pwc-archive/papers-with-abstracts huggingface dataset:
Efficient Querying from Weighted Binary Codes
Authors: Zhenyu Weng, Yuesheng Zhu
Date: 2019-11-21 00:00:00

Binary codes are widely used to represent the data due to their small storage and efficient computation. However, there exists an ambiguity problem that lots of binary codes share the same Hamming distance to a query. To alleviate the ambiguity problem, weighted binary codes assign different weights to each bit of binary codes and compare the binary codes by the weighted Hamming distance. Till now, performing the querying from the weighted binary codes efficiently is still an open issue. In this paper, we propose a new method to rank the weighted binary codes and return the nearest weighted binary codes of the query efficiently. In our method, based on the multi-index hash tables, two algorithms, the table bucket finding algorithm and the table merging algorithm, are proposed to select the nearest weighted binary codes of the query in a non-exhaustive and accurate way. The proposed algorithms are justified by proving their theoretic properties. The experiments on three large-scale datasets validate both the search efficiency and the search accuracy of our method. Especially for the number of weighted binary codes up to one billion, our method shows a great improvement of more than 1000 times faster than the linear scan.
Oktoberfest Food Dataset
Authors: Alexander Ziller, Julius Hansjakob, Vitalii Rusinov, Daniel Zügner, Peter Vogel, Stephan Günnemann
Date: 2019-11-22 00:00:00
Tasks: Object, object-detection, Object Detection

We release a realistic, diverse, and challenging dataset for object detection on images. The data was recorded at a beer tent in Germany and consists of 15 different categories of food and drink items. We created more than 2,500 object annotations by hand for 1,110 images captured by a video camera above the checkout. We further make available the remaining 600GB of (unlabeled) data containing days of footage. Additionally, we provide our trained models as a benchmark. Possible applications include automated checkout systems which could significantly speed up the process.
Deep-Learning Assisted High-Resolution Binocular Stereo Depth Reconstruction
Authors: Yaoyu Hu, Weikun Zhen, Sebastian Scherer
Date: 2019-11-23 00:00:00
Tasks: 3D Reconstruction, Deep Learning, Vocal Bursts Intensity Prediction

This work presents dense stereo reconstruction using high-resolution images for infrastructure inspections. The state-of-the-art stereo reconstruction methods, both learning and non-learning ones, consume too much computational resource on high-resolution data. Recent learning-based methods achieve top ranks on most benchmarks. However, they suffer from the generalization issue due to lack of task-specific training data. We propose to use a less resource demanding non-learning method, guided by a learning-based model, to handle high-resolution images and achieve accurate stereo reconstruction. The deep-learning model produces an initial disparity prediction with uncertainty for each pixel of the down-sampled stereo image pair. The uncertainty serves as a self-measurement of its generalization ability and the per-pixel searching range around the initially predicted disparity. The downstream process performs a modified version of the Semi-Global Block Matching method with the up-sampled per-pixel searching range. The proposed deep-learning assisted method is evaluated on the Middlebury dataset and high-resolution stereo images collected by our customized binocular stereo camera. The combination of learning and non-learning methods achieves better performance on 12 out of 15 cases of the Middlebury dataset. In our infrastructure inspection experiments, the average 3D reconstruction error is less than 0.004m.
Line-based Camera Pose Estimation in Point Cloud of Structured Environments
Authors: Huai Yu, Weikun Zhen, Wen Yang, Sebastian Scherer
Date: 2019-11-23 00:00:00
Tasks: Camera Localization, Camera Pose Estimation, Pose Estimation, Translation, Two-sample testing

