pwc-archive/papers-with-abstracts huggingface dataset:
Learning Video Salient Object Detection Progressively from Unlabeled Videos
Authors: Binwei Xu, Haoran Liang, Wentian Ni, Weihua Gong, Ronghua Liang, Peng Chen
Date: 2022-04-05 00:00:00
Tasks: Object, object-detection, Object Detection, Optical Flow Estimation, Salient Object Detection, Video Salient Object Detection

Recent deep learning-based video salient object detection (VSOD) has achieved some breakthrough, but these methods rely on expensive annotated videos with pixel-wise annotations, weak annotations, or part of the pixel-wise annotations. In this paper, based on the similarities and the differences between VSOD and image salient object detection (SOD), we propose a novel VSOD method via a progressive framework that locates and segments salient objects in sequence without utilizing any video annotation. To use the knowledge learned in the SOD dataset for VSOD efficiently, we introduce dynamic saliency to compensate for the lack of motion information of SOD during the locating process but retain the same fine segmenting process. Specifically, an algorithm for generating spatiotemporal location labels, which consists of generating high-saliency location labels and tracking salient objects in adjacent frames, is proposed. Based on these location labels, a two-stream locating network that introduces an optical flow branch for video salient object locating is presented. Although our method does not require labeled video at all, the experimental results on five public benchmarks of DAVIS, FBMS, ViSal, VOS, and DAVSOD demonstrate that our proposed method is competitive with fully supervised methods and outperforms the state-of-the-art weakly and unsupervised methods.
Bimodal Distributed Binarized Neural Networks
Authors: Tal Rozen, Moshe Kimhi, Brian Chmiel, Avi Mendelson, Chaim Baskin
Date: 2022-04-05 00:00:00
Tasks: Binarization, Quantization

Binary Neural Networks (BNNs) are an extremely promising method to reduce deep neural networks' complexity and power consumption massively. Binarization techniques, however, suffer from ineligible performance degradation compared to their full-precision counterparts. Prior work mainly focused on strategies for sign function approximation during forward and backward phases to reduce the quantization error during the binarization process. In this work, we propose a Bi-Modal Distributed binarization method (\methodname{}). That imposes bi-modal distribution of the network weights by kurtosis regularization. The proposed method consists of a training scheme that we call Weight Distribution Mimicking (WDM), which efficiently imitates the full-precision network weight distribution to their binary counterpart. Preserving this distribution during binarization-aware training creates robust and informative binary feature maps and significantly reduces the generalization error of the BNN. Extensive evaluations on CIFAR-10 and ImageNet demonstrate the superiority of our method over current state-of-the-art schemes. Our source code, experimental settings, training logs, and binary models are available at \url{https://github.com/BlueAnon/BD-BNN}.
Multi-Weight Respecification of Scan-specific Learning for Parallel Imaging
Authors: Hui Tao, Haifeng Wang, Shanshan Wang, Dong Liang, Xiaoling Xu, Qiegen Liu
Date: 2022-04-05 00:00:00

Parallel imaging is widely used in magnetic resonance imaging as an acceleration technology. Traditional linear reconstruction methods in parallel imaging often suffer from noise amplification. Recently, a non-linear robust artificial-neural-network for k-space interpolation (RAKI) exhibits superior noise resilience over other linear methods. However, RAKI performs poorly at high acceleration rates, and needs a large amount of autocalibration signals as the training samples. In order to tackle these issues, we propose a multi-weight method that implements multiple weighting matrices on the undersampled data, named as MW-RAKI. Enforcing multiple weighted matrices on the measurements can effectively reduce the influence of noise and increase the data constraints. Furthermore, we incorporate the strategy of multiple weighting matrixes into a residual version of RAKI, and form MW-rRAKI.Experimental compari-sons with the alternative methods demonstrated noticeably better reconstruction performances, particularly at high acceleration rates.
Audio-visual multi-channel speech separation, dereverberation and recognition
Authors: Guinan Li, Jianwei Yu, Jiajun Deng, Xunying Liu, Helen Meng
Date: 2022-04-05 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Speech Enhancement, speech-recognition, Speech Recognition, Speech Separation

