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Isolated Sign recognition from RGB Video using Pose Flow and Self-Attention

Isolated Sign Recognition from RGB Video using Pose Flow and...

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

作者： De Coster, Mathieu Van Herreweghe, Mieke Dambre, Joni Univ Ghent IMEC IDLab AIRO Technol Pk Zwijnaarde 126 Ghent Belgium Univ Ghent Blandijnberg 2 Ghent Belgium

ISBN: (纸本)9781665448994

Automatic sign language recognition lies at the intersection of natural language processing (NLP) and computer vision. The highly successful transformer architectures, based on multi-head attention, originate from the field of NLP. The Video Transformer Network (VTN) is an adaptation of this concept for tasks that require video understanding, e.g., action recognition. However, due to the limited amount of labeled data that is commonly available for training automatic sign (language) recognition, the VTN cannot reach its full potential in this domain. In this work, we reduce the impact of this data limitation by automatically pre-extracting useful information from the sign language videos. In our approach, different types of information are offered to a VTN in a multi-modal setup. It includes per-frame human pose keypoints (extracted by OpenPose) to capture the body movement and hand crops to capture the (evolution of) hand shapes. We evaluate our method on the recently released AUTSL dataset for isolated sign recognition and obtain 92.92% accuracy on the test set using only RGB data. For comparison: the VTN architecture without hand crops and pose flow achieved 82% accuracy. A qualitative inspection of our model hints at further potential of multi-modal multi-head attention in a sign language recognition context.

关键词： computer vision Analytical models Shape Assistive technology Computational modeling Gesture recognition computer architecture

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Determining Dendrometry Using Drone Scouting, Convolutional Neural Networks and Point Clouds

Determining Dendrometry Using Drone Scouting, Convolutional ...

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

作者： Jensen, Kim Krogh, Oskar Kondrup Jorgensen, Marius Willemoes Lehotsky, Daniel Andersen, Anton Bock Porqueras, Ernest Sondergaard, Jens Aksel S. Gade, Rikke Aalborg Univ Dept Elect Syst Aalborg Denmark TeeJet Technol Aabybro Denmark Aalborg Univ Sect Media Technol Aalborg Denmark

ISBN: (纸本)9781665448994

This paper presents a solution for mapping the location of trees in an orchard and estimating the dendrometric data of the trees. The combined solution consists of a mapping and navigation algorithm, which allows for autonomous data collection at an orchard with a regular rectangular layout, and data processing for tree detection and dendrometric data estimation. The data collection is done using an Intel RealSense D435i camera, which can obtain both RGB and depth data. The paper presents a comparison between the performance of point cloud processing (PCP) and convolutional neural networks (CNNs) on RGB data for tree detection and dendrometric data estimation. The YOLOv3 CNN achieved a mAP50 of 63.53% with 65.5 FPS and a mean error of 20.6 cm in height estimation. Point cloud processing achieved a precision of 76.72% with 2.1 FPS and a mean error of 20.4 cm in height estimation. In conclusion, this work shows that point cloud processing shows comparable results to convolutional neural networks for height estimation, but trades off processing time for better precision in detection.

关键词： Three-dimensional displays Navigation Shape Estimation Vegetation Data collection pattern recognition

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Appearance-based Gaze Estimation using Attention and Difference Mechanism

Appearance-based Gaze Estimation using Attention and Differe...

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

作者： Murthy, L. R. D. Biswas, Pradipta Indian Inst Sci CPDM I3D Lab Bangalore Karnataka India

ISBN: (纸本)9781665448994

Appearance-based gaze estimation problem received wide attention over the past few years. Even though model-based approaches existed earlier, availability of large datasets and novel deep learning techniques made appearance-based methods achieve superior accuracy than model-based approaches. In this paper, we proposed two novel techniques to improve gaze estimation accuracy. Our first approach, I2D-Net uses a difference layer to eliminate any common features from left and right eyes of a participant that are not pertinent to gaze estimation task. Our second approach, AGE-Net adapted the idea of attention-mechanism and assigns weights to the features extracted from eye images. I2D-Net performed on par with the existing state-of-the-art approaches while AGE-Net reported state-of-the-art accuracy of 4.09 degrees and 7.44 degrees error on MPIIGaze and RT-Gene datasets respectively. We performed ablation studies to understand the effectiveness of the proposed approaches followed by analysis of gaze error distribution with respect to various factors of MPIIGaze dataset.

