pwc-archive/papers-with-abstracts huggingface dataset:
Modelling the development of counting with memory-augmented neural networks
Authors: Zack Dulberg, Taylor Webb, Jonathan Cohen
Date: 2021-05-21 00:00:00
Tasks: Systematic Generalization

Learning to count is an important example of the broader human capacity for systematic generalization, and the development of counting is often characterized by an inflection point when children rapidly acquire proficiency with the procedures that support this ability. We aimed to model this process by training a reinforcement learning agent to select N items from a binary vector when instructed (known as the give-$N$ task). We found that a memory-augmented modular network architecture based on the recently proposed Emergent Symbol Binding Network (ESBN) exhibited an inflection during learning that resembled human development. This model was also capable of systematic extrapolation outside the range of its training set - for example, trained only to select between 1 and 10 items, it could succeed at selecting 11 to 15 items as long as it could make use of an arbitrary count sequence of at least that length. The close parallels to child development and the capacity for extrapolation suggest that our model could shed light on the emergence of systematicity in humans.
GapPredict: A Language Model for Resolving Gaps in Draft Genome Assemblies
Authors: Eric Chen, Justin Chu, Jessica Zhang, Rene L. Warren, Inanc Birol
Date: 2021-05-21 00:00:00
Tasks: Language Modeling, Language Modelling

Short-read DNA sequencing instruments can yield over 1e+12 bases per run, typically composed of reads 150 bases long. Despite this high throughput, de novo assembly algorithms have difficulty reconstructing contiguous genome sequences using short reads due to both repetitive and difficult-to-sequence regions in these genomes. Some of the short read assembly challenges are mitigated by scaffolding assembled sequences using paired-end reads. However, unresolved sequences in these scaffolds appear as "gaps". Here, we introduce GapPredict, a tool that uses a character-level language model to predict unresolved nucleotides in scaffold gaps. We benchmarked GapPredict against the state-of-the-art gap-filling tool Sealer, and observed that the former can fill 65.6% of the sampled gaps that were left unfilled by the latter, demonstrating the practical utility of deep learning approaches to the gap-filling problem in genome sequence assembly.
Addressing the Multiplicity of Solutions in Optical Lens Design as a Niching Evolutionary Algorithms Computational Challenge
Authors: Anna V. Kononova, Ofer M. Shir, Teus Tukker, Pierluigi Frisco, Shutong Zeng, Thomas Bäck
Date: 2021-05-21 00:00:00
Tasks: Evolutionary Algorithms, Triplet

Optimal Lens Design constitutes a fundamental, long-standing real-world optimization challenge. Potentially large number of optima, rich variety of critical points, as well as solid understanding of certain optimal designs per simple problem instances, provide altogether the motivation to address it as a niching challenge. This study applies established Niching-CMA-ES heuristic to tackle this design problem (6-dimensional Cooke triplet) in a simulation-based fashion. The outcome of employing Niching-CMA-ES `out-of-the-box' proves successful, and yet it performs best when assisted by a local searcher which accurately drives the search into optima. The obtained search-points are corroborated based upon concrete knowledge of this problem-instance, accompanied by gradient and Hessian calculations for validation. We extensively report on this computational campaign, which overall resulted in (i) the location of 19 out of 21 known minima within a single run, (ii) the discovery of 540 new optima. These are new minima similar in shape to 21 theoretical solutions, but some of them have better merit function value (unknown heretofore), (iii) the identification of numerous infeasibility pockets throughout the domain (also unknown heretofore). We conclude that niching mechanism is well-suited to address this problem domain, and hypothesize on the apparent multidimensional structures formed by the attained new solutions.
SAT: 2D Semantics Assisted Training for 3D Visual Grounding
Authors: Zhengyuan Yang, Songyang Zhang, LiWei Wang, Jiebo Luo
Date: 2021-05-24 00:00:00
Proceeding: ICCV 2021 10
Tasks: 3D visual grounding, Object, Representation Learning, Visual Grounding

