pwc-archive/papers-with-abstracts huggingface dataset:
SAMOT: Switcher-Aware Multi-Object Tracking and Still Another MOT Measure
Authors: Weitao Feng, Zhihao Hu, Baopu Li, Weihao Gan, Wei Wu, Wanli Ouyang
Date: 2020-09-22 00:00:00
Tasks: Multi-Object Tracking, Object, Object Tracking

Multi-Object Tracking (MOT) is a popular topic in computer vision. However, identity issue, i.e., an object is wrongly associated with another object of a different identity, still remains to be a challenging problem. To address it, switchers, i.e., confusing targets thatmay cause identity issues, should be focused. Based on this motivation,this paper proposes a novel switcher-aware framework for multi-object tracking, which consists of Spatial Conflict Graph model (SCG) and Switcher-Aware Association (SAA). The SCG eliminates spatial switch-ers within one frame by building a conflict graph and working out the optimal subgraph. The SAA utilizes additional information from potential temporal switcher across frames, enabling more accurate data association. Besides, we propose a new MOT evaluation measure, Still Another IDF score (SAIDF), aiming to focus more on identity issues.This new measure may overcome some problems of the previous measures and provide a better insight for identity issues in MOT. Finally,the proposed framework is tested under both the traditional measures and the new measure we proposed. Extensive experiments show that ourmethod achieves competitive results on all measure.
Neural Face Models for Example-Based Visual Speech Synthesis
Authors: Wolfgang Paier, Anna Hilsmann, Peter Eisert
Date: 2020-09-22 00:00:00
Tasks: Face Model, Speech Synthesis

Creating realistic animations of human faces with computer graphic models is still a challenging task. It is often solved either with tedious manual work or motion capture based techniques that require specialised and costly hardware. Example based animation approaches circumvent these problems by re-using captured data of real people. This data is split into short motion samples that can be looped or concatenated in order to create novel motion sequences. The obvious advantages of this approach are the simplicity of use and the high realism, since the data exhibits only real deformations. Rather than tuning weights of a complex face rig, the animation task is performed on a higher level by arranging typical motion samples in a way such that the desired facial performance is achieved. Two difficulties with example based approaches, however, are high memory requirements as well as the creation of artefact-free and realistic transitions between motion samples. We solve these problems by combining the realism and simplicity of example-based animations with the advantages of neural face models. Our neural face model is capable of synthesising high quality 3D face geometry and texture according to a compact latent parameter vector. This latent representation reduces memory requirements by a factor of 100 and helps creating seamless transitions between concatenated motion samples. In this paper, we present a marker-less approach for facial motion capture based on multi-view video. Based on the captured data, we learn a neural representation of facial expressions, which is used to seamlessly concatenate facial performances during the animation procedure. We demonstrate the effectiveness of our approach by synthesising mouthings for Swiss-German sign language based on viseme query sequences.
Visual Methods for Sign Language Recognition: A Modality-Based Review
Authors: Bassem Seddik, Najoua Essoukri Ben Amara
Date: 2020-09-22 00:00:00
Tasks: GPU, Sign Language Recognition

Sign language visual recognition from continuous multi-modal streams is still one of the most challenging fields. Recent advances in human actions recognition are exploiting the ascension of GPU-based learning from massive data, and are getting closer to human-like performances. They are then prone to creating interactive services for the deaf and hearing-impaired communities. A population that is expected to grow considerably in the years to come. This paper aims at reviewing the human actions recognition literature with the sign-language visual understanding as a scope. The methods analyzed will be mainly organized according to the different types of unimodal inputs exploited, their relative multi-modal combinations and pipeline steps. In each section, we will detail and compare the related datasets, approaches then distinguish the still open contribution paths suitable for the creation of sign language related services. Special attention will be paid to the approaches and commercial solutions handling facial expressions and continuous signing.
Conditional Sequential Modulation for Efficient Global Image Retouching
Authors: Jingwen He, Yihao Liu, Yu Qiao, Chao Dong
Date: 2020-09-22 00:00:00
Proceeding: ECCV 2020 8
Tasks: Image Retouching, Photo Retouching