关键词： Deep learning computer vision Adaptation models conferences Estimation Lighting Feature extraction

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Graph-based Person Signature for Person Re-Identifications

Graph-based Person Signature for Person Re-Identifications

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

作者： Nguyen, Binh X. Nguyen, Binh D. Do, Tuong Tjiputra, Erman Tran, Quang D. Nguyen, Anh AIOZ Singapore Singapore Imperial Coll London London England

ISBN: (纸本)9781665448994

The task of person re-identification (ReID) is to match images of the same person over multiple non-overlapping camera views. Due to the variations in visual factors, previous works have investigated how the person identity, body parts, and attributes benefit the person ReID problem. However, the correlations between attributes, body parts, and within each attribute are not fully utilized. In this paper, we propose a new method to effectively aggregate detailed person descriptions (attributes labels) and visual features (body parts and global features) into a graph, namely Graph-based Person Signature, and utilize Graph Convolutional Networks to learn the topological structure of the visual signature of a person. The graph is integrated into a multi-branch multi-task framework for person re-identification. The extensive experiments are conducted to demonstrate the effectiveness of our proposed approach on two large-scale datasets, including Market-1501 and DukeMTMC-ReID. Our approach achieves competitive results among the state of the art and outperforms other attribute-based or mask-guided methods. Source available at https://***/aioz-ai/cvprW21_GPS.

关键词： Visualization computer vision Correlation conferences Aggregates Benchmark testing Cameras

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CASSOD-Net: Cascaded and Separable Structures of Dilated Convolution for Embedded vision Systems and Applications

CASSOD-Net: Cascaded and Separable Structures of Dilated Con...

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

作者： Chen, Tse-Wei Wang, Deyu Tao, Wei Wen, Dongchao Yin, Lingxiao Ito, Tadayuki Osa, Kinya Kato, Masami Canon Inc Device Technol Dev Headquarters Tokyo Japan Canon Innovat Solut Beijing Co Ltd Beijing Peoples R China

ISBN: (纸本)9781665448994

The field of view (FOV) of convolutional neural networks is highly related to the accuracy of inference. Dilated convolutions are known as an effective solution to the problems which require large FOVs. However, for general-purpose hardware or dedicated hardware, it usually takes extra time to handle dilated convolutions compared with standard convolutions. In this paper, we propose a network module, Cascaded and Separable Structure of Dilated (CASSOD) Convolution, and a special hardware system to handle the CASSOD networks efficiently. A CASSOD-Net includes multiple cascaded 2 x 2 dilated filters, which can be used to replace the traditional 3x3 dilated filters without decreasing the accuracy of inference. Two example applications, face detection and image segmentation, are tested with dilated convolutions and the proposed CASSOD modules. The new network for face detection achieves higher accuracy than the previous work with only 47% of filter weights in the dilated convolution layers of the context module. Moreover, the proposed hardware system can accelerate the computations of dilated convolutions, and it is 2.78 times faster than traditional hardware systems when the filter size is 3 x 3.

关键词： Image segmentation Machine vision conferences Hardware pattern recognition Face detection Convolutional neural networks

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Evaluating Deep Neural Networks Trained on Clinical Images in Dermatology with the Fitzpatrick 17k Dataset

Evaluating Deep Neural Networks Trained on Clinical Images i...

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

作者： Groh, Matthew Harris, Caleb Soenksen, Luis Lau, Felix Han, Rachel Kim, Aerin Koochek, Arash Badri, Omar MIT Media Lab Cambridge MA 02139 USA Harvard Univ MIT Cambridge MA 02139 USA Scale San Francisco CA USA Banner Hlth Phoenix AZ USA Northeast Dermatol Associates Beverly MA USA

ISBN: (纸本)9781665448994

How does the accuracy of deep neural network models trained to classify clinical images of skin conditions vary across skin color? While recent studies demonstrate computer vision models can serve as a useful decision support tool in healthcare and provide dermatologist-level classification on a number of specific tasks, darker skin is under-represented in the data. Most publicly available data sets do not include Fitzpatrick skin type labels. We annotate 16,577 clinical images sourced from two dermatology atlases with Fitzpatrick skin type labels and open-source these annotations. Based on these labels, we find that there are significantly more images of light skin types than dark skin types in this dataset. We train a deep neural network model to classify 114 skin conditions and find that the model is most accurate on skin types similar to those it was trained on. In addition, we evaluate how an algorithmic approach to identifying skin tones, individual typology angle, compares with Fitzpatrick skin type labels annotated by a team of human labelers.

关键词： Deep learning Training computer vision Image segmentation Dermatology Neural networks Training data

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Rethinking Ensemble-Distillation for Semantic Segmentation Based Unsupervised Domain Adaption

Rethinking Ensemble-Distillation for Semantic Segmentation B...

