pwc-archive/papers-with-abstracts huggingface dataset:
Towards Efficient Training for Neural Network Quantization
Authors: Qing Jin, Linjie Yang, Zhenyu Liao
Date: 2019-12-21 00:00:00
Tasks: Quantization

Quantization reduces computation costs of neural networks but suffers from performance degeneration. Is this accuracy drop due to the reduced capacity, or inefficient training during the quantization procedure? After looking into the gradient propagation process of neural networks by viewing the weights and intermediate activations as random variables, we discover two critical rules for efficient training. Recent quantization approaches violates the two rules and results in degenerated convergence. To deal with this problem, we propose a simple yet effective technique, named scale-adjusted training (SAT), to comply with the discovered rules and facilitates efficient training. We also analyze the quantization error introduced in calculating the gradient in the popular parameterized clipping activation (PACT) technique. Through SAT together with gradient-calibrated PACT, quantized models obtain comparable or even better performance than their full-precision counterparts, achieving state-of-the-art accuracy with consistent improvement over previous quantization methods on a wide spectrum of models including MobileNet-V1/V2 and PreResNet-50.
Exploiting Style and Attention in Real-World Super-Resolution
Authors: Xin Ma, Yi Li, Huaibo Huang, Mandi Luo, Ran He
Date: 2019-12-21 00:00:00
Tasks: Image Super-Resolution, Mutual Information Estimation, Super-Resolution

Real-world image super-resolution (SR) is a challenging image translation problem. Low-resolution (LR) images are often generated by various unknown transformations rather than by applying simple bilinear down-sampling on high-resolution (HR) images. To address this issue, this paper proposes a novel pipeline which exploits style and attention mechanism in real-world SR. Our pipeline consists of a style Variational Autoencoder (styleVAE) and a SR network incorporated with attention mechanism. To get real-world-like low-quality images paired with the HR images, we design the styleVAE to transfer the complex nuisance factors in real-world LR images to the generated LR images. We also use mutual information estimation (MI) to get better style information. For our SR network, we firstly propose a global attention residual block to learn long-range dependencies in images. Then another local attention residual block is proposed to enforce the attention of SR network moving to local areas of images in which texture detail will be filled. It is worth noticing that styleVAE can be presented in a plug-and-play manner and thus can help to improve the generalization and robustness of our SR method as well as other SR methods. Extensive experiments demonstrate that our method surpasses the state-of-the-art work, both quantitatively and qualitatively.
A Survey on Deep Learning-based Architectures for Semantic Segmentation on 2D images
Authors: Irem Ulku, Erdem Akagunduz
Date: 2019-12-21 00:00:00
Tasks: 2D Semantic Segmentation, Deep Learning, Segmentation, Semantic Segmentation, Survey

Semantic segmentation is the pixel-wise labelling of an image. Since the problem is defined at the pixel level, determining image class labels only is not acceptable, but localising them at the original image pixel resolution is necessary. Boosted by the extraordinary ability of convolutional neural networks (CNN) in creating semantic, high level and hierarchical image features; several deep learning-based 2D semantic segmentation approaches have been proposed within the last decade. In this survey, we mainly focus on the recent scientific developments in semantic segmentation, specifically on deep learning-based methods using 2D images. We started with an analysis of the public image sets and leaderboards for 2D semantic segmentation, with an overview of the techniques employed in performance evaluation. In examining the evolution of the field, we chronologically categorised the approaches into three main periods, namely pre-and early deep learning era, the fully convolutional era, and the post-FCN era. We technically analysed the solutions put forward in terms of solving the fundamental problems of the field, such as fine-grained localisation and scale invariance. Before drawing our conclusions, we present a table of methods from all mentioned eras, with a summary of each approach that explains their contribution to the field. We conclude the survey by discussing the current challenges of the field and to what extent they have been solved.
Seek and You Will Find: A New Optimized Framework for Efficient Detection of Pedestrian
Authors: Sudip Das, Partha Sarathi Mukherjee, Ujjwal Bhattacharya
Date: 2019-12-21 00:00:00
Tasks: object-detection, Object Detection, Pedestrian Detection

Studies of object detection and localization, particularly pedestrian detection have received considerable attention in recent times due to its several prospective applications such as surveillance, driving assistance, autonomous cars, etc. Also, a significant trend of latest research studies in related problem areas is the use of sophisticated Deep Learning based approaches to improve the benchmark performance on various standard datasets. A trade-off between the speed (number of video frames processed per second) and detection accuracy has often been reported in the existing literature. In this article, we present a new but simple deep learning based strategy for pedestrian detection that improves this trade-off. Since training of similar models using publicly available sample datasets failed to improve the detection performance to some significant extent, particularly for the instances of pedestrians of smaller sizes, we have developed a new sample dataset consisting of more than 80K annotated pedestrian figures in videos recorded under varying traffic conditions. Performance of the proposed model on the test samples of the new dataset and two other existing datasets, namely Caltech Pedestrian Dataset (CPD) and CityPerson Dataset (CD) have been obtained. Our proposed system shows nearly 16\% improvement over the existing state-of-the-art result.
A sparse resultant based method for efficient minimal solvers
Authors: Snehal Bhayani, Zuzana Kukelova, Janne Heikkilä
Date: 2019-12-21 00:00:00
Proceeding: CVPR 2020 6