Photo retouching aims at enhancing the aesthetic visual quality of images that suffer from photographic defects such as over/under exposure, poor contrast, inharmonious saturation. Practically, photo retouching can be accomplished by a series of image processing operations. In this paper, we investigate some commonly-used retouching operations and mathematically find that these pixel-independent operations can be approximated or formulated by multi-layer perceptrons (MLPs). Based on this analysis, we propose an extremely light-weight framework - Conditional Sequential Retouching Network (CSRNet) - for efficient global image retouching. CSRNet consists of a base network and a condition network. The base network acts like an MLP that processes each pixel independently and the condition network extracts the global features of the input image to generate a condition vector. To realize retouching operations, we modulate the intermediate features using Global Feature Modulation (GFM), of which the parameters are transformed by condition vector. Benefiting from the utilization of $1\times1$ convolution, CSRNet only contains less than 37k trainable parameters, which is orders of magnitude smaller than existing learning-based methods. Extensive experiments show that our method achieves state-of-the-art performance on the benchmark MIT-Adobe FiveK dataset quantitively and qualitatively. Code is available at https://github.com/hejingwenhejingwen/CSRNet.
Frame-wise Cross-modal Matching for Video Moment Retrieval
Authors: Haoyu Tang, Jihua Zhu, Meng Liu, Member, IEEE, Zan Gao, Zhiyong Cheng
Date: 2020-09-22 00:00:00
Tasks: Boundary Detection, Moment Retrieval, Retrieval

Video moment retrieval targets at retrieving a moment in a video for a given language query. The challenges of this task include 1) the requirement of localizing the relevant moment in an untrimmed video, and 2) bridging the semantic gap between textual query and video contents. To tackle those problems, early approaches adopt the sliding window or uniform sampling to collect video clips first and then match each clip with the query. Obviously, these strategies are time-consuming and often lead to unsatisfied accuracy in localization due to the unpredictable length of the golden moment. To avoid the limitations, researchers recently attempt to directly predict the relevant moment boundaries without the requirement to generate video clips first. One mainstream approach is to generate a multimodal feature vector for the target query and video frames (e.g., concatenation) and then use a regression approach upon the multimodal feature vector for boundary detection. Although some progress has been achieved by this approach, we argue that those methods have not well captured the cross-modal interactions between the query and video frames. In this paper, we propose an Attentive Cross-modal Relevance Matching (ACRM) model which predicts the temporal boundaries based on an interaction modeling. In addition, an attention module is introduced to assign higher weights to query words with richer semantic cues, which are considered to be more important for finding relevant video contents. Another contribution is that we propose an additional predictor to utilize the internal frames in the model training to improve the localization accuracy. Extensive experiments on two datasets TACoS and Charades-STA demonstrate the superiority of our method over several state-of-the-art methods. Ablation studies have been also conducted to examine the effectiveness of different modules in our ACRM model.
Performance Indicator in Multilinear Compressive Learning
Authors: Dat Thanh Tran, Moncef Gabbouj, Alexandros Iosifidis
Date: 2020-09-22 00:00:00
Tasks: Compressive Sensing

Recently, the Multilinear Compressive Learning (MCL) framework was proposed to efficiently optimize the sensing and learning steps when working with multidimensional signals, i.e. tensors. In Compressive Learning in general, and in MCL in particular, the number of compressed measurements captured by a compressive sensing device characterizes the storage requirement or the bandwidth requirement for transmission. This number, however, does not completely characterize the learning performance of a MCL system. In this paper, we analyze the relationship between the input signal resolution, the number of compressed measurements and the learning performance of MCL. Our empirical analysis shows that the reconstruction error obtained at the initialization step of MCL strongly correlates with the learning performance, thus can act as a good indicator to efficiently characterize learning performances obtained from different sensor configurations without optimizing the entire system.
Deep N-ary Error Correcting Output Codes
Authors: Hao Zhang, Joey Tianyi Zhou, Tianying Wang, Ivor W. Tsang, Rick Siow Mong Goh
Date: 2020-09-22 00:00:00
Tasks: Classification, Ensemble Learning, General Classification, Multi-class Classification, text-classification, Text Classification

