pwc-archive/papers-with-abstracts huggingface dataset:
BAF-Detector: An Efficient CNN-Based Detector for Photovoltaic Cell Defect Detection
Authors: Binyi Su, Haiyong Chen, Zhong Zhou
Date: 2020-12-19 00:00:00
Tasks: Defect Detection, Region Proposal

The multi-scale defect detection for photovoltaic (PV) cell electroluminescence (EL) images is a challenging task, due to the feature vanishing as network deepens. To address this problem, an attention-based top-down and bottom-up architecture is developed to accomplish multi-scale feature fusion. This architecture, called Bidirectional Attention Feature Pyramid Network (BAFPN), can make all layers of the pyramid share similar semantic features. In BAFPN, cosine similarity is employed to measure the importance of each pixel in the fused features. Furthermore, a novel object detector is proposed, called BAF-Detector, which embeds BAFPN into Region Proposal Network (RPN) in Faster RCNN+FPN. BAFPN improves the robustness of the network to scales, thus the proposed detector achieves a good performance in multi-scale defects detection task. Finally, the experimental results on a large-scale EL dataset including 3629 images, 2129 of which are defective, show that the proposed method achieves 98.70% (F-measure), 88.07% (mAP), and 73.29% (IoU) in terms of multi-scale defects classification and detection results in raw PV cell EL images.
The importance of silhouette optimization in 3D shape reconstruction system from multiple object scenes
Authors: Waqqas-ur-Rehman Butt, Martin Servin
Date: 2020-12-19 00:00:00
Tasks: 3D Shape Reconstruction

This paper presents a multi stage 3D shape reconstruction system of multiple object scenes by considering the silhouette inconsistencies in shape-from silhouette SFS method. These inconsistencies are common in multiple view images due to object occlusions in different views, segmentation and shadows or reflection due to objects or light directions. These factors raise huge challenges when attempting to construct the 3D shape by using existing approaches which reconstruct only that part of the volume which projects consistently in all the silhouettes, leaving the rest unreconstructed. As a result, final shape are not robust due to multi view objects occlusion and shadows. In this regard, we consider the primary factors affecting reconstruction by analyzing the multiple images and perform pre-processing steps to optimize the silhouettes. Finally, the 3D shape is reconstructed by using the volumetric approach SFS. Theory and experimental results show that, the performance of the modified algorithm was efficiently improved, which can improve the accuracy of the reconstructed shape and being robust to errors in the silhouettes, volume and computational inexpensive.
SMART Frame Selection for Action Recognition
Authors: Shreyank N Gowda, Marcus Rohrbach, Laura Sevilla-Lara
Date: 2020-12-19 00:00:00
Tasks: Action Recognition

Action recognition is computationally expensive. In this paper, we address the problem of frame selection to improve the accuracy of action recognition. In particular, we show that selecting good frames helps in action recognition performance even in the trimmed videos domain. Recent work has successfully leveraged frame selection for long, untrimmed videos, where much of the content is not relevant, and easy to discard. In this work, however, we focus on the more standard short, trimmed action recognition problem. We argue that good frame selection can not only reduce the computational cost of action recognition but also increase the accuracy by getting rid of frames that are hard to classify. In contrast to previous work, we propose a method that instead of selecting frames by considering one at a time, considers them jointly. This results in a more efficient selection, where good frames are more effectively distributed over the video, like snapshots that tell a story. We call the proposed frame selection SMART and we test it in combination with different backbone architectures and on multiple benchmarks (Kinetics, Something-something, UCF101). We show that the SMART frame selection consistently improves the accuracy compared to other frame selection strategies while reducing the computational cost by a factor of 4 to 10 times. Additionally, we show that when the primary goal is recognition performance, our selection strategy can improve over recent state-of-the-art models and frame selection strategies on various benchmarks (UCF101, HMDB51, FCVID, and ActivityNet).
Camera-aware Proxies for Unsupervised Person Re-Identification
Authors: Menglin Wang, Baisheng Lai, Jianqiang Huang, Xiaojin Gong, Xian-Sheng Hua
Date: 2020-12-19 00:00:00
Tasks: Clustering, Contrastive Learning, Person Re-Identification, Unsupervised Person Re-Identification