Despite the rapid advance of automatic speech recognition (ASR) technologies, accurate recognition of cocktail party speech characterised by the interference from overlapping speakers, background noise and room reverberation remains a highly challenging task to date. Motivated by the invariance of visual modality to acoustic signal corruption, audio-visual speech enhancement techniques have been developed, although predominantly targeting overlapping speech separation and recognition tasks. In this paper, an audio-visual multi-channel speech separation, dereverberation and recognition approach featuring a full incorporation of visual information into all three stages of the system is proposed. The advantage of the additional visual modality over using audio only is demonstrated on two neural dereverberation approaches based on DNN-WPE and spectral mapping respectively. The learning cost function mismatch between the separation and dereverberation models and their integration with the back-end recognition system is minimised using fine-tuning on the MSE and LF-MMI criteria. Experiments conducted on the LRS2 dataset suggest that the proposed audio-visual multi-channel speech separation, dereverberation and recognition system outperforms the baseline audio-visual multi-channel speech separation and recognition system containing no dereverberation module by a statistically significant word error rate (WER) reduction of 2.06% absolute (8.77% relative).
Region Rebalance for Long-Tailed Semantic Segmentation
Authors: Jiequan Cui, Yuhui Yuan, Zhisheng Zhong, Zhuotao Tian, Han Hu, Stephen Lin, Jiaya Jia
Date: 2022-04-05 00:00:00
Tasks: Segmentation, Semantic Segmentation

In this paper, we study the problem of class imbalance in semantic segmentation. We first investigate and identify the main challenges of addressing this issue through pixel rebalance. Then a simple and yet effective region rebalance scheme is derived based on our analysis. In our solution, pixel features belonging to the same class are grouped into region features, and a rebalanced region classifier is applied via an auxiliary region rebalance branch during training. To verify the flexibility and effectiveness of our method, we apply the region rebalance module into various semantic segmentation methods, such as Deeplabv3+, OCRNet, and Swin. Our strategy achieves consistent improvement on the challenging ADE20K and COCO-Stuff benchmark. In particular, with the proposed region rebalance scheme, state-of-the-art BEiT receives +0.7% gain in terms of mIoU on the ADE20K val set.
PSDoodle: Searching for App Screens via Interactive Sketching
Authors: Soumik Mohian, Christoph Csallner
Date: 2022-04-05 00:00:00

Keyword-based mobile screen search does not account for screen content and fails to operate as a universal tool for all levels of users. Visual searching (e.g., image, sketch) is structured and easy to adopt. Current visual search approaches count on a complete screen and are therefore slow and tedious. PSDoodle employs a deep neural network to recognize partial screen element drawings instantly on a digital drawing interface and shows results in real-time. PSDoodle is the first tool that utilizes partial sketches and searches for screens in an interactive iterative way. PSDoodle supports different drawing styles and retrieves search results that are relevant to the user's sketch query. A short video demonstration is available online at: https://youtu.be/3cVLHFm5pY4
Controllable Garment Transfer
Authors: Jooeun Son, Tomas Cabezon Pedroso, Carolene Siga, Jinsung Lee
Date: 2022-04-05 00:00:00

Image-based garment transfer replaces the garment on the target human with the desired garment; this enables users to virtually view themselves in the desired garment. To this end, many approaches have been proposed using the generative model and have shown promising results. However, most fail to provide the user with on the fly garment modification functionality. We aim to add this customizable option of "garment tweaking" to our model to control garment attributes, such as sleeve length, waist width, and garment texture.
Autoregressive 3D Shape Generation via Canonical Mapping
Authors: An-Chieh Cheng, Xueting Li, Sifei Liu, Min Sun, Ming-Hsuan Yang
Date: 2022-04-05 00:00:00
Tasks: 3D Shape Generation, Point Cloud Generation, Point cloud reconstruction

With the capacity of modeling long-range dependencies in sequential data, transformers have shown remarkable performances in a variety of generative tasks such as image, audio, and text generation. Yet, taming them in generating less structured and voluminous data formats such as high-resolution point clouds have seldom been explored due to ambiguous sequentialization processes and infeasible computation burden. In this paper, we aim to further exploit the power of transformers and employ them for the task of 3D point cloud generation. The key idea is to decompose point clouds of one category into semantically aligned sequences of shape compositions, via a learned canonical space. These shape compositions can then be quantized and used to learn a context-rich composition codebook for point cloud generation. Experimental results on point cloud reconstruction and unconditional generation show that our model performs favorably against state-of-the-art approaches. Furthermore, our model can be easily extended to multi-modal shape completion as an application for conditional shape generation.
Towards On-Board Panoptic Segmentation of Multispectral Satellite Images
Authors: Tharindu Fernando, Clinton Fookes, Harshala Gammulle, Simon Denman, Sridha Sridharan
Date: 2022-04-05 00:00:00
Tasks: Knowledge Distillation, Panoptic Segmentation, Segmentation

