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The emotionally intelligent robot: Improving socially-aware human prediction in crowded environments 32

The emotionally intelligent robot: Improving socially-aware ...

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32nd ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2019

作者： Bera, Aniket Randhavane, Tanmay Manocha, Dinesh Department of Computer Science University of North Carolina Chapel Hill United States Department of Computer Science University of Maryland at College Park United States

ISBN: (纸本)9781728125060

— We present a real-time algorithm for emotion-aware navigation of a robot among pedestrians. Our approach estimates time-varying emotional behaviors of pedestrians from their faces and trajectories using a combination of Bayesian-inference, CNN-based learning, and the PAD (Pleasure-Arousal-Dominance) model from psychology. These PAD characteristics are used for long-term path prediction and generating proxemic constraints for each pedestrian. We use a multi-channel model to classify pedestrian characteristics into four emotion categories (happy, sad, angry, neutral). In our validation results, we observe an emotion detection accuracy of 85.33%. We formulate emotion-based proxemic constraints to perform socially-aware robot navigation in low- to medium-density environments. We demonstrate the benefits of our algorithm in simulated environments with tens of pedestrians as well as in a real-world setting with Pepper, a social humanoid robot. © 2019 ieee computer Society. All rights reserved.

关键词： Anthropomorphic robots

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OC-FakeDect: Classifying Deepfakes Using One-class Variational Autoencoder

OC-FakeDect: Classifying Deepfakes Using One-class Variation...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Hasam Khalid Simon S. Woo Computer Science and Engineering Department Sungkyunkwan University South Korea

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

An image forgery method called Deepfakes can cause security and privacy issues by changing the identity of a person in a photo through the replacement of his/her face with a computer-generated image or another person's face. Therefore, a new challenge of detecting Deepfakes arises to protect individuals from potential misuses. Many researchers have proposed various binary-classification based detection approaches to detect deepfakes. However, binary-classification based methods generally require a large amount of both real and fake face images for training, and it is challenging to collect sufficient fake images data in advance. Besides, when new deepfakes generation methods are introduced, little deepfakes data will be available, and the detection performance may be mediocre. To overcome these data scarcity limitations, we formulate deepfakes detection as a one-class anomaly detection problem. We propose OC-FakeDect, which uses a one-class Variational Autoencoder (VAE) to train only on real face images and detects non-real images such as deepfakes by treating them as anomalies. Our preliminary result shows that our one class-based approach can be promising when detecting Deepfakes, achieving a 97.5% accuracy on the NeuralTextures data of the well-known FaceForensics++ benchmark dataset without using any fake images for the training process.

关键词： Face Training Image reconstruction Forensics Streaming media Anomaly detection Benchmark testing

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A topological encoding convolutional neural network for segmentation of 3D multiphoton images of brain vasculature using persistent homology

A topological encoding convolutional neural network for segm...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Mohammad Haft-Javaherian Martin Villiger Chris B. Schaffer Nozomi Nishimura Polina Golland Brett E. Bouma Wellman Center for Photomedicine Massachusetts General Hospital Harvard Medical School Boston MA USA Meinig School of Biomedical Engineering Cornell University Ithaca NY USA Computer Science and Artificial Intelligence Laboratory (CSAIL) Massachusetts Institute of Technology Cambridge MA USA

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

The clinical evidence suggests that cognitive disorders are associated with vasculature dysfunction and decreased blood flow in the brain. Hence, a functional understanding of the linkage between brain functionality and the vascular network is essential. However, methods to systematically and quantitatively describe and compare structures as complex as brain blood vessels are lacking. 3D imaging modalities such as multiphoton microscopy enables researchers to capture the network of brain vasculature with high spatial resolutions. Nonetheless, image processing and inference are some of the bottlenecks for biomedical research involving imaging, and any advancement in this area impacts many research groups. Here, we propose a topological encoding convolutional neural network based on persistent homology to segment 3D multiphoton images of brain vasculature. We demonstrate that our model outperforms state-of-the-art models in terms of the Dice coefficient and it is comparable in terms of other metrics such as sensitivity. Additionally, the topological characteristics of our model's segmentation results mimic manual ground truth. Our code and model are open source at https://***/mhaft/DeepVess.

关键词： Image segmentation Three-dimensional displays Biological system modeling computer vision computer architecture Biomedical imaging

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TherISuRNet - A Computationally Efficient Thermal Image Super-Resolution Network

TherISuRNet - A Computationally Efficient Thermal Image Supe...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Vishal Chudasama Heena Patel Kalpesh Prajapati Kishor Upla Raghavendra Ramachandra Kiran Raja Christoph Busch Sardar Vallabhbhai National Institute of Technology (SVNIT) Surat India Norwegian University of Science and Technology (NTNU) Gjøvik Norway

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Human perception is limited to perceive the objects beyond the range of visible wavelengths in the Electromagnetic (EM) spectrum. This prevents them to recognize objects in different conditions such as poor illumination or severe weather (e.g., under fog or smoke). The technological advancement in thermographic imaging enables the visualization of objects beyond visible range which enables it's use in many applications such as military, medical, agriculture, etc. However, due to the hardware constraints, the thermal cameras are limited with poor spatial resolution when compared to similar visible range RGB cameras. In this paper, we propose a Super-Resolution (SR) of thermal images using a deep neural network architecture which we refer to as TherISuRNet. We use progressive upscaling strategy with asymmetrical residual learning in the network which is computationally efficient for different upscaling factors such as ×2, ×3 and ×4. The proposed architecture consists of different modules for low and high-frequency feature extraction along with upsampling blocks. The effectiveness of the proposed architecture in TherISuRNet is verified by evaluating it with different datasets. The obtained results indicate superior results as compared to other state-of-the-art SR methods.