This paper tackles the purely unsupervised person re-identification (Re-ID) problem that requires no annotations. Some previous methods adopt clustering techniques to generate pseudo labels and use the produced labels to train Re-ID models progressively. These methods are relatively simple but effective. However, most clustering-based methods take each cluster as a pseudo identity class, neglecting the large intra-ID variance caused mainly by the change of camera views. To address this issue, we propose to split each single cluster into multiple proxies and each proxy represents the instances coming from the same camera. These camera-aware proxies enable us to deal with large intra-ID variance and generate more reliable pseudo labels for learning. Based on the camera-aware proxies, we design both intra- and inter-camera contrastive learning components for our Re-ID model to effectively learn the ID discrimination ability within and across cameras. Meanwhile, a proxy-balanced sampling strategy is also designed, which facilitates our learning further. Extensive experiments on three large-scale Re-ID datasets show that our proposed approach outperforms most unsupervised methods by a significant margin. Especially, on the challenging MSMT17 dataset, we gain $14.3\%$ Rank-1 and $10.2\%$ mAP improvements when compared to the second place. Code is available at: \texttt{https://github.com/Terminator8758/CAP-master}.
Self-supervised monocular depth estimation from oblique UAV videos
Authors: Logambal Madhuanand, Francesco Nex, Michael Ying Yang
Date: 2020-12-19 00:00:00
Tasks: 3D Reconstruction, Depth Estimation, Image Generation, Monocular Depth Estimation, object-detection, Object Detection, Self-Supervised Learning

UAVs have become an essential photogrammetric measurement as they are affordable, easily accessible and versatile. Aerial images captured from UAVs have applications in small and large scale texture mapping, 3D modelling, object detection tasks, DTM and DSM generation etc. Photogrammetric techniques are routinely used for 3D reconstruction from UAV images where multiple images of the same scene are acquired. Developments in computer vision and deep learning techniques have made Single Image Depth Estimation (SIDE) a field of intense research. Using SIDE techniques on UAV images can overcome the need for multiple images for 3D reconstruction. This paper aims to estimate depth from a single UAV aerial image using deep learning. We follow a self-supervised learning approach, Self-Supervised Monocular Depth Estimation (SMDE), which does not need ground truth depth or any extra information other than images for learning to estimate depth. Monocular video frames are used for training the deep learning model which learns depth and pose information jointly through two different networks, one each for depth and pose. The predicted depth and pose are used to reconstruct one image from the viewpoint of another image utilising the temporal information from videos. We propose a novel architecture with two 2D CNN encoders and a 3D CNN decoder for extracting information from consecutive temporal frames. A contrastive loss term is introduced for improving the quality of image generation. Our experiments are carried out on the public UAVid video dataset. The experimental results demonstrate that our model outperforms the state-of-the-art methods in estimating the depths.
Static object detection and segmentation in videos based on dual foregrounds difference with noise filtering
Authors: Waqqas-ur-Rehman Butt, Martin Servin
Date: 2020-12-19 00:00:00
Tasks: Object, object-detection, Object Detection

This paper presents static object detection and segmentation method in videos from cluttered scenes. Robust static object detection is still challenging task due to presence of moving objects in many surveillance applications. The level of difficulty is extremely influenced by on how you label the object to be identified as static that do not establish the original background but appeared in the video at different time. In this context, background subtraction technique based on the frame difference concept is applied to the identification of static objects. Firstly, we estimate a frame differencing foreground mask image by computing the difference of each frame with respect to a static reference frame. The Mixture of Gaussian MOG method is applied to detect the moving particles and then outcome foreground mask is subtracted from frame differencing foreground mask. Pre-processing techniques, illumination equalization and de-hazing methods are applied to handle low contrast and to reduce the noise from scattered materials in the air e.g. water droplets and dust particles. Finally, a set of mathematical morphological operation and largest connected-component analysis is applied to segment the object and suppress the noise. The proposed method was built for rock breaker station application and effectively validated with real, synthetic and two public data sets. The results demonstrate the proposed approach can robustly detect, segmented the static objects without any prior information of tracking.
Political Posters Identification with Appearance-Text Fusion
Authors: Xuan Qin, Meizhu Liu, Yifan Hu, Christina Moo, Christian M. Riblet, Changwei Hu, Kevin Yen, Haibin Ling
Date: 2020-12-19 00:00:00