3D visual grounding aims at grounding a natural language description about a 3D scene, usually represented in the form of 3D point clouds, to the targeted object region. Point clouds are sparse, noisy, and contain limited semantic information compared with 2D images. These inherent limitations make the 3D visual grounding problem more challenging. In this study, we propose 2D Semantics Assisted Training (SAT) that utilizes 2D image semantics in the training stage to ease point-cloud-language joint representation learning and assist 3D visual grounding. The main idea is to learn auxiliary alignments between rich, clean 2D object representations and the corresponding objects or mentioned entities in 3D scenes. SAT takes 2D object semantics, i.e., object label, image feature, and 2D geometric feature, as the extra input in training but does not require such inputs during inference. By effectively utilizing 2D semantics in training, our approach boosts the accuracy on the Nr3D dataset from 37.7% to 49.2%, which significantly surpasses the non-SAT baseline with the identical network architecture and inference input. Our approach outperforms the state of the art by large margins on multiple 3D visual grounding datasets, i.e., +10.4% absolute accuracy on Nr3D, +9.9% on Sr3D, and +5.6% on ScanRef.
LuvHarris: A Practical Corner Detector for Event-cameras
Authors: Arren Glover, Aiko Dinale, Leandro De Souza Rosa, Simeon Bamford, Chiara Bartolozzi
Date: 2021-05-24 00:00:00

There have been a number of corner detection methods proposed for event cameras in the last years, since event-driven computer vision has become more accessible. Current state-of-the-art have either unsatisfactory accuracy or real-time performance when considered for practical use, for example when a camera is randomly moved in an unconstrained environment. In this paper, we present yet another method to perform corner detection, dubbed look-up event-Harris (luvHarris), that employs the Harris algorithm for high accuracy but manages an improved event throughput. Our method has two major contributions, 1. a novel "threshold ordinal event-surface" that removes certain tuning parameters and is well suited for Harris operations, and 2. an implementation of the Harris algorithm such that the computational load per event is minimised and computational heavy convolutions are performed only "as-fast-as-possible", i.e. only as computational resources are available. The result is a practical, real-time, and robust corner detector that runs more than 2.6x the speed of current state-of-the-art; a necessity when using high-resolution event-camera in real-time. We explain the considerations taken for the approach, compare the algorithm to current state-of-the-art in terms of computational performance and detection accuracy, and discuss the validity of the proposed approach for event cameras.
Design to automate the detection and counting of Tuberculosis(TB) bacilli
Authors: Dinesh Jackson Samuel, Rajesh Kanna Baskaran
Date: 2021-05-24 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction, Specificity

Tuberculosis is a contagious disease which is one of the leading causes of death, globally. The general diagnosis methods for tuberculosis include microscopic examination, tuberculin skin test, culture method, enzyme linked immunosorbent assay (ELISA) and electronic nose system. World Health Organization (WHO) recommends standard microscopic examination for early diagnosis of tuberculosis. In microscopy, the technician examines field of views (FOVs) in sputum smear for presence of any TB bacilli and counts the number of TB bacilli per FOV to report the level of severity. This process is time consuming with an increased concentration for an experienced staff to examine a single sputum smear. The examination demands for skilled technicians in high-prevalence countries which may lead to overload, fatigue and diminishes the quality of microscopy. Thus, a computer assisted system is proposed and designed for the detection of tuberculosis bacilli to assist pathologists with increased sensitivity and specificity. The manual efforts in detecting and counting the number of TB bacilli is greatly minimized. The system obtains Ziehl-Neelsen stained microscopic images from conventional microscope at 100x magnification and passes the data to the detection system. Initially the segmentation of TB bacilli was done using RGB thresholding and Sauvola's adaptive thresholding algorithm. To eliminate the non-TB bacilli from coarse level segmentation, shape descriptors like area, perimeter, convex hull, major axis length and eccentricity are used to extract only the TB bacilli features. Finally, the TB bacilli are counted using the generated bounding boxes to report the level of severity.
Attention-guided Temporally Coherent Video Object Matting
Authors: Yunke Zhang, Chi Wang, Miaomiao Cui, Peiran Ren, Xuansong Xie, Xian-Sheng Hua, Hujun Bao, QiXing Huang, Weiwei Xu
Date: 2021-05-24 00:00:00
Tasks: Image Matting, Object, Semantic Segmentation, Video Matting, Video Object Segmentation, Video Semantic Segmentation