关键词： Cameras Spatial resolution Feature extraction Training computer architecture

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When Person Re-identification Meets Changing Clothes

When Person Re-identification Meets Changing Clothes

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Fangbin Wan Yang Wu Xuelin Qian Yixiong Chen Yanwei Fu School of Data Science Fudan University Kyoto University School of Computer Science Fudan University

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Person re-identification (ReID) is now an active research topic for AI-based video surveillance applications such as specific person search, but the practical issue that the target person(s) may change clothes (clothes inconsistency problem) has been overlooked for long. For the first time, this paper systematically studies this problem. We first overcome the difficulty of lack of suitable dataset, by collecting a small yet representative real dataset for testing whilst building a large realistic synthetic dataset for training and deeper studies. Facilitated by our new datasets, we are able to conduct various interesting new experiments for studying the influence of clothes inconsistency. We find that changing clothes makes ReID a much harder problem in the sense of bringing difficulties to learning effective representations and also challenges the generalization ability of previous ReID models to identify persons with unseen (new) clothes. Representative existing ReID models are adopted to show informative results on such a challenging setting, and we also provide some preliminary efforts on improving the robustness of existing models on handling the clothes inconsistency issue in the data. We believe that this study can be inspiring and helpful for encouraging more researches in this direction. The dataset is available on the project website: https://***/***.

关键词： Cameras Benchmark testing Lighting Buildings Games Data models Biological system modeling

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AMC-Loss: Angular Margin Contrastive Loss for Improved Explainability in Image Classification

AMC-Loss: Angular Margin Contrastive Loss for Improved Expla...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Hongjun Choi Anirudh Som Pavan Turaga Geometric Media Lab School of Arts Media and Engineering Arizona State University

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Deep-learning architectures for classification problems involve the cross-entropy loss sometimes assisted with auxiliary loss functions like center loss, contrastive loss and triplet loss. These auxiliary loss functions facilitate better discrimination between the different classes of interest. However, recent studies hint at the fact that these loss functions do not take into account the intrinsic angular distribution exhibited by the low-level and high-level feature representations. This results in less compactness between samples from the same class and unclear boundary separations between data clusters of different classes. In this paper, we address this issue by proposing the use of geometric constraints, rooted in Riemannian geometry. Specifically, we propose Angular Margin Contrastive Loss (AMC-Loss), a new loss function to be used along with the traditional cross-entropy loss. The AMC-Loss employs the discriminative angular distance metric that is equivalent to geodesic distance on a hypersphere manifold such that it can serve a clear geometric interpretation. We demonstrate the effectiveness of AMC-Loss by providing quantitative and qualitative results. We find that although the proposed geometrically constrained loss-function improves quantitative results modestly, it has a qualitatively surprisingly beneficial effect on increasing the interpretability of deep-net decisions as seen by the visual explanations generated by techniques such as the Grad-CAM. Our code is available at https://***/hchoi71/AMC-Loss.

关键词： Training Task analysis Feature extraction Euclidean distance Airplanes computer vision Media

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Ternary MobileNets via Per-Layer Hybrid Filter Banks

Ternary MobileNets via Per-Layer Hybrid Filter Banks

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Dibakar Gope Jesse Beu Urmish Thakker Matthew Mattina Arm ML Research Lab

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

MobileNets family of computer vision neural networks have fueled tremendous progress in the design and organization of resource-efficient architectures in recent years. New applications with stringent real-time requirements on highly constrained devices require further compression of MobileNets-like compute-efficient networks. Model quantization is a widely used technique to compress and accelerate neural network inference and prior works have quantized MobileNets to 4 - 6 bits, albeit with a modest to significant drop in accuracy. While quantization to sub-byte values (i.e. precision ≤ 8 bits) has been valuable, even further quantization of MobileNets to binary or ternary values is necessary to realize significant energy savings and possibly runtime speedups on specialized hardware, such as ASICs and FPGAs. Under the key observation that convolutional filters at each layer of a deep neural network may respond differently to ternary quantization, we propose a novel quantization method that generates per-layer hybrid filter banks consisting of full-precision and ternary weight filters for MobileNets. Using this proposed quantization method, we quantize a substantial portion of weight filters of MobileNets to ternary values resulting in a 27.98% savings in energy, and a 51.07% reduction in the model size, while achieving comparable accuracy and no degradation in throughput on specialized hardware in comparison to the baseline full-precision MobileNets. Finally, we demonstrate the generalizability and effectiveness of hybrid filter banks to other neural network architectures.