Accurate registration of 2D imagery with point clouds is a key technology for image-LiDAR point cloud fusion, camera to laser scanner calibration and camera localization. Despite continuous improvements, automatic registration of 2D and 3D data without using additional textured information still faces great challenges. In this paper, we propose a new 2D-3D registration method to estimate 2D-3D line feature correspondences and the camera pose in untextured point clouds of structured environments. Specifically, we first use geometric constraints between vanishing points and 3D parallel lines to compute all feasible camera rotations. Then, we utilize a hypothesis testing strategy to estimate the 2D-3D line correspondences and the translation vector. By checking the consistency with computed correspondences, the best rotation matrix can be found. Finally, the camera pose is further refined using non-linear optimization with all the 2D-3D line correspondences. The experimental results demonstrate the effectiveness of the proposed method on the synthetic and real dataset (outdoors and indoors) with repeated structures and rapid depth changes.
Natural Image Manipulation for Autoregressive Models Using Fisher Scores
Authors: Wilson Yan, Jonathan Ho, Pieter Abbeel
Date: 2019-11-25 00:00:00
Tasks: Image Manipulation

Deep autoregressive models are one of the most powerful models that exist today which achieve state-of-the-art bits per dim. However, they lie at a strict disadvantage when it comes to controlled sample generation compared to latent variable models. Latent variable models such as VAEs and normalizing flows allow meaningful semantic manipulations in latent space, which autoregressive models do not have. In this paper, we propose using Fisher scores as a method to extract embeddings from an autoregressive model to use for interpolation and show that our method provides more meaningful sample manipulation compared to alternate embeddings such as network activations.
Robust Feature-Based Point Registration Using Directional Mixture Model
Authors: Saman Fahandezh-Saadi, Di Wang, Masayoshi Tomizuka
Date: 2019-11-25 00:00:00
Tasks: Translation

This paper presents a robust probabilistic point registration method for estimating the rigid transformation (i.e. rotation matrix and translation vector) between two pointcloud dataset. The method improves the robustness of point registration and consequently the robot localization in the presence of outliers in the pointclouds which always occurs due to occlusion, dynamic objects, and sensor errors. The framework models the point registration task based on directional statistics on a unit sphere. In particular, a Kent distribution mixture model is adopted and the process of point registration has been carried out in the two phases of Expectation-Maximization algorithm. The proposed method has been evaluated on the pointcloud dataset from LiDAR sensors in an indoor environment.
A Comprehensive Review On Various State Of Art Techniques For Eye Blink Detection
Authors: Sannidhan MS, Sunil Kumar Aithal, Abhir Bhandary
Date: 2019-11-27 00:00:00

Computer Vision is considered to be one of the most important areas in research and has focused on developing many applications that has proved to be useful for both research and societal benefits. Today we have been witnessing many of the road mishaps happening just because of the lack of concentration while driving.As a part of avoiding this kind of disaster happening in day to day life there are many technologies focusing on keeping track of the vehicle drivers concentration.One such technology uses the method of eye blink detection to find out the concentration level of the driver.With the advent of many high end camera devices with cost effectiveness factor today it has become more efficient and cheaper to use eye blink detection for keeping track of the concentration level of the driver.Hence this paper presents an exhaustive review on the implementations of various eye blink detection algorithms.The detection system has also extended its application in various other fields like drowsiness detection and fatigue detection and expression detection.
Source Camera Verification from Strongly Stabilized Videos
Authors: Enes Altinisik, Husrev Taha Sencar
Date: 2019-11-26 00:00:00
Tasks: Video Generation

Image stabilization performed during imaging and/or post-processing poses one of the most significant challenges to photo-response non-uniformity based source camera attribution from videos. When performed digitally, stabilization involves cropping, warping, and inpainting of video frames to eliminate unwanted camera motion. Hence, successful attribution requires the inversion of these transformations in a blind manner. To address this challenge, we introduce a source camera verification method for videos that takes into account the spatially variant nature of stabilization transformations and assumes a larger degree of freedom in their search. Our method identifies transformations at a sub-frame level, incorporates a number of constraints to validate their correctness, and offers computational flexibility in the search for the correct transformation. The method also adopts a holistic approach in countering disruptive effects of other video generation steps, such as video coding and downsizing, for more reliable attribution. Tests performed on one public and two custom datasets show that the proposed method is able to verify the source of 23-30% of all videos that underwent stronger stabilization, depending on computation load, without a significant impact on false attribution.
SketchZooms: Deep multi-view descriptors for matching line drawings
Authors: Pablo Navarro, José Ignacio Orlando, Claudio Delrieux, Emmanuel Iarussi
Date: 2019-11-29 00:00:00