With tremendous advancements in low-power embedded computing devices and remote sensing instruments, the traditional satellite image processing pipeline which includes an expensive data transfer step prior to processing data on the ground is being replaced by on-board processing of captured data. This paradigm shift enables critical and time-sensitive analytic intelligence to be acquired in a timely manner on-board the satellite itself. However, at present, the on-board processing of multi-spectral satellite images is limited to classification and segmentation tasks. Extending this processing to its next logical level, in this paper we propose a lightweight pipeline for on-board panoptic segmentation of multi-spectral satellite images. Panoptic segmentation offers major economic and environmental insights, ranging from yield estimation from agricultural lands to intelligence for complex military applications. Nevertheless, the on-board intelligence extraction raises several challenges due to the loss of temporal observations and the need to generate predictions from a single image sample. To address this challenge, we propose a multimodal teacher network based on a cross-modality attention-based fusion strategy to improve the segmentation accuracy by exploiting data from multiple modes. We also propose an online knowledge distillation framework to transfer the knowledge learned by this multi-modal teacher network to a uni-modal student which receives only a single frame input, and is more appropriate for an on-board environment. We benchmark our approach against existing state-of-the-art panoptic segmentation models using the PASTIS multi-spectral panoptic segmentation dataset considering an on-board processing setting. Our evaluations demonstrate a substantial increase in accuracy metrics compared to the existing state-of-the-art models.
Unified Implicit Neural Stylization
Authors: Zhiwen Fan, Yifan Jiang, Peihao Wang, Xinyu Gong, Dejia Xu, Zhangyang Wang
Date: 2022-04-05 00:00:00
Tasks: Neural Stylization, Novel View Synthesis

Representing visual signals by implicit representation (e.g., a coordinate based deep network) has prevailed among many vision tasks. This work explores a new intriguing direction: training a stylized implicit representation, using a generalized approach that can apply to various 2D and 3D scenarios. We conduct a pilot study on a variety of implicit functions, including 2D coordinate-based representation, neural radiance field, and signed distance function. Our solution is a Unified Implicit Neural Stylization framework, dubbed INS. In contrary to vanilla implicit representation, INS decouples the ordinary implicit function into a style implicit module and a content implicit module, in order to separately encode the representations from the style image and input scenes. An amalgamation module is then applied to aggregate these information and synthesize the stylized output. To regularize the geometry in 3D scenes, we propose a novel self-distillation geometry consistency loss which preserves the geometry fidelity of the stylized scenes. Comprehensive experiments are conducted on multiple task settings, including novel view synthesis of complex scenes, stylization for implicit surfaces, and fitting images using MLPs. We further demonstrate that the learned representation is continuous not only spatially but also style-wise, leading to effortlessly interpolating between different styles and generating images with new mixed styles. Please refer to the video on our project page for more view synthesis results: https://zhiwenfan.github.io/INS.
Attention Distraction: Watermark Removal Through Continual Learning with Selective Forgetting
Authors: Qi Zhong, Leo Yu Zhang, Shengshan Hu, Longxiang Gao, Jun Zhang, Yong Xiang
Date: 2022-04-05 00:00:00
Tasks: Continual Learning

Fine-tuning attacks are effective in removing the embedded watermarks in deep learning models. However, when the source data is unavailable, it is challenging to just erase the watermark without jeopardizing the model performance. In this context, we introduce Attention Distraction (AD), a novel source data-free watermark removal attack, to make the model selectively forget the embedded watermarks by customizing continual learning. In particular, AD first anchors the model's attention on the main task using some unlabeled data. Then, through continual learning, a small number of \textit{lures} (randomly selected natural images) that are assigned a new label distract the model's attention away from the watermarks. Experimental results from different datasets and networks corroborate that AD can thoroughly remove the watermark with a small resource budget without compromising the model's performance on the main task, which outperforms the state-of-the-art works.
High-Quality Pluralistic Image Completion via Code Shared VQGAN
Authors: Chuanxia Zheng, Guoxian Song, Tat-Jen Cham, Jianfei Cai, Dinh Phung, Linjie Luo
Date: 2022-04-05 00:00:00
Tasks: Diversity, Image Reconstruction, Vocal Bursts Intensity Prediction

PICNet pioneered the generation of multiple and diverse results for image completion task, but it required a careful balance between $\mathcal{KL}$ loss (diversity) and reconstruction loss (quality), resulting in a limited diversity and quality . Separately, iGPT-based architecture has been employed to infer distributions in a discrete space derived from a pixel-level pre-clustered palette, which however cannot generate high-quality results directly. In this work, we present a novel framework for pluralistic image completion that can achieve both high quality and diversity at much faster inference speed. The core of our design lies in a simple yet effective code sharing mechanism that leads to a very compact yet expressive image representation in a discrete latent domain. The compactness and the richness of the representation further facilitate the subsequent deployment of a transformer to effectively learn how to composite and complete a masked image at the discrete code domain. Based on the global context well-captured by the transformer and the available visual regions, we are able to sample all tokens simultaneously, which is completely different from the prevailing autoregressive approach of iGPT-based works, and leads to more than 100$\times$ faster inference speed. Experiments show that our framework is able to learn semantically-rich discrete codes efficiently and robustly, resulting in much better image reconstruction quality. Our diverse image completion framework significantly outperforms the state-of-the-art both quantitatively and qualitatively on multiple benchmark datasets.
Text Spotting Transformers
Authors: Xiang Zhang, Yongwen Su, Subarna Tripathi, Zhuowen Tu
Date: 2022-04-05 00:00:00
Proceeding: CVPR 2022 1
Tasks: Text Detection, Text Spotting