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

作者： Chao, Chen-Hao Cheng, Bo-Wun Lee, Chun-Yi Natl Tsing Hua Univ Dept Comp Sci Elsa Lab Hsinchu Taiwan

ISBN: (纸本)9781665448994

Recent researches on unsupervised domain adaptation (UDA) have demonstrated that end-to-end ensemble learning frameworks serve as a compelling option for UDA tasks. Nevertheless, these end-to-end ensemble learning methods often lack flexibility as any modification to the ensemble requires retraining of their frameworks. To address this problem, we propose a flexible ensemble-distillation framework for performing semantic segmentation based UDA, allowing any arbitrary composition of the members in the ensemble while still maintaining its superior performance. To achieve such flexibility, our framework is designed to be robust against the output inconsistency and the performance variation of the members within the ensemble. To examine the effectiveness and the robustness of our method, we perform an extensive set of experiments on both GTA5 -> Cityscapes and SYNTHIA -> Cityscapes benchmarks to quantitatively inspect the improvements achievable by our method. We further provide detailed analyses to validate that our design choices are practical and beneficial. The experimental evidence validates that the proposed method indeed offer superior performance, robustness and flexibility in semantic segmentation based UDA tasks against contemporary baseline methods.

关键词： Learning systems computer vision conferences Semantics Benchmark testing Robustness pattern recognition

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Training Deep Generative Models in Highly Incomplete Data Scenarios with Prior Regularization

Training Deep Generative Models in Highly Incomplete Data Sc...

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

作者： Bernal, Edgar A. Univ Rochester Rochester Data Sci Consortium Rochester NY 14627 USA

ISBN: (纸本)9781665448994

Deep generative frameworks including GANs and normalizing flow models have proven successful at filling in missing values in partially observed data samples by effectively learning -either explicitly or implicitly- complex, high-dimensional statistical distributions. In tasks where the data available for learning is only partially observed, however, their performance decays monotonically as a function of the data missingness rate. In high missing data rate regimes (e.g., 60% and above), it has been observed that state-of-the-art models tend to break down and produce unrealistic and/or semantically inaccurate data. We propose a novel framework to facilitate the learning of data distributions in high paucity scenarios that is inspired by traditional formulations of solutions to ill-posed problems. The proposed framework naturally stems from posing the process of learning from incomplete data as a joint optimization task of the parameters of the model being learned and the missing data values. The method involves enforcing a prior regularization term that seamlessly integrates with objectives used to train explicit and tractable deep generative frameworks such as deep normalizing flow models. We demonstrate via extensive experimental validation that the proposed framework outperforms competing techniques, particularly as the rate of data paucity approaches unity.

关键词： Training computer vision Computational modeling conferences Statistical distributions Data models Filling

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Width transfer: on the (in)variance of width optimization

Width transfer: on the (in)variance of width optimization

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

作者： Chin, Ting-Wu Marculescu, Diana Morcos, Ari S. Carnegie Mellon Univ Pittsburgh PA 15213 USA Univ Texas Austin Austin TX 78712 USA Facebook AI Res Menlo Pk CA USA

ISBN: (纸本)9781665448994

Optimizing the channel counts for different layers of a CNN has shown great promise in improving the efficiency of CNNs at test-time. However, these methods often introduce large computational overhead (e.g., an additional 2x FLOPs of standard training). Minimizing this overhead could therefore significantly speed up training. In this work, we propose width transfer, a technique that harnesses the assumptions that the optimized widths (or channel counts) are regular across sizes and depths. We show that width transfer works well across various width optimization algorithms and networks. Specifically, we can achieve up to 320x reduction in width optimization overhead without compromising the top-1 accuracy on ImageNet, making the additional cost of width optimization negligible relative to initial training. Our findings not only suggest an efficient way to conduct width optimization, but also highlight that the widths that lead to better accuracy are invariant to various aspects of network architectures and training data.

关键词： Training computer vision Design methodology conferences Training data Optimization methods Network architecture

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Differentiable Event Stream Simulator for Non-Rigid 3D Tracking

Differentiable Event Stream Simulator for Non-Rigid 3D Track...

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

作者： Nehvi, Jalees Golyanik, Vladislav Mueller, Franziska Seidel, Hans-Peter Elgharib, Mohamed Theobalt, Christian Saarland Univ Saarbrucken Germany SIC MPI Informat Saarbrucken Germany Google Inc Mountain View CA USA

ISBN: (纸本)9781665448994

This paper introduces the first differentiable simulator of event streams, i.e., streams of asynchronous brightness change signals recorded by event cameras. Our differentiable simulator enables non-rigid 3D tracking of deformable objects (such as human hands, isometric surfaces and general watertight meshes) from event streams by leveraging an analysis-by-synthesis principle. So far, event-based tracking and reconstruction of non-rigid objects in 3D, like hands and body, has been either tackled using explicit event trajectories or large-scale datasets. In contrast, our method does not require any such processing or data, and can be readily applied to incoming event streams. We show the effectiveness of our approach for various types of non-rigid objects and compare to existing methods for non-rigid 3D tracking. In our experiments, the proposed energy-based formulations outperform competing RGB-based methods in terms of 3D errors. The source code and the new data are publicly available(1).

关键词： Training Surface reconstruction Three-dimensional displays conferences Supervised learning Gesture recognition Trajectory

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