关键词： Convolution Quantization (signal) computer architecture Neural networks Throughput Hardware Computational modeling

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Systematic Evaluation of Backdoor Data Poisoning Attacks on Image Classifiers

Systematic Evaluation of Backdoor Data Poisoning Attacks on ...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Loc Truong Chace Jones Brian Hutchinson Andrew August Brenda Praggastis Robert Jasper Nicole Nichols Aaron Tuor Western Washington University Pacific Northwest National Laboratory

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Backdoor data poisoning attacks have recently been demonstrated in computer vision research as a potential safety risk for machine learning (ML) systems. Traditional data poisoning attacks manipulate training data to induce unreliability of an ML model, whereas backdoor data poisoning attacks maintain system performance unless the ML model is presented with an input containing an embedded "trigger " that provides a predetermined response advantageous to the adversary. Our work builds upon prior backdoor data-poisoning research for ML image classifiers and systematically assesses different experimental conditions including types of trigger patterns, persistence of trigger patterns during retraining, poisoning strategies, architectures (ResNet-50, NasNet, NasNet-Mobile), datasets (Flowers, CIFAR-10), and potential defensive regularization techniques (Contrastive Loss, Logit Squeezing, Manifold Mixup, Soft-Nearest-Neighbors Loss). Experiments yield four key findings. First, the success rate of backdoor poisoning attacks varies widely, depending on several factors, including model architecture, trigger pattern and regularization technique. Second, we find that poisoned models are hard to detect through performance inspection alone. Third, regularization typically reduces backdoor success rate, although it can have no effect or even slightly increase it, depending on the form of regularization. Finally, backdoors inserted through data poisoning can be rendered ineffective after just a few epochs of additional training on a small set of clean data without affecting the model's performance.

关键词： Data models Training Computational modeling Machine learning Training data computer vision Safety

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CascadeTabNet: An approach for end to end table detection and structure recognition from image-based documents

CascadeTabNet: An approach for end to end table detection an...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Devashish Prasad Ayan Gadpal Kshitij Kapadni Manish Visave Kavita Sultanpure Pune Institute of Computer Technology India

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

An automatic table recognition method for interpretation of tabular data in document images majorly involves solving two problems of table detection and table structure recognition. The prior work involved solving both problems independently using two separate approaches. More recent works signify the use of deep learning-based solutions while also attempting to design an end to end solution. In this paper, we present an improved deep learning-based end to end approach for solving both problems of table detection and structure recognition using a single Convolution Neural Network (CNN) model. We propose CascadeTabNet: a Cascade mask Region-based CNN High-Resolution Network (Cascade mask R-CNN HRNet) based model that detects the regions of tables and recognizes the structural body cells from the detected tables at the same time. We evaluate our results on ICDAR 2013, ICDAR 2019 and Table-Bank public datasets. We achieved 3rd rank in ICDAR 2019 post-competition results for table detection while attaining the best accuracy results for the ICDAR 2013 and Table-Bank dataset. We also attain the highest accuracy results on the ICDAR 2019 table structure recognition dataset. Additionally, we demonstrate effective transfer learning and image augmentation techniques that enable CNNs to achieve very accurate table detection results. Code and dataset has been made available at: https://***/DevashishPrasad/CascadeTabNet.

关键词： Task analysis Image segmentation Semantics Head Predictive models Analytical models Image recognition

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Robust image colorization using self attention based progressive generative adversarial network

Robust image colorization using self attention based progres...

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32nd ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2019

作者： Sharma, Manoj Makwana, Megh Upadhyay, Avinash Singh, Ajay Pratap Badhwar, Anuj Trivedi, Akkshita Saini, Anil Chaudhury, Santanu CSIR-CEERI Pilani India CCS Computer Pvt Ltd India A and N Telecom Circle BSNL India DTU Delhi India IIT Jodhpur India

ISBN: (纸本)9781728125060

Automatic image colorization is a very interesting computer graphics problem wherein an input grayscale image is transformed into its RGB domain. However, it is an ill-posed problem as there can be multiple RGB outcomes for a particular grayscale pixel. The problem further complicates if noise is present in the grayscale image. In this paper, we propose a Robust Image Colorization using Self-attention based Progressive Generative Adversarial Network (RIC-SPGAN) which consists of residual encoder-decoder (RED) network and a Self-attention based progressive Generative network (SP-GAN) in a cascaded form to perform the denoising and colorization of the image. We have used self-attention based progressive network to model the long-range dependencies and gradually enhanced the resolution of the colorized image for faster, stable and variation rich features for generation of the image. We also presented the stabilization technique of the presented generative model. Our model has shown exceptional perceptual results on noisy and normal grayscale images. We have trained our model on ILSVRC2012. The visual results on images of DIV2K with and without noise has been presented in the paper along with the failure cases of the model. © 2019 ieee.

关键词： Image enhancement

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