Ensemble learning consistently improves the performance of multi-class classification through aggregating a series of base classifiers. To this end, data-independent ensemble methods like Error Correcting Output Codes (ECOC) attract increasing attention due to its easiness of implementation and parallelization. Specifically, traditional ECOCs and its general extension N-ary ECOC decompose the original multi-class classification problem into a series of independent simpler classification subproblems. Unfortunately, integrating ECOCs, especially N-ary ECOC with deep neural networks, termed as deep N-ary ECOC, is not straightforward and yet fully exploited in the literature, due to the high expense of training base learners. To facilitate the training of N-ary ECOC with deep learning base learners, we further propose three different variants of parameter sharing architectures for deep N-ary ECOC. To verify the generalization ability of deep N-ary ECOC, we conduct experiments by varying the backbone with different deep neural network architectures for both image and text classification tasks. Furthermore, extensive ablation studies on deep N-ary ECOC show its superior performance over other deep data-independent ensemble methods.
Self-Supervised Learning of Non-Rigid Residual Flow and Ego-Motion
Authors: Ivan Tishchenko, Sandro Lombardi, Martin R. Oswald, Marc Pollefeys
Date: 2020-09-22 00:00:00
Tasks: Self-Supervised Learning, Translation

Most of the current scene flow methods choose to model scene flow as a per point translation vector without differentiating between static and dynamic components of 3D motion. In this work we present an alternative method for end-to-end scene flow learning by joint estimation of non-rigid residual flow and ego-motion flow for dynamic 3D scenes. We propose to learn the relative rigid transformation from a pair of point clouds followed by an iterative refinement. We then learn the non-rigid flow from transformed inputs with the deducted rigid part of the flow. Furthermore, we extend the supervised framework with self-supervisory signals based on the temporal consistency property of a point cloud sequence. Our solution allows both training in a supervised mode complemented by self-supervisory loss terms as well as training in a fully self-supervised mode. We demonstrate that decomposition of scene flow into non-rigid flow and ego-motion flow along with an introduction of the self-supervisory signals allowed us to outperform the current state-of-the-art supervised methods.
Spatial-Temporal Block and LSTM Network for Pedestrian Trajectories Prediction
Authors: Xiong Dan
Date: 2020-09-22 00:00:00
Tasks: Autonomous Driving, Graph Embedding, Pedestrian Trajectory Prediction, Prediction, Trajectory Prediction

Pedestrian trajectory prediction is a critical to avoid autonomous driving collision. But this prediction is a challenging problem due to social forces and cluttered scenes. Such human-human and human-space interactions lead to many socially plausible trajectories. In this paper, we propose a novel LSTM-based algorithm. We tackle the problem by considering the static scene and pedestrian which combine the Graph Convolutional Networks and Temporal Convolutional Networks to extract features from pedestrians. Each pedestrian in the scene is regarded as a node, and we can obtain the relationship between each node and its neighborhoods by graph embedding. It is LSTM that encode the relationship so that our model predicts nodes trajectories in crowd scenarios simultaneously. To effectively predict multiple possible future trajectories, we further introduce Spatio-Temporal Convolutional Block to make the network flexible. Experimental results on two public datasets, i.e. ETH and UCY, demonstrate the effectiveness of our proposed ST-Block and we achieve state-of-the-art approaches in human trajectory prediction.
OpenREALM: Real-time Mapping for Unmanned Aerial Vehicles
Authors: Alexander Kern, Markus Bobbe, Yogesh Khedar, Ulf Bestmann
Date: 2020-09-22 00:00:00
Tasks: Position

