, Seoyoung Samaras, Dimitris Hoai, Minh Zelinsky, Gregory Smith Kettlewell Eye Res Inst 2232 Webster St San Francisco CA 94115 USA SUNY Stony Brook Stony Brook NY 11794 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Human efficiency in finding a target in an image has attracted the attention of machine learning researchers, but what about when no target is there? Knowing how people search in the absence of a target, and when they stop, is important for Human-computer-interaction systems attempting to predict human gaze behavior in the wild. Here we report a rigorous evaluation of target-absent search behavior using the COCO-Search18 dataset to train state-of-the-art models. We focus on two specific aims. First, we characterize the presence of a target guidance signal in target-absent search behavior by comparing it to target-present guidance and free viewing. We do this by comparing how well a model trained on one type of fixation behavior (target-present, target-absent, free viewing) can predict behavior in either the same or different task. To compare target-absent search to free viewing behavior we created COCO-FreeView, a dataset of free-viewing fixations for the same images used in COCO-Search18. These comparisons revealed the existence of a target guidance signal in target-absent search, albeit one much less dominant compared to when a target actually appeared in an image, and that the target-absent guidance signal was similar to free viewing in that saliency and center bias were both weighted more than guidance from target features. Our second aim focused on the stopping criteria, a question intrinsic to target-absent search. Here we propose to train a foveated target detector whose target detection representation is sensitive to the relationship between distance from the fovea. Then combining the predicted target detection representation with other information such as fixation history and subject ID, our model outperforms the baselines in predicting when a person stops moving his attention during target-absent search.

关键词： computer vision conferences Object detection Machine learning Detectors Predictive models Behavioral sciences

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Investigating Neural Architectures by Synthetic Dataset Design

Investigating Neural Architectures by Synthetic Dataset Desi...

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

作者： Courtois, Adrien Morel, Jean-Michel Arias, Pablo ENS Paris Saclay Ctr Borelli 4 Av Sci F-91190 Gif Sur Yvette France

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Recent years have seen the emergence of many new neural network structures (architectures and layers). To solve a given task, a network requires a certain set of abilities reflected in its structure. The required abilities depend on each task. There is so far no systematic study of the real capacities of the proposed neural structures. The question of what each structure can and cannot achieve is only partially answered by its performance on common benchmarks. Indeed, natural data contain complex unknown statistical cues. It is therefore impossible to know what cues a given neural structure is taking advantage of in such data. In this work, we sketch a methodology to measure the effect of each structure on a network's ability, by designing ad hoc synthetic datasets. Each dataset is tailored to assess a given ability and is reduced to its simplest form: each input contains exactly the amount of information needed to solve the task. We illustrate our methodology by building three datasets to evaluate each of the three following network properties: a) the ability to link local cues to distant inferences, b) the translation covariance and c) the ability to group pixels with the same characteristics and share information among them. Using a first simplified depth estimation dataset, we pinpoint a serious nonlocal deficit of the U-Net. We then evaluate how to resolve this limitation by embedding its structure with nonlocal layers, which allow computing complex features with long-range dependencies. Using a second dataset, we compare different positional encoding methods and use the results to further improve the U-Net on the depth estimation task. The third introduced dataset serves to demonstrate the need for self-attention-like mechanisms for resolving more realistic depth estimation tasks.

关键词： computer vision Systematics conferences Neural networks Buildings Estimation computer architecture

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Systematic Architectural Design of Scale Transformed Attention Condenser DNNs via Multi-Scale Class Representational Response Similarity Analysis

Systematic Architectural Design of Scale Transformed Attenti...

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2023 IEEE/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Hryniowski, Andrew Wong, Alexander Vision and Image Processing Research Group University of Waterloo Canada Waterloo Artificial Intelligence Institute WaterlooON Canada DarwinAI Corp. WaterlooON Canada

ISBN: (纸本)9798350302493

Self-attention mechanisms are commonly included in a convolutional neural networks to achieve an improved efficiency performance balance. However, adding self-attention mechanisms adds additional hyperparameters to tune for the application at hand. In this work we propose a novel type of DNN analysis called Multi-Scale Class Representational Response Similarity Analysis (ClassRep-Sim) which can be used to identify specific design interventions that lead to more efficient self-attention convolutional neural network architectures. Using insights grained from ClassRepSim we propose the Spatial Transformed Attention Condenser (STAC) module, a novel attention-condenser based self-attention module. We show that adding STAC modules to ResNet style architectures can result in up to a 1.6% increase in top-1 accuracy compared to vanilla ResNet models and up to a 0.5% increase in top-1 accuracy compared to SENet models on the ImageNet64x64 dataset, at the cost of up to 1.7% increase in FLOPs and 2x the number of parameters. In addition, we demonstrate that results from ClassRepSim analysis can be used to select an effective parameterization of the STAC module resulting in competitive performance compared to an extensive parameter search. © 2023 IEEE.