This paper proposes a novel deep learning-based video object matting method that can achieve temporally coherent matting results. Its key component is an attention-based temporal aggregation module that maximizes image matting networks' strength for video matting networks. This module computes temporal correlations for pixels adjacent to each other along the time axis in feature space, which is robust against motion noises. We also design a novel loss term to train the attention weights, which drastically boosts the video matting performance. Besides, we show how to effectively solve the trimap generation problem by fine-tuning a state-of-the-art video object segmentation network with a sparse set of user-annotated keyframes. To facilitate video matting and trimap generation networks' training, we construct a large-scale video matting dataset with 80 training and 28 validation foreground video clips with ground-truth alpha mattes. Experimental results show that our method can generate high-quality alpha mattes for various videos featuring appearance change, occlusion, and fast motion. Our code and dataset can be found at: https://github.com/yunkezhang/TCVOM
Large-Scale Attribute-Object Compositions
Authors: Filip Radenovic, Animesh Sinha, Albert Gordo, Tamara Berg, Dhruv Mahajan
Date: 2021-05-24 00:00:00
Tasks: Attribute, Object

We study the problem of learning how to predict attribute-object compositions from images, and its generalization to unseen compositions missing from the training data. To the best of our knowledge, this is a first large-scale study of this problem, involving hundreds of thousands of compositions. We train our framework with images from Instagram using hashtags as noisy weak supervision. We make careful design choices for data collection and modeling, in order to handle noisy annotations and unseen compositions. Finally, extensive evaluations show that learning to compose classifiers outperforms late fusion of individual attribute and object predictions, especially in the case of unseen attribute-object pairs.
DDR-Net: Dividing and Downsampling Mixed Network for Diffeomorphic Image Registration
Authors: Ankita Joshi, Yi Hong
Date: 2021-05-24 00:00:00
Tasks: Image Registration

Deep diffeomorphic registration faces significant challenges for high-dimensional images, especially in terms of memory limits. Existing approaches either downsample original images, or approximate underlying transformations, or reduce model size. The information loss during the approximation or insufficient model capacity is a hindrance to the registration accuracy for high-dimensional images, e.g., 3D medical volumes. In this paper, we propose a Dividing and Downsampling mixed Registration network (DDR-Net), a general architecture that preserves most of the image information at multiple scales. DDR-Net leverages the global context via downsampling the input and utilizes the local details from divided chunks of the input images. This design reduces the network input size and its memory cost; meanwhile, by fusing global and local information, DDR-Net obtains both coarse-level and fine-level alignments in the final deformation fields. We evaluate DDR-Net on three public datasets, i.e., OASIS, IBSR18, and 3DIRCADB-01, and the experimental results demonstrate our approach outperforms existing approaches.
Reconstructing Small 3D Objects in front of a Textured Background
Authors: Petr Hruby, Tomas Pajdla
Date: 2021-05-24 00:00:00
Tasks: 3D Reconstruction

We present a technique for a complete 3D reconstruction of small objects moving in front of a textured background. It is a particular variation of multibody structure from motion, which specializes to two objects only. The scene is captured in several static configurations between which the relative pose of the two objects may change. We reconstruct every static configuration individually and segment the points locally by finding multiple poses of cameras that capture the scene's other configurations. Then, the local segmentation results are combined, and the reconstructions are merged into the resulting model of the scene. In experiments with real artifacts, we show that our approach has practical advantages when reconstructing 3D objects from all sides. In this setting, our method outperforms the state-of-the-art. We integrate our method into the state of the art 3D reconstruction pipeline COLMAP.
Multi-modal Understanding and Generation for Medical Images and Text via Vision-Language Pre-Training
Authors: Jong Hak Moon, Hyungyung Lee, Woncheol Shin, Young-Hak Kim, Edward Choi
Date: 2021-05-24 00:00:00
Tasks: Image Captioning, Medical Visual Question Answering, Multimodal Deep Learning, Question Answering, Representation Learning, Retrieval, Text Generation, Visual Question Answering, Visual Question Answering (VQA)