This paper presents OpenREALM, a real-time mapping framework for Unmanned Aerial Vehicles (UAVs). A camera attached to the onboard computer of a moving UAV is utilized to acquire high resolution image mosaics of a targeted area of interest. Different modes of operation allow OpenREALM to perform simple stitching assuming an approximate plane ground, or to fully recover complex 3D surface information to extract both elevation maps and geometrically corrected orthophotos. Additionally, the global position of the UAV is used to georeference the data. In all modes incremental progress of the resulting map can be viewed live by an operator on the ground. Obtained, up-to-date surface information will be a push forward to a variety of UAV applications. For the benefit of the community, source code is public at https://github.com/laxnpander/OpenREALM.
A survey on Kornia: an Open Source Differentiable Computer Vision Library for PyTorch
Authors: E. Riba, D. Mishkin, J. Shi, D. Ponsa, F. Moreno-Noguer, G. Bradski
Date: 2020-09-21 00:00:00
Tasks: Camera Calibration, Data Augmentation, Edge Detection, Image Augmentation, Image Cropping

This work presents Kornia, an open source computer vision library built upon a set of differentiable routines and modules that aims to solve generic computer vision problems. The package uses PyTorch as its main backend, not only for efficiency but also to take advantage of the reverse auto-differentiation engine to define and compute the gradient of complex functions. Inspired by OpenCV, Kornia is composed of a set of modules containing operators that can be integrated into neural networks to train models to perform a wide range of operations including image transformations,camera calibration, epipolar geometry, and low level image processing techniques, such as filtering and edge detection that operate directly on high dimensional tensor representations on graphical processing units, generating faster systems. Examples of classical vision problems implemented using our framework are provided including a benchmark comparing to existing vision libraries.
Improving Point Cloud Semantic Segmentation by Learning 3D Object Detection
Authors: Ozan Unal, Luc van Gool, Dengxin Dai
Date: 2020-09-22 00:00:00
Tasks: 3D Object Detection, 3D Semantic Segmentation, Autonomous Driving, Collision Avoidance, object-detection, Object Detection, Object Proposal Generation, Segmentation, Semantic Segmentation

Point cloud semantic segmentation plays an essential role in autonomous driving, providing vital information about drivable surfaces and nearby objects that can aid higher level tasks such as path planning and collision avoidance. While current 3D semantic segmentation networks focus on convolutional architectures that perform great for well represented classes, they show a significant drop in performance for underrepresented classes that share similar geometric features. We propose a novel Detection Aware 3D Semantic Segmentation (DASS) framework that explicitly leverages localization features from an auxiliary 3D object detection task. By utilizing multitask training, the shared feature representation of the network is guided to be aware of per class detection features that aid tackling the differentiation of geometrically similar classes. We additionally provide a pipeline that uses DASS to generate high recall proposals for existing 2-stage detectors and demonstrate that the added supervisory signal can be used to improve 3D orientation estimation capabilities. Extensive experiments on both the SemanticKITTI and KITTI object datasets show that DASS can improve 3D semantic segmentation results of geometrically similar classes up to 37.8% IoU in image FOV while maintaining high precision bird's-eye view (BEV) detection results.
Heuristic Rank Selection with Progressively Searching Tensor Ring Network
Authors: Nannan Li, Yu Pan, Yaran Chen, Zixiang Ding, Dongbin Zhao, Zenglin Xu
Date: 2020-09-22 00:00:00

Recently, Tensor Ring Networks (TRNs) have been applied in deep networks, achieving remarkable successes in compression ratio and accuracy. Although highly related to the performance of TRNs, rank selection is seldom studied in previous works and usually set to equal in experiments. Meanwhile, there is not any heuristic method to choose the rank, and an enumerating way to find appropriate rank is extremely time-consuming. Interestingly, we discover that part of the rank elements is sensitive and usually aggregate in a narrow region, namely an interest region. Therefore, based on the above phenomenon, we propose a novel progressive genetic algorithm named Progressively Searching Tensor Ring Network Search (PSTRN), which has the ability to find optimal rank precisely and efficiently. Through the evolutionary phase and progressive phase, PSTRN can converge to the interest region quickly and harvest good performance. Experimental results show that PSTRN can significantly reduce the complexity of seeking rank, compared with the enumerating method. Furthermore, our method is validated on public benchmarks like MNIST, CIFAR10/100, UCF11 and HMDB51, achieving the state-of-the-art performance.
The Use of AI for Thermal Emotion Recognition: A Review of Problems and Limitations in Standard Design and Data
Authors: Catherine Ordun, Edward Raff, Sanjay Purushotham
Date: 2020-09-22 00:00:00
Tasks: BIG-bench Machine Learning, Emotion Recognition, Facial Emotion Recognition