Many computer vision applications require robust and efficient estimation of camera geometry. The robust estimation is usually based on solving camera geometry problems from a minimal number of input data measurements, i.e. solving minimal problems in a RANSAC framework. Minimal problems often result in complex systems of polynomial equations. Many state-of-the-art efficient polynomial solvers to these problems are based on Gr\"obner bases and the action-matrix method that has been automatized and highly optimized in recent years. In this paper we study an alternative algebraic method for solving systems of polynomial equations, i.e., the sparse resultant-based method and propose a novel approach to convert the resultant constraint to an eigenvalue problem. This technique can significantly improve the efficiency and stability of existing resultant-based solvers. We applied our new resultant-based method to a large variety of computer vision problems and show that for most of the considered problems, the new method leads to solvers that are the same size as the the best available Gr\"obner basis solvers and of similar accuracy. For some problems the new sparse-resultant based method leads to even smaller and more stable solvers than the state-of-the-art Gr\"obner basis solvers. Our new method can be fully automatized and incorporated into existing tools for automatic generation of efficient polynomial solvers and as such it represents a competitive alternative to popular Gr\"obner basis methods for minimal problems in computer vision.
\emph{cm}SalGAN: RGB-D Salient Object Detection with Cross-View Generative Adversarial Networks
Authors: Bo Jiang, Zitai Zhou, Xiao Wang, Jin Tang, Bin Luo
Date: 2019-12-21 00:00:00
Tasks: Edge Detection, Generative Adversarial Network, Object, object-detection, Object Detection, RGB-D Salient Object Detection, RGB Salient Object Detection, Saliency Detection, Salient Object Detection

Image salient object detection (SOD) is an active research topic in computer vision and multimedia area. Fusing complementary information of RGB and depth has been demonstrated to be effective for image salient object detection which is known as RGB-D salient object detection problem. The main challenge for RGB-D salient object detection is how to exploit the salient cues of both intra-modality (RGB, depth) and cross-modality simultaneously which is known as cross-modality detection problem. In this paper, we tackle this challenge by designing a novel cross-modality Saliency Generative Adversarial Network (\emph{cm}SalGAN). \emph{cm}SalGAN aims to learn an optimal view-invariant and consistent pixel-level representation for RGB and depth images via a novel adversarial learning framework, which thus incorporates both information of intra-view and correlation information of cross-view images simultaneously for RGB-D saliency detection problem. To further improve the detection results, the attention mechanism and edge detection module are also incorporated into \emph{cm}SalGAN. The entire \emph{cm}SalGAN can be trained in an end-to-end manner by using the standard deep neural network framework. Experimental results show that \emph{cm}SalGAN achieves the new state-of-the-art RGB-D saliency detection performance on several benchmark datasets.
Candidate Fusion: Integrating Language Modelling into a Sequence-to-Sequence Handwritten Word Recognition Architecture
Authors: Lei Kang, Pau Riba, Mauricio Villegas, Alicia Fornés, Marçal Rusiñol
Date: 2019-12-21 00:00:00
Tasks: Language Modeling, Language Modelling

Sequence-to-sequence models have recently become very popular for tackling handwritten word recognition problems. However, how to effectively integrate an external language model into such recognizer is still a challenging problem. The main challenge faced when training a language model is to deal with the language model corpus which is usually different to the one used for training the handwritten word recognition system. Thus, the bias between both word corpora leads to incorrectness on the transcriptions, providing similar or even worse performances on the recognition task. In this work, we introduce Candidate Fusion, a novel way to integrate an external language model to a sequence-to-sequence architecture. Moreover, it provides suggestions from an external language knowledge, as a new input to the sequence-to-sequence recognizer. Hence, Candidate Fusion provides two improvements. On the one hand, the sequence-to-sequence recognizer has the flexibility not only to combine the information from itself and the language model, but also to choose the importance of the information provided by the language model. On the other hand, the external language model has the ability to adapt itself to the training corpus and even learn the most commonly errors produced from the recognizer. Finally, by conducting comprehensive experiments, the Candidate Fusion proves to outperform the state-of-the-art language models for handwritten word recognition tasks.
Do Facial Expressions Predict Ad Sharing? A Large-Scale Observational Study
Authors: Daniel McDuff, Jonah Berger
Date: 2019-12-21 00:00:00