Recently a number of studies demonstrated impressive performance on diverse vision-language multi-modal tasks such as image captioning and visual question answering by extending the BERT architecture with multi-modal pre-training objectives. In this work we explore a broad set of multi-modal representation learning tasks in the medical domain, specifically using radiology images and the unstructured report. We propose Medical Vision Language Learner (MedViLL), which adopts a BERT-based architecture combined with a novel multi-modal attention masking scheme to maximize generalization performance for both vision-language understanding tasks (diagnosis classification, medical image-report retrieval, medical visual question answering) and vision-language generation task (radiology report generation). By statistically and rigorously evaluating the proposed model on four downstream tasks with three radiographic image-report datasets (MIMIC-CXR, Open-I, and VQA-RAD), we empirically demonstrate the superior downstream task performance of MedViLL against various baselines, including task-specific architectures. The source code is publicly available at: https://github.com/SuperSupermoon/MedViLL
Real-time Human Action Recognition Using Locally Aggregated Kinematic-Guided Skeletonlet and Supervised Hashing-by-Analysis Model
Authors: Bin Sun, Shaofan Wang, Dehui Kong, LiChun Wang, BaoCai Yin
Date: 2021-05-24 00:00:00
Tasks: 3D Action Recognition, Action Recognition, Denoising, Temporal Action Localization

3D action recognition is referred to as the classification of action sequences which consist of 3D skeleton joints. While many research work are devoted to 3D action recognition, it mainly suffers from three problems: highly complicated articulation, a great amount of noise, and a low implementation efficiency. To tackle all these problems, we propose a real-time 3D action recognition framework by integrating the locally aggregated kinematic-guided skeletonlet (LAKS) with a supervised hashing-by-analysis (SHA) model. We first define the skeletonlet as a few combinations of joint offsets grouped in terms of kinematic principle, and then represent an action sequence using LAKS, which consists of a denoising phase and a locally aggregating phase. The denoising phase detects the noisy action data and adjust it by replacing all the features within it with the features of the corresponding previous frame, while the locally aggregating phase sums the difference between an offset feature of the skeletonlet and its cluster center together over all the offset features of the sequence. Finally, the SHA model which combines sparse representation with a hashing model, aiming at promoting the recognition accuracy while maintaining a high efficiency. Experimental results on MSRAction3D, UTKinectAction3D and Florence3DAction datasets demonstrate that the proposed method outperforms state-of-the-art methods in both recognition accuracy and implementation efficiency.
LineCounter: Learning Handwritten Text Line Segmentation by Counting
Authors: Deng Li, Yue Wu, Yicong Zhou
Date: 2021-05-24 00:00:00
Tasks: Handwritten Text Recognition, object-detection, Object Detection, Segmentation, Semantic Segmentation

Handwritten Text Line Segmentation (HTLS) is a low-level but important task for many higher-level document processing tasks like handwritten text recognition. It is often formulated in terms of semantic segmentation or object detection in deep learning. However, both formulations have serious shortcomings. The former requires heavy post-processing of splitting/merging adjacent segments, while the latter may fail on dense or curved texts. In this paper, we propose a novel Line Counting formulation for HTLS -- that involves counting the number of text lines from the top at every pixel location. This formulation helps learn an end-to-end HTLS solution that directly predicts per-pixel line number for a given document image. Furthermore, we propose a deep neural network (DNN) model LineCounter to perform HTLS through the Line Counting formulation. Our extensive experiments on the three public datasets (ICDAR2013-HSC, HIT-MW, and VML-AHTE) demonstrate that LineCounter outperforms state-of-the-art HTLS approaches. Source code is available at https://github.com/Leedeng/Line-Counter.
What is the State of the Art of Computer Vision-Assisted Cytology? A Systematic Literature Review
Authors: André Victória Matias, João Gustavo Atkinson Amorim, Luiz Antonio Buschetto Macarini, Allan Cerentini, Alexandre Sherlley Casimiro Onofre, Fabiana Botelho de Miranda Onofre, Felipe Perozzo Daltoé, Marcelo Ricardo Stemmer, Aldo von Wangenheim
Date: 2021-05-24 00:00:00
Tasks: Articles, Diagnostic, object-detection, Object Detection, Systematic Literature Review