Finding point-wise correspondences between images is a long-standing problem in image analysis. This becomes particularly challenging for sketch images, due to the varying nature of human drawing style, projection distortions and viewport changes. In this paper we present the first attempt to obtain a learned descriptor for dense registration in line drawings. Based on recent deep learning techniques for corresponding photographs, we designed descriptors to locally match image pairs where the object of interest belongs to the same semantic category, yet still differ drastically in shape, form, and projection angle. To this end, we have specifically crafted a data set of synthetic sketches using non-photorealistic rendering over a large collection of part-based registered 3D models. After training, a neural network generates descriptors for every pixel in an input image, which are shown to generalize correctly in unseen sketches hand-drawn by humans. We evaluate our method against a baseline of correspondences data collected from expert designers, in addition to comparisons with other descriptors that have been proven effective in sketches. Code, data and further resources will be publicly released by the time of publication.
CG-GAN: An Interactive Evolutionary GAN-based Approach for Facial Composite Generation)
Authors: Nicola Zaltron, Luisa Zurlo, Sebastian Risi
Date: 2019-11-28 00:00:00

Facial composites are graphical representations of an eyewitness's memory of a face. Many digital systems are available for the creation of such composites but are either unable to reproduce features unless previously designed or do not allow holistic changes to the image. In this paper, we improve the efficiency of composite creation by removing the reliance on expert knowledge and letting the system learn to represent faces from examples. The novel approach, Composite Generating GAN (CG-GAN), applies generative and evolutionary computation to allow casual users to easily create facial composites. Specifically, CG-GAN utilizes the generator network of a pg-GAN to create high-resolution human faces. Users are provided with several functions to interactively breed and edit faces. CG-GAN offers a novel way of generating and handling static and animated photo-realistic facial composites, with the possibility of combining multiple representations of the same perpetrator, generated by different eyewitnesses.
Design and Interpretation of Universal Adversarial Patches in Face Detection
Authors: Xiao Yang, Fangyun Wei, Hongyang Zhang, Jun Zhu
Date: 2019-11-30 00:00:00
Proceeding: ECCV 2020 8
Tasks: Face Detection

We consider universal adversarial patches for faces -- small visual elements whose addition to a face image reliably destroys the performance of face detectors. Unlike previous work that mostly focused on the algorithmic design of adversarial examples in terms of improving the success rate as an attacker, in this work we show an interpretation of such patches that can prevent the state-of-the-art face detectors from detecting the real faces. We investigate a phenomenon: patches designed to suppress real face detection appear face-like. This phenomenon holds generally across different initialization, locations, scales of patches, backbones, and state-of-the-art face detection frameworks. We propose new optimization-based approaches to automatic design of universal adversarial patches for varying goals of the attack, including scenarios in which true positives are suppressed without introducing false positives. Our proposed algorithms perform well on real-world datasets, deceiving state-of-the-art face detectors in terms of multiple precision/recall metrics and transferability.
A Robust Stereo Camera Localization Method with Prior LiDAR Map Constrains
Authors: Dong Han, Zuhao Zou, Lujia Wang, Cheng-Zhong Xu
Date: 2019-12-02 00:00:00
Tasks: Camera Localization, Visual Localization

In complex environments, low-cost and robust localization is a challenging problem. For example, in a GPSdenied environment, LiDAR can provide accurate position information, but the cost is high. In general, visual SLAM based localization methods become unreliable when the sunlight changes greatly. Therefore, inexpensive and reliable methods are required. In this paper, we propose a stereo visual localization method based on the prior LiDAR map. Different from the conventional visual localization system, we design a novel visual optimization model by matching planar information between the LiDAR map and visual image. Bundle adjustment is built by using coplanarity constraints. To solve the optimization problem, we use a graph-based optimization algorithm and a local window optimization method. Finally, we estimate a full six degrees of freedom (DOF) pose without scale drift. To validate the efficiency, the proposed method has been tested on the KITTI dataset. The results show that our method is more robust and accurate than the state-of-art ORB-SLAM2.
Google street view and deep learning: a new ground truthing approach for crop mapping
Authors: Yulin Yan, Youngryel Ryu
Date: 2019-12-03 00:00:00
Tasks: image-classification, Image Classification