People often share news and information with their social connections, but why do some advertisements get shared more than others? A large-scale test examines whether facial responses predict sharing. Facial expressions play a key role in emotional expression. Using scalable automated facial coding algorithms, we quantify the facial expressions of thousands of individuals in response to hundreds of advertisements. Results suggest that not all emotions expressed during viewing increase sharing, and that the relationship between emotion and transmission is more complex than mere valence alone. Facial actions linked to positive emotions (i.e., smiles) were associated with increased sharing. But while some actions associated with negative emotion (e.g., lip depressor, associated with sadness) were linked to decreased sharing, others (i.e., nose wrinkles, associated with disgust) were linked to increased sharing. The ability to quickly collect facial responses at scale in peoples' natural environment has important implications for marketers and opens up a range of avenues for further research.
Multimodal Prediction based on Graph Representations
Authors: Icaro Cavalcante Dourado, Salvatore Tabbone, Ricardo da Silva Torres
Date: 2019-12-21 00:00:00
Tasks: image-classification, Image Classification, Prediction, Retrieval

This paper proposes a learning model, based on rank-fusion graphs, for general applicability in multimodal prediction tasks, such as multimodal regression and image classification. Rank-fusion graphs encode information from multiple descriptors and retrieval models, thus being able to capture underlying relationships between modalities, samples, and the collection itself. The solution is based on the encoding of multiple ranks for a query (or test sample), defined according to different criteria, into a graph. Later, we project the generated graph into an induced vector space, creating fusion vectors, targeting broader generality and efficiency. A fusion vector estimator is then built to infer whether a multimodal input object refers to a class or not. Our method is capable of promoting a fusion model better than early-fusion and late-fusion alternatives. Performed experiments in the context of multiple multimodal and visual datasets, as well as several descriptors and retrieval models, demonstrate that our learning model is highly effective for different prediction scenarios involving visual, textual, and multimodal features, yielding better effectiveness than state-of-the-art methods.
Depth Completion via Deep Basis Fitting
Authors: Chao Qu, Ty Nguyen, Camillo J. Taylor
Date: 2019-12-21 00:00:00
Tasks: Depth Completion

In this paper we consider the task of image-guided depth completion where our system must infer the depth at every pixel of an input image based on the image content and a sparse set of depth measurements. We propose a novel approach that builds upon the strengths of modern deep learning techniques and classical optimization algorithms and significantly improves performance. The proposed method replaces the final $1\times 1$ convolutional layer employed in most depth completion networks with a least squares fitting module which computes weights by fitting the implicit depth bases to the given sparse depth measurements. In addition, we show how our proposed method can be naturally extended to a multi-scale formulation for improved self-supervised training. We demonstrate through extensive experiments on various datasets that our approach achieves consistent improvements over state-of-the-art baseline methods with small computational overhead.
Learning to Impute: A General Framework for Semi-supervised Learning
Authors: Wei-Hong Li, Chuan-Sheng Foo, Hakan Bilen
Date: 2019-12-22 00:00:00
Tasks: Classification, Facial Landmark Detection, General Classification, image-classification, Image Classification

Recent semi-supervised learning methods have shown to achieve comparable results to their supervised counterparts while using only a small portion of labels in image classification tasks thanks to their regularization strategies. In this paper, we take a more direct approach for semi-supervised learning and propose learning to impute the labels of unlabeled samples such that a network achieves better generalization when it is trained on these labels. We pose the problem in a learning-to-learn formulation which can easily be incorporated to the state-of-the-art semi-supervised techniques and boost their performance especially when the labels are limited. We demonstrate that our method is applicable to both classification and regression problems including image classification and facial landmark detection tasks.
Robust Pose Invariant Shape and Texture based Hand Recognition
Authors: F. Sohel, A. El-Sallam, M. Bennamoun
Date: 2019-12-22 00:00:00

This paper presents a novel personal identification and verification system using information extracted from the hand shape and texture. The system has two major constituent modules: a fully automatic and robust peg free segmentation and pose normalisation module, and a recognition module. In the first module, the hand is segmented from its background using a thresholding technique based on Otsu`s method combined with a skin colour detector. A set of fully automatic algorithms are then proposed to segment the palm and fingers. In these algorithms, the skeleton and the contour of the hand and fingers are estimated and used to determine the global pose of the hand and the pose of each individual finger. Finally the palm and fingers are cropped, pose corrected and normalised. In the recognition module, various shape and texture based features are extracted and used for matching purposes. The modified Hausdorff distance, the Iterative Closest Point (ICP) and Independent Component Analysis (ICA) algorithms are used for shape and texture features of the fingers. For the palmprints, we use the Discrete Cosine Transform (DCT), directional line features and ICA. Recognition (identification and verification) tests were performed using fusion strategies based on the similarity scores of the fingers and the palm. Experimental results show that the proposed system exhibits a superior performance over existing systems with an accuracy of over 98\% for hand identification and verification (at equal error rate) in a database of 560 different subjects.
Rethinking Temporal Object Detection from Robotic Perspectives
Authors: Xingyu Chen, Zhengxing Wu, Junzhi Yu, Li Wen
Date: 2019-12-22 00:00:00
Tasks: Multi-Object Tracking, Object, object-detection, Object Detection, Object Tracking, Video Object Detection