Cytology is a low-cost and non-invasive diagnostic procedure employed to support the diagnosis of a broad range of pathologies. Computer Vision technologies, by automatically generating quantitative and objective descriptions of examinations' contents, can help minimize the chances of misdiagnoses and shorten the time required for analysis. To identify the state-of-art of computer vision techniques currently applied to cytology, we conducted a Systematic Literature Review. We analyzed papers published in the last 5 years. The initial search was executed in September 2020 and resulted in 431 articles. After applying the inclusion/exclusion criteria, 157 papers remained, which we analyzed to build a picture of the tendencies and problems present in this research area, highlighting the computer vision methods, staining techniques, evaluation metrics, and the availability of the used datasets and computer code. As a result, we identified that the most used methods in the analyzed works are deep learning-based (70 papers), while fewer works employ classic computer vision only (101 papers). The most recurrent metric used for classification and object detection was the accuracy (33 papers and 5 papers), while for segmentation it was the Dice Similarity Coefficient (38 papers). Regarding staining techniques, Papanicolaou was the most employed one (130 papers), followed by H&E (20 papers) and Feulgen (5 papers). Twelve of the datasets used in the papers are publicly available, with the DTU/Herlev dataset being the most used one. We conclude that there still is a lack of high-quality datasets for many types of stains and most of the works are not mature enough to be applied in a daily clinical diagnostic routine. We also identified a growing tendency towards adopting deep learning-based approaches as the methods of choice.
A self-supervised learning strategy for postoperative brain cavity segmentation simulating resections
Authors: Fernando Pérez-García, Reuben Dorent, Michele Rizzi, Francesco Cardinale, Valerio Frazzini, Vincent Navarro, Caroline Essert, Irène Ollivier, Tom Vercauteren, Rachel Sparks, John S. Duncan, Sébastien Ourselin
Date: 2021-05-24 00:00:00
Tasks: Image Segmentation, Segmentation, Self-Supervised Learning, Semantic Segmentation

Accurate segmentation of brain resection cavities (RCs) aids in postoperative analysis and determining follow-up treatment. Convolutional neural networks (CNNs) are the state-of-the-art image segmentation technique, but require large annotated datasets for training. Annotation of 3D medical images is time-consuming, requires highly-trained raters, and may suffer from high inter-rater variability. Self-supervised learning strategies can leverage unlabeled data for training. We developed an algorithm to simulate resections from preoperative magnetic resonance images (MRIs). We performed self-supervised training of a 3D CNN for RC segmentation using our simulation method. We curated EPISURG, a dataset comprising 430 postoperative and 268 preoperative MRIs from 430 refractory epilepsy patients who underwent resective neurosurgery. We fine-tuned our model on three small annotated datasets from different institutions and on the annotated images in EPISURG, comprising 20, 33, 19 and 133 subjects. The model trained on data with simulated resections obtained median (interquartile range) Dice score coefficients (DSCs) of 81.7 (16.4), 82.4 (36.4), 74.9 (24.2) and 80.5 (18.7) for each of the four datasets. After fine-tuning, DSCs were 89.2 (13.3), 84.1 (19.8), 80.2 (20.1) and 85.2 (10.8). For comparison, inter-rater agreement between human annotators from our previous study was 84.0 (9.9). We present a self-supervised learning strategy for 3D CNNs using simulated RCs to accurately segment real RCs on postoperative MRI. Our method generalizes well to data from different institutions, pathologies and modalities. Source code, segmentation models and the EPISURG dataset are available at https://github.com/fepegar/ressegijcars .