Ground referencing is essential for supervised crop mapping. However, conventional ground truthing involves extensive field surveys and post processing, which is costly in terms of time and labor. In this study, we applied a convolutional neural network (CNN) model to explore the efficacy of automatic ground truthing via Google street view (GSV) images in two distinct farming regions: central Illinois and southern California. We demonstrated the feasibility and reliability of the new ground referencing technique further by performing pixel-based crop mapping with vegetation indices as the model input. The results were evaluated using the United States Department of Agriculture (USDA) crop data layer (CDL) products. From 8,514 GSV images, the CNN model screened out 2,645 target crop images. These images were well classified into crop types, including alfalfa, almond, corn, cotton, grape, soybean, and pistachio. The overall GSV image classification accuracy reached 93% in California and 97% in Illinois. We then shifted the image geographic coordinates using fixed empirical coefficients to produce 8,173 crop reference points including 1,764 in Illinois and 6,409 in California. Evaluation of these new reference points with CDL products showed satisfactory coherence, with 94 to 97% agreement. CNN-based mapping also captured the general pattern of crop type distributions. The overall differences between CDL products and our mapping results were 4% in California and 5% in Illinois. Thus, using these deep learning and GSV image techniques, we have provided an efficient and cost-effective alternative method for ground referencing and crop mapping.
Detecting Hardly Visible Roads in Low-Resolution Satellite Time Series Data
Authors: Stefan Oehmcke, Christoffer Thrysøe, Andreas Borgstad, Marcos Antonio Vaz Salles, Martin Brandt, Fabian Gieseke
Date: 2019-12-04 00:00:00
Tasks: Ordinal Classification, Time Series, Time Series Analysis

Massive amounts of satellite data have been gathered over time, holding the potential to unveil a spatiotemporal chronicle of the surface of Earth. These data allow scientists to investigate various important issues, such as land use changes, on a global scale. However, not all land-use phenomena are equally visible on satellite imagery. In particular, the creation of an inventory of the planet's road infrastructure remains a challenge, despite being crucial to analyze urbanization patterns and their impact. Towards this end, this work advances data-driven approaches for the automatic identification of roads based on open satellite data. Given the typical resolutions of these historical satellite data, we observe that there is inherent variation in the visibility of different road types. Based on this observation, we propose two deep learning frameworks that extend state-of-the-art deep learning methods by formalizing road detection as an ordinal classification task. In contrast to related schemes, one of the two models also resorts to satellite time series data that are potentially affected by missing data and cloud occlusion. Taking these time series data into account eliminates the need to manually curate datasets of high-quality image tiles, substantially simplifying the application of such models on a global scale. We evaluate our approaches on a dataset that is based on Sentinel~2 satellite imagery and OpenStreetMap vector data. Our results indicate that the proposed models can successfully identify large and medium-sized roads. We also discuss opportunities and challenges related to the detection of roads and other infrastructure on a global scale.
Continual egocentric object recognition
Authors: Luca Erculiani, Fausto Giunchiglia, Andrea Passerini
Date: 2019-12-06 00:00:00
Tasks: Active Learning, Novelty Detection, Object, Object Recognition