With the increased attention on thermal imagery for Covid-19 screening, the public sector may believe there are new opportunities to exploit thermal as a modality for computer vision and AI. Thermal physiology research has been ongoing since the late nineties. This research lies at the intersections of medicine, psychology, machine learning, optics, and affective computing. We will review the known factors of thermal vs. RGB imaging for facial emotion recognition. But we also propose that thermal imagery may provide a semi-anonymous modality for computer vision, over RGB, which has been plagued by misuse in facial recognition. However, the transition to adopting thermal imagery as a source for any human-centered AI task is not easy and relies on the availability of high fidelity data sources across multiple demographics and thorough validation. This paper takes the reader on a short review of machine learning in thermal FER and the limitations of collecting and developing thermal FER data for AI training. Our motivation is to provide an introductory overview into recent advances for thermal FER and stimulate conversation about the limitations in current datasets.
Detection Of Concrete Cracks using Dual-channel Deep Convolutional Network
Authors: Babloo Kumar, Sayantari Ghosh
Date: 2020-09-22 00:00:00

Due to cyclic loading and fatigue stress cracks are generated, which affect the safety of any civil infrastructure. Nowadays machine vision is being used to assist us for appropriate maintenance, monitoring and inspection of concrete structures by partial replacement of human-conducted onsite inspections. The current study proposes a crack detection method based on deep convolutional neural network (CNN) for detection of concrete cracks without explicitly calculating the defect features. In the course of the study, a database of 3200 labelled images with concrete cracks has been created, where the contrast, lighting conditions, orientations and severity of the cracks were extremely variable. In this paper, starting from a deep CNN trained with these images of 256 x 256 pixel-resolution, we have gradually optimized the model by identifying the difficulties. Using an augmented dataset, which takes into account the variations and degradations compatible to drone videos, like, random zooming, rotation and intensity scaling and exhaustive ablation studies, we have designed a dual-channel deep CNN which shows high accuracy (~ 92.25%) as well as robustness in finding concrete cracks in realis-tic situations. The model has been tested on the basis of performance and analyzed with the help of feature maps, which establishes the importance of the dual-channel structure.
Curriculum Learning with Diversity for Supervised Computer Vision Tasks
Authors: Petru Soviany
Date: 2020-09-22 00:00:00
Proceeding: ICML Workshop LifelongML 2020 7
Tasks: Diversity, Instance Segmentation, object-detection, Object Detection, Semantic Segmentation

Curriculum learning techniques are a viable solution for improving the accuracy of automatic models, by replacing the traditional random training with an easy-to-hard strategy. However, the standard curriculum methodology does not automatically provide improved results, but it is constrained by multiple elements like the data distribution or the proposed model. In this paper, we introduce a novel curriculum sampling strategy which takes into consideration the diversity of the training data together with the difficulty of the inputs. We determine the difficulty using a state-of-the-art estimator based on the human time required for solving a visual search task. We consider this kind of difficulty metric to be better suited for solving general problems, as it is not based on certain task-dependent elements, but more on the context of each image. We ensure the diversity during training, giving higher priority to elements from less visited classes. We conduct object detection and instance segmentation experiments on Pascal VOC 2007 and Cityscapes data sets, surpassing both the randomly-trained baseline and the standard curriculum approach. We prove that our strategy is very efficient for unbalanced data sets, leading to faster convergence and more accurate results, when other curriculum-based strategies fail.