In this paper, we propose a method that efficiently utilizes appearance features and text vectors to accurately classify political posters from other similar political images. The majority of this work focuses on political posters that are designed to serve as a promotion of a certain political event, and the automated identification of which can lead to the generation of detailed statistics and meets the judgment needs in a variety of areas. Starting with a comprehensive keyword list for politicians and political events, we curate for the first time an effective and practical political poster dataset containing 13K human-labeled political images, including 3K political posters that explicitly support a movement or a campaign. Second, we make a thorough case study for this dataset and analyze common patterns and outliers of political posters. Finally, we propose a model that combines the power of both appearance and text information to classify political posters with significantly high accuracy.
GlocalNet: Class-aware Long-term Human Motion Synthesis
Authors: Neeraj Battan, Yudhik Agrawal, Veeravalli Saisooryarao, Aman Goel, Avinash Sharma
Date: 2020-12-19 00:00:00
Tasks: Motion Synthesis, Pedestrian Trajectory Prediction, Trajectory Prediction, Video Generation

Synthesis of long-term human motion skeleton sequences is essential to aid human-centric video generation with potential applications in Augmented Reality, 3D character animations, pedestrian trajectory prediction, etc. Long-term human motion synthesis is a challenging task due to multiple factors like, long-term temporal dependencies among poses, cyclic repetition across poses, bi-directional and multi-scale dependencies among poses, variable speed of actions, and a large as well as partially overlapping space of temporal pose variations across multiple class/types of human activities. This paper aims to address these challenges to synthesize a long-term (> 6000 ms) human motion trajectory across a large variety of human activity classes (>50). We propose a two-stage activity generation method to achieve this goal, where the first stage deals with learning the long-term global pose dependencies in activity sequences by learning to synthesize a sparse motion trajectory while the second stage addresses the generation of dense motion trajectories taking the output of the first stage. We demonstrate the superiority of the proposed method over SOTA methods using various quantitative evaluation metrics on publicly available datasets.
Three Ways to Improve Semantic Segmentation with Self-Supervised Depth Estimation
Authors: Lukas Hoyer, Dengxin Dai, Yuhua Chen, Adrian Köring, Suman Saha, Luc van Gool
Date: 2020-12-19 00:00:00
Proceeding: CVPR 2021 1
Tasks: Data Augmentation, Depth Estimation, Diversity, Monocular Depth Estimation, Segmentation, Semantic Segmentation, Semi-Supervised Semantic Segmentation

Training deep networks for semantic segmentation requires large amounts of labeled training data, which presents a major challenge in practice, as labeling segmentation masks is a highly labor-intensive process. To address this issue, we present a framework for semi-supervised semantic segmentation, which is enhanced by self-supervised monocular depth estimation from unlabeled image sequences. In particular, we propose three key contributions: (1) We transfer knowledge from features learned during self-supervised depth estimation to semantic segmentation, (2) we implement a strong data augmentation by blending images and labels using the geometry of the scene, and (3) we utilize the depth feature diversity as well as the level of difficulty of learning depth in a student-teacher framework to select the most useful samples to be annotated for semantic segmentation. We validate the proposed model on the Cityscapes dataset, where all three modules demonstrate significant performance gains, and we achieve state-of-the-art results for semi-supervised semantic segmentation. The implementation is available at https://github.com/lhoyer/improving_segmentation_with_selfsupervised_depth.
Rethinking Road Surface 3D Reconstruction and Pothole Detection: From Perspective Transformation to Disparity Map Segmentation
Authors: Rui Fan, Umar Ozgunalp, YuAn Wang, Ming Liu, Ioannis Pitas
Date: 2020-12-19 00:00:00
Tasks: 3D Reconstruction, Clustering, GPU, Superpixels