We present a framework capable of tackilng the problem of continual object recognition in a setting which resembles that under whichhumans see and learn. This setting has a set of unique characteristics:it assumes an egocentric point-of-view bound to the needs of a singleperson, which implies a relatively low diversity of data and a coldstart with no data; it requires to operate in an open world, where newobjects can be encounteredat any time; supervision is scarce and hasto be solicited to the user, and completelyunsupervised recognitionof new objects should be possible. Note that this setting differs fromthe one addressed in the open world recognition literature, where supervised feedback is always requested to be able to incorporate newobjects. We propose a first solution to this problem in the form ofa memory-based incremental framework that is capable of storinginformation of each and any object it encounters, while using the supervision of the user to learn to discriminate between known and unknown objects. Our approach is based on four main features: the useof time and space persistence (i.e., the appearance of objects changesrelatively slowly), the use of similarity as the main driving principlefor object recognition and novelty detection, the progressive introduction of new objects in a developmental fashion and the selectiveelicitation of user feedback in an online active learning fashion. Experimental results show the feasibility of open world, generic objectrecognition, the ability to recognize, memorize and re-identify newobjects even in complete absence of user supervision, and the utilityof persistence and incrementality in boosting performance.
Video Person Re-ID: Fantastic Techniques and Where to Find Them
Authors: Priyank Pathak, Amir Erfan Eshratifar, Michael Gormish
Date: 2019-11-21 00:00:00
Tasks: Person Re-Identification, Video-Based Person Re-Identification

The ability to identify the same person from multiple camera views without the explicit use of facial recognition is receiving commercial and academic interest. The current status-quo solutions are based on attention neural models. In this paper, we propose Attention and CL loss, which is a hybrid of center and Online Soft Mining (OSM) loss added to the attention loss on top of a temporal attention-based neural network. The proposed loss function applied with bag-of-tricks for training surpasses the state of the art on the common person Re-ID datasets, MARS and PRID 2011. Our source code is publicly available on github.
Deep Learning Methods for Signature Verification
Authors: Zihan Zeng, Jing Tian
Date: 2019-12-08 00:00:00
Tasks: Deep Learning

Signature is widely used in human daily lives, and serves as a supplementary characteristic for verifying human identity. However, there is rare work of verifying signature. In this paper, we propose a few deep learning architectures to tackle this task, ranging from CNN, RNN to CNN-RNN compact model. We also improve Path Signature Features by encoding temporal information in order to enlarge the discrepancy between genuine and forgery signatures. Our numerical experiments demonstrate the effectiveness of our constructed models and features representations.
Analysis of effectiveness of thresholding in perfusion ROI detection on T2-weighted MR images with abnormal brain anatomy
Authors: Svitlana Alkhimova, Svitlana Sliusar
Date: 2019-12-05 00:00:00
Tasks: Anatomy, Brain Segmentation

The brain perfusion ROI detection being a preliminary step, designed to exclude non-brain tissues from analyzed DSC perfusion MR images. Its accuracy is considered as the key factor for delivering correct results of perfusion data analysis. Despite the large variety of algorithms developed on brain tissues segmentation, there is no one that works reliably and robustly on 2T-waited MR images of a human head with abnormal brain anatomy. Therefore, thresholding method is still the state-of-the-art technique that is widely used as a way of managing pixels involved in brain perfusion ROI. This paper presents the analysis of effectiveness of thresholding techniques in brain perfusion ROI detection on 2T-waited MR images of a human head with abnormal brain anatomy. Four threshold-based algorithms implementation are considered: according to Otsu method as global thresholding, according to Niblack method as local thresholding, thresholding in approximate anatomical brain location, and brute force thresholding. The analysis is done using comparison of qualitative maps produced from thresholded images and from the reference ones. Pearson correlation analysis showed strong positive (r was ranged from 0.7123 to 0.8518, p<0.01) and weak positive (r<0.35, p<0.01) relationship in case of conducted experiments with CBF, CBV, MTT and Tmax maps, respectively. Linear regression analysis showed at level of 95% confidence interval that maps produced from thresholded images were subject to scale and offset errors in all conducted experiments. The experimental results showed that widely used thresholding methods are an ineffective way of managing pixels involved in brain perfusion ROI. Thresholding as brain segmentation tool can lead to poor placement of perfusion ROI and, as a result, produced maps will be subject to artifacts and can cause falsely high or falsely low perfusion parameters assessment.