In this paper, we present TExt Spotting TRansformers (TESTR), a generic end-to-end text spotting framework using Transformers for text detection and recognition in the wild. TESTR builds upon a single encoder and dual decoders for the joint text-box control point regression and character recognition. Other than most existing literature, our method is free from Region-of-Interest operations and heuristics-driven post-processing procedures; TESTR is particularly effective when dealing with curved text-boxes where special cares are needed for the adaptation of the traditional bounding-box representations. We show our canonical representation of control points suitable for text instances in both Bezier curve and polygon annotations. In addition, we design a bounding-box guided polygon detection (box-to-polygon) process. Experiments on curved and arbitrarily shaped datasets demonstrate state-of-the-art performances of the proposed TESTR algorithm.
An Exploration of Active Learning for Affective Digital Phenotyping
Authors: Peter Washington, Cezmi Mutlu, Aaron Kline, Cathy Hou, Kaitlyn Dunlap, Jack Kent, Arman Husic, Nate Stockham, Brianna Chrisman, Kelley Paskov, Jae-Yoon Jung, Dennis P. Wall
Date: 2022-04-05 00:00:00
Tasks: Active Learning

Some of the most severe bottlenecks preventing widespread development of machine learning models for human behavior include a dearth of labeled training data and difficulty of acquiring high quality labels. Active learning is a paradigm for using algorithms to computationally select a useful subset of data points to label using metrics for model uncertainty and data similarity. We explore active learning for naturalistic computer vision emotion data, a particularly heterogeneous and complex data space due to inherently subjective labels. Using frames collected from gameplay acquired from a therapeutic smartphone game for children with autism, we run a simulation of active learning using gameplay prompts as metadata to aid in the active learning process. We find that active learning using information generated during gameplay slightly outperforms random selection of the same number of labeled frames. We next investigate a method to conduct active learning with subjective data, such as in affective computing, and where multiple crowdsourced labels can be acquired for each image. Using the Child Affective Facial Expression (CAFE) dataset, we simulate an active learning process for crowdsourcing many labels and find that prioritizing frames using the entropy of the crowdsourced label distribution results in lower categorical cross-entropy loss compared to random frame selection. Collectively, these results demonstrate pilot evaluations of two novel active learning approaches for subjective affective data collected in noisy settings.
MonoTrack: Shuttle trajectory reconstruction from monocular badminton video
Authors: Paul Liu, Jui-Hsien Wang
Date: 2022-04-04 00:00:00
Tasks: 3D Reconstruction, Sports Ball Detection and Tracking

Trajectory estimation is a fundamental component of racket sport analytics, as the trajectory contains information not only about the winning and losing of each point, but also how it was won or lost. In sports such as badminton, players benefit from knowing the full 3D trajectory, as the height of shuttlecock or ball provides valuable tactical information. Unfortunately, 3D reconstruction is a notoriously hard problem, and standard trajectory estimators can only track 2D pixel coordinates. In this work, we present the first complete end-to-end system for the extraction and segmentation of 3D shuttle trajectories from monocular badminton videos. Our system integrates badminton domain knowledge such as court dimension, shot placement, physical laws of motion, along with vision-based features such as player poses and shuttle tracking. We find that significant engineering efforts and model improvements are needed to make the overall system robust, and as a by-product of our work, improve state-of-the-art results on court recognition, 2D trajectory estimation, and hit recognition.
Truck Axle Detection with Convolutional Neural Networks
Authors: Leandro Arab Marcomini, André Luiz Cunha
Date: 2022-04-04 00:00:00
Tasks: object-detection, Object Detection

Axle count in trucks is important to the classification of vehicles and to the operation of road systems. It is used in the determination of service fees and in the impact on the pavement. Although axle count can be achieved with traditional methods, such as manual labor, it is increasingly possible to count axles using deep learning and computer vision methods. This paper aims to compare three deep-learning object detection algorithms, YOLO, Faster R-CNN, and SSD, for the detection of truck axles. A dataset was built to provide training and testing examples for the neural networks. The training was done on different base models, to increase training time efficiency and to compare results. We evaluated results based on five metrics: precision, recall, mAP, F1-score, and FPS count. Results indicate that YOLO and SSD have similar accuracy and performance, with more than 96\% mAP for both models. Datasets and codes are publicly available for download.