Potholes are one of the most common forms of road damage, which can severely affect driving comfort, road safety and vehicle condition. Pothole detection is typically performed by either structural engineers or certified inspectors. This task is, however, not only hazardous for the personnel but also extremely time-consuming. This paper presents an efficient pothole detection algorithm based on road disparity map estimation and segmentation. We first generalize the perspective transformation by incorporating the stereo rig roll angle. The road disparities are then estimated using semi-global matching. A disparity map transformation algorithm is then performed to better distinguish the damaged road areas. Finally, we utilize simple linear iterative clustering to group the transformed disparities into a collection of superpixels. The potholes are then detected by finding the superpixels, whose values are lower than an adaptively determined threshold. The proposed algorithm is implemented on an NVIDIA RTX 2080 Ti GPU in CUDA. The experiments demonstrate the accuracy and efficiency of our proposed road pothole detection algorithm, where an accuracy of 99.6% and an F-score of 89.4% are achieved.
Quantum Optical Convolutional Neural Network: A Novel Image Recognition Framework for Quantum Computing
Authors: Rishab Parthasarathy, Rohan Bhowmik
Date: 2020-12-19 00:00:00
Tasks: Self-Driving Cars

Large machine learning models based on Convolutional Neural Networks (CNNs) with rapidly increasing number of parameters, trained with massive amounts of data, are being deployed in a wide array of computer vision tasks from self-driving cars to medical imaging. The insatiable demand for computing resources required to train these models is fast outpacing the advancement of classical computing hardware, and new frameworks including Optical Neural Networks (ONNs) and quantum computing are being explored as future alternatives. In this work, we report a novel quantum computing based deep learning model, the Quantum Optical Convolutional Neural Network (QOCNN), to alleviate the computational bottleneck in future computer vision applications. Using the popular MNIST dataset, we have benchmarked this new architecture against a traditional CNN based on the seminal LeNet model. We have also compared the performance with previously reported ONNs, namely the GridNet and ComplexNet, as well as a Quantum Optical Neural Network (QONN) that we built by combining the ComplexNet with quantum based sinusoidal nonlinearities. In essence, our work extends the prior research on QONN by adding quantum convolution and pooling layers preceding it. We have evaluated all the models by determining their accuracies, confusion matrices, Receiver Operating Characteristic (ROC) curves, and Matthews Correlation Coefficients. The performance of the models were similar overall, and the ROC curves indicated that the new QOCNN model is robust. Finally, we estimated the gains in computational efficiencies from executing this novel framework on a quantum computer. We conclude that switching to a quantum computing based approach to deep learning may result in comparable accuracies to classical models, while achieving unprecedented boosts in computational performances and drastic reduction in power consumption.
Transductive Visual Verb Sense Disambiguation
Authors: Sebastiano Vascon, Sinem Aslan, Gianluca Bigaglia, Lorenzo Giudice, Marcello Pelillo
Date: 2020-12-20 00:00:00
Tasks: Sentence

Verb Sense Disambiguation is a well-known task in NLP, the aim is to find the correct sense of a verb in a sentence. Recently, this problem has been extended in a multimodal scenario, by exploiting both textual and visual features of ambiguous verbs leading to a new problem, the Visual Verb Sense Disambiguation (VVSD). Here, the sense of a verb is assigned considering the content of an image paired with it rather than a sentence in which the verb appears. Annotating a dataset for this task is more complex than textual disambiguation, because assigning the correct sense to a pair of $<$image, verb$>$ requires both non-trivial linguistic and visual skills. In this work, differently from the literature, the VVSD task will be performed in a transductive semi-supervised learning (SSL) setting, in which only a small amount of labeled information is required, reducing tremendously the need for annotated data. The disambiguation process is based on a graph-based label propagation method which takes into account mono or multimodal representations for $<$image, verb$>$ pairs. Experiments have been carried out on the recently published dataset VerSe, the only available dataset for this task. The achieved results outperform the current state-of-the-art by a large margin while using only a small fraction of labeled samples per sense. Code available: https://github.com/GiBg1aN/TVVSD.
PTN: A Poisson Transfer Network for Semi-supervised Few-shot Learning
Authors: Huaxi Huang, Junjie Zhang, Jian Zhang, Qiang Wu, Chang Xu
Date: 2020-12-20 00:00:00
Tasks: Contrastive Learning, Few-Shot Learning