关键词： Convolutional neural networks

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Unsupervised Automatic Defect Inspection based on Image Matching and Local One-class Classification

Unsupervised Automatic Defect Inspection based on Image Matc...

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2023 IEEE/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Lv, Chengkan Zhang, Zhengtao Shen, Fei Zhang, Feng Institute of Automation Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences The School of Artificial Intelligence Beijing China CASI Vision Technology Co. Ltd. Luoyang China

ISBN: (纸本)9798350302493

In this paper, an unsupervised defect inspection method based on anomaly detection is proposed to inspect various kinds of surface defects in the field of industrial production. This method consists of two modules: (i) An image matching module is utilized to align the input image with a pre-specified template image. Specifically, all objects to be detected will be adjusted to the same position and angle. The aligned images can reduce the difficulty of the training stage, facilitating the subsequent feature extraction and anomaly localization. (ii) After the image matching procedure, an anomaly localization module is trained to learn a mapping that concentrates normal samples in feature space. In particular, each local image region is assigned a feature center by adopting a feature map as the mapping target. Therefore, the compactness of the features extracted from the same region can be improved, which is beneficial to detect potential anomalous targets. Moreover, various artificial defective images are synthesized during the training stage to further improve the discriminatory ability of the anomaly localization module. A series of experiments are conducted on MAD dataset and the industrial production line. The experimental results verify the efficiency and versatility of the proposed method. © 2023 IEEE.

关键词： Inspection

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Transfer Learning from Synthetic In-vitro Soybean Pods Dataset for In-situ Segmentation of On-branch Soybean Pods

Transfer Learning from Synthetic In-vitro Soybean Pods Datas...

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

作者： Yang, Si Zheng, Lihua Chen, Xieyuanli Zabawa, Laura Zhang, Man Wang, Minjuan China Agr Univ Coll Informat & Elect Engn Beijing Peoples R China China Agr Univ Minist Educ Key Lab Smart Agr Syst Beijing Peoples R China Univ Bonn Inst Geodesy & Geoinformat Bonn Germany

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The mature soybean plants are of complex architecture with pods frequently touching each other, posing a challenge for in-situ segmentation of on-branch soybean pods. Deep learning-based methods can achieve accurate training and strong generalization capabilities, but it demands massive labeled data, which is often a limitation, especially for agricultural applications. As lacking the labeled data to train an in-situ segmentation model for on-branch soybean pods, we propose a transfer learning from synthetic in-vitro soybean pods. First, we present a novel automated image generation method to rapidly generate a synthetic in-vitro soybean pods dataset with plenty of annotated samples. The in-vitro soybean pods samples are overlapped to simulate the frequently physically touching of on-branch soybean pods. Then, we design a two-step transfer learning. In the first step, we finetune an instance segmentation network pretrained by a source domain (MS COCO dataset) with a synthetic target domain (in-vitro soybean pods dataset). In the second step, transferring from simulation to reality is performed by finetuning on a few real-world mature soybean plant samples. The experimental results show the effectiveness of the proposed two-step transfer learning method, such that AP50 was 0.80 for the real-world mature soybean plant test dataset, which is higher than that of direct adaptation and its AP50 was 0.77. Furthermore, the visualizations of in-situ segmentation results of on-branch soybean pods show that our method performs better than other methods, especially when soybean pods overlap densely.

关键词： Deep learning Transfer learning computer vision Instance segmentation Plant phenotyping

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WSRD: A Novel Benchmark for High Resolution Image Shadow Removal

WSRD: A Novel Benchmark for High Resolution Image Shadow Rem...