Video object detection (VID) has been vigorously studied for years but almost all literature adopts a static accuracy-based evaluation, i.e., average precision (AP). From a robotic perspective, the importance of recall continuity and localization stability is equal to that of accuracy, but the AP is insufficient to reflect detectors' performance across time. In this paper, non-reference assessments are proposed for continuity and stability based on object tracklets. These temporal evaluations can serve as supplements to static AP. Further, we develop an online tracklet refinement for improving detectors' temporal performance through short tracklet suppression, fragment filling, and temporal location fusion. In addition, we propose a small-overlap suppression to extend VID methods to single object tracking (SOT) task so that a flexible SOT-by-detection framework is then formed. Extensive experiments are conducted on ImageNet VID dataset and real-world robotic tasks, where the superiority of our proposed approaches are validated and verified. Codes will be publicly available.
Joint Face Super-Resolution and Deblurring Using a Generative Adversarial Network
Authors: Jung Un Yun, In Kyu Park
Date: 2019-12-22 00:00:00
Tasks: 3D Face Reconstruction, Deblurring, Face Recognition, Face Reconstruction, Generative Adversarial Network, Image Reconstruction, Image Super-Resolution, SSIM, Super-Resolution

Facial image super-resolution (SR) is an important preprocessing for facial image analysis, face recognition, and image-based 3D face reconstruction. Recent convolutional neural network (CNN) based method has shown excellent performance by learning mapping relation using pairs of low-resolution (LR) and high-resolution (HR) facial images. However, since the HR facial image reconstruction using CNN is conventionally aimed to increase the PSNR and SSIM metrics, the reconstructed HR image might not be realistic even with high scores. An adversarial framework is proposed in this study to reconstruct the HR facial image by simultaneously generating an HR image with and without blur. First, the spatial resolution of the LR facial image is increased by eight times using a five-layer CNN. Then, the encoder extracts the features of the up-scaled image. These features are finally sent to two branches (decoders) to generate an HR facial image with and without blur. In addition, local and global discriminators are combined to focus on the reconstruction of HR facial structures. Experiment results show that the proposed algorithm generates a realistic HR facial image. Furthermore, the proposed method can generate a variety of different facial images.
Adversarial Feature Distribution Alignment for Semi-Supervised Learning
Authors: Christoph Mayer, Matthieu Paul, Radu Timofte
Date: 2019-12-22 00:00:00

Training deep neural networks with only a few labeled samples can lead to overfitting. This is problematic in semi-supervised learning where only a few labeled samples are available. In this paper, we show that a consequence of overfitting in SSL is feature distribution misalignment between labeled and unlabeled samples. Hence, we propose a new feature distribution alignment method. Our method is particularly effective when using only a small amount of labeled samples. We test our method on CIFAR10 and SVHN. On SVHN we achieve a test error of 3.88% (250 labeled samples) and 3.39% (1000 labeled samples) which is close to the fully supervised model 2.89% (73k labeled samples). In comparison, the current SOTA achieves only 4.29% and 3.74%. Finally, we provide a theoretical insight why feature distribution alignment occurs and show that our method reduces it.
Facial Synthesis from Visual Attributes via Sketch using Multi-Scale Generators
Authors: Xing Di, Vishal M. Patel
Date: 2019-12-17 00:00:00
Tasks: Attribute, Face Generation

Automatic synthesis of faces from visual attributes is an important problem in computer vision and has wide applications in law enforcement and entertainment. With the advent of deep generative convolutional neural networks (CNNs), attempts have been made to synthesize face images from attributes and text descriptions. In this paper, we take a different approach, where we formulate the original problem as a stage-wise learning problem. We first synthesize the facial sketch corresponding to the visual attributes and then we generate the face image based on the synthesized sketch. The proposed framework, is based on a combination of two different Generative Adversarial Networks (GANs) - (1) a sketch generator network which synthesizes realistic sketch from the input attributes, and (2) a face generator network which synthesizes facial images from the synthesized sketch images with the help of facial attributes. Extensive experiments and comparison with recent methods are performed to verify the effectiveness of the proposed attribute-based two-stage face synthesis method.