The predicament in semi-supervised few-shot learning (SSFSL) is to maximize the value of the extra unlabeled data to boost the few-shot learner. In this paper, we propose a Poisson Transfer Network (PTN) to mine the unlabeled information for SSFSL from two aspects. First, the Poisson Merriman Bence Osher (MBO) model builds a bridge for the communications between labeled and unlabeled examples. This model serves as a more stable and informative classifier than traditional graph-based SSFSL methods in the message-passing process of the labels. Second, the extra unlabeled samples are employed to transfer the knowledge from base classes to novel classes through contrastive learning. Specifically, we force the augmented positive pairs close while push the negative ones distant. Our contrastive transfer scheme implicitly learns the novel-class embeddings to alleviate the over-fitting problem on the few labeled data. Thus, we can mitigate the degeneration of embedding generality in novel classes. Extensive experiments indicate that PTN outperforms the state-of-the-art few-shot and SSFSL models on miniImageNet and tieredImageNet benchmark datasets.
Geometric Scene Refocusing
Authors: Parikshit Sakurikar, P. J. Narayanan
Date: 2020-12-20 00:00:00

An image captured with a wide-aperture camera exhibits a finite depth-of-field, with focused and defocused pixels. A compact and robust representation of focus and defocus helps analyze and manipulate such images. In this work, we study the fine characteristics of images with a shallow depth-of-field in the context of focal stacks. We present a composite measure for focus that is a combination of existing measures. We identify in-focus pixels, dual-focus pixels, pixels that exhibit bokeh and spatially-varying blur kernels between focal slices. We use these to build a novel representation that facilitates easy manipulation of focal stacks. We present a comprehensive algorithm for post-capture refocusing in a geometrically correct manner. Our approach can refocus the scene at high fidelity while preserving fine aspects of focus and defocus blur.
Anchor-Based Spatio-Temporal Attention 3D Convolutional Networks for Dynamic 3D Point Cloud Sequences
Authors: Guangming Wang, Muyao Chen, Hanwen Liu, Yehui Yang, Zhe Liu, Hesheng Wang
Date: 2020-12-20 00:00:00
Tasks: Action Recognition, Autonomous Driving, Semantic Segmentation

With the rapid development of measurement technology, LiDAR and depth cameras are widely used in the perception of the 3D environment. Recent learning based methods for robot perception most focus on the image or video, but deep learning methods for dynamic 3D point cloud sequences are underexplored. Therefore, developing efficient and accurate perception method compatible with these advanced instruments is pivotal to autonomous driving and service robots. An Anchor-based Spatio-Temporal Attention 3D Convolution operation (ASTA3DConv) is proposed in this paper to process dynamic 3D point cloud sequences. The proposed convolution operation builds a regular receptive field around each point by setting several virtual anchors around each point. The features of neighborhood points are firstly aggregated to each anchor based on the spatio-temporal attention mechanism. Then, anchor-based 3D convolution is adopted to aggregate these anchors' features to the core points. The proposed method makes better use of the structured information within the local region and learns spatio-temporal embedding features from dynamic 3D point cloud sequences. Anchor-based Spatio-Temporal Attention 3D Convolutional Neural Networks (ASTA3DCNNs) are built for classification and segmentation tasks based on the proposed ASTA3DConv and evaluated on action recognition and semantic segmentation tasks. The experiments and ablation studies on MSRAction3D and Synthia datasets demonstrate the superior performance and effectiveness of our method for dynamic 3D point cloud sequences. Our method achieves the state-of-the-art performance among the methods with dynamic 3D point cloud sequences as input on MSRAction3D and Synthia datasets.