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2023 IEEE/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Vasluianu, Florin-Alexandru Seizinger, Tim Timofte, Radu University of Würzburg Computer Vision Laboratory Caidas & Ifi Germany

ISBN: (纸本)9798350302493

Shadow removal is an important computer vision task, whose aim is to successfully detect the shadow affected area appearing through light occlussion, followed by a photorealistic restoration of the affected image contents, textures, and details. After decades of research, a multitude of hand-crafted restoration techniques were proposed, following different observations on shadow formation models, with scenes altered in particular conditions. However, the increased popularity of deep learning based solutions enabled a significant step forward for the shadow removal solutions, both in terms of reconstruction fidelity and perceptual properties. However, the publicly available datasets remain focused around a particularly low complexity setup, with a low variety of light occluders and affected backgrounds, and with limited representation for more complex light interactions and complex shadow patterns. In this work, we propose WSRD, a novel benchmark for high resolution image shadow removal, characterized by a large variety in terms or represented objects, backgrounds and light occluders. We study more complex interactions, combining self shadows with externally casted shadows, to further extend the study of the phenomenon, its factors and effects. To prove WSRD as a relevant benchmark, we propose DNSR, a novel shadow removal method, comparing the results on WSRD with the performance level observed on other well-established benchmarks like ISTD and ISTD+. We validate our approach comparing with existing state-of-the-art (SOTA) methods, improving both in reconstruction fidelity and perceptual properties, setting a new SOTA for the field. © 2023 IEEE.

关键词： Restoration

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MAPLE-Edge: A Runtime Latency Predictor for Edge Devices

MAPLE-Edge: A Runtime Latency Predictor for Edge Devices

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

作者： Nair, Saeejith Abbasi, Saad Wong, Alexander Shafiee, Mohammad Javad Univ Waterloo Waterloo ON Canada DarwinAI Waterloo ON Canada

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Neural Architecture Search (NAS) has enabled automatic discovery of more efficient neural network architectures, especially for mobile and embedded vision applications. Although recent research has proposed ways of quickly estimating latency on unseen hardware devices with just a few samples, little focus has been given to the challenges of estimating latency on runtimes using optimized graphs, such as TensorRT and specifically for edge devices. As devices like NVIDIA's Jetsons get more popular in embedded computing and robotics, we observe a pressing need to more accurately estimate inference latency of neural network architectures on diverse runtimes, including highly optimized ones. In this work, we propose MAPLE-Edge, an edge device-oriented extension of MAPLE, the state-of-the-art latency predictor for general purpose hardware, where we train a regression network on architecture-latency pairs in conjunction with a hardware-runtime descriptor to effectively estimate latency on a diverse pool of edge devices. Compared to MAPLE, MAPLE-Edge can describe the runtime and target device platform using a much smaller set of CPU performance counters that are widely available on all Linux kernels, while still achieving up to +49.6% accuracy gains against previous state-of-the-art baseline methods on optimized edge device runtimes, using just 10 measurements from an unseen target device. We also demonstrate that unlike MAPLE which performs best when trained on a pool of devices sharing a common runtime, MAPLE-Edge can effectively generalize across runtimes by applying a trick of normalizing performance counters by the operator latency, in the measured hardware-runtime descriptor. Lastly, we show that for runtimes exhibiting lower than desired accuracy, performance can be boosted by collecting additional samples from the target device, with an extra 90 samples translating to gains of nearly +40%.

关键词： Performance evaluation Training Runtime Linux Neural networks computer architecture Pressing

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BinaryViT: Pushing Binary vision Transformers Towards Convolutional Models

BinaryViT: Pushing Binary Vision Transformers Towards Convol...

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2023 IEEE/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Le, Phuoc-Hoan Charles Li, Xinlin Huawei Noah's Ark Lab Montreal Research Center Canada Huawei Noah's Ark Lab Canada

ISBN: (纸本)9798350302493

With the increasing popularity and the increasing size of vision transformers (ViTs), there has been an increasing interest in making them more efficient and less computationally costly for deployment on edge devices with limited computing resources. Binarization can be used to help reduce the size of ViT models and their computational cost significantly, using popcount operations when the weights and the activations are in binary. However, ViTs suffer a larger performance drop when directly applying convolutional neural network (CNN) binarization methods or existing binarization methods to binarize ViTs compared to CNNs on datasets with a large number of classes such as ImageNet-1k. With extensive analysis, we find that binary vanilla ViTs such as DeiT miss out on a lot of key architectural properties that CNNs have that allow binary CNNs to have much higher representational capability than binary vanilla ViT. Therefore, we propose BinaryViT, in which inspired by the CNN architecture, we include operations from the CNN architecture into a pure ViT architecture to enrich the representational capability of a binary ViT without introducing convolutions. These include an average pooling layer instead of a token pooling layer, a block that contains multiple average pooling branches, an affine transformation right before the addition of each main residual connection, and a pyramid structure. Experimental results on the ImageNet-1k dataset show the effectiveness of these operations that allow a fully-binary pure ViT model to be competitive with previous state-of-the-art binary (SOTA) CNN models. © 2023 IEEE.

关键词： Convolution

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