pwc-archive/papers-with-abstracts huggingface dataset:
MedFuse: Multi-modal fusion with clinical time-series data and chest X-ray images
Authors: Nasir Hayat, Krzysztof J. Geras, Farah E. Shamout
Date: 2022-07-14 00:00:00
Tasks: Mortality Prediction, Phenotype classification, Time Series

Multi-modal fusion approaches aim to integrate information from different data sources. Unlike natural datasets, such as in audio-visual applications, where samples consist of "paired" modalities, data in healthcare is often collected asynchronously. Hence, requiring the presence of all modalities for a given sample is not realistic for clinical tasks and significantly limits the size of the dataset during training. In this paper, we propose MedFuse, a conceptually simple yet promising LSTM-based fusion module that can accommodate uni-modal as well as multi-modal input. We evaluate the fusion method and introduce new benchmark results for in-hospital mortality prediction and phenotype classification, using clinical time-series data in the MIMIC-IV dataset and corresponding chest X-ray images in MIMIC-CXR. Compared to more complex multi-modal fusion strategies, MedFuse provides a performance improvement by a large margin on the fully paired test set. It also remains robust across the partially paired test set containing samples with missing chest X-ray images. We release our code for reproducibility and to enable the evaluation of competing models in the future.
Accurate Ground-Truth Depth Image Generation via Overfit Training of Point Cloud Registration using Local Frame Sets
Authors: Jiwan Kim, Minchang Kim, Yeong-Gil Shin, Minyoung Chung
Date: 2022-07-14 00:00:00
Tasks: Image Generation, Point Cloud Registration

Accurate three-dimensional perception is a fundamental task in several computer vision applications. Recently, commercial RGB-depth (RGB-D) cameras have been widely adopted as single-view depth-sensing devices owing to their efficient depth-sensing abilities. However, the depth quality of most RGB-D sensors remains insufficient owing to the inherent noise from a single-view environment. Recently, several studies have focused on the single-view depth enhancement of RGB-D cameras. Recent research has proposed deep-learning-based approaches that typically train networks using high-quality supervised depth datasets, which indicates that the quality of the ground-truth (GT) depth dataset is a top-most important factor for accurate system; however, such high-quality GT datasets are difficult to obtain. In this study, we developed a novel method for high-quality GT depth generation based on an RGB-D stream dataset. First, we defined consecutive depth frames in a local spatial region as a local frame set. Then, the depth frames were aligned to a certain frame in the local frame set using an unsupervised point cloud registration scheme. The registration parameters were trained based on an overfit-training scheme, which was primarily used to construct a single GT depth image for each frame set. The final GT depth dataset was constructed using several local frame sets, and each local frame set was trained independently. The primary advantage of this study is that a high-quality GT depth dataset can be constructed under various scanning environments using only the RGB-D stream dataset. Moreover, our proposed method can be used as a new benchmark GT dataset for accurate performance evaluations. We evaluated our GT dataset on previously benchmarked GT depth datasets and demonstrated that our method is superior to state-of-the-art depth enhancement frameworks.
ObjectBox: From Centers to Boxes for Anchor-Free Object Detection
Authors: Mohsen Zand, Ali Etemad, Michael Greenspan
Date: 2022-07-14 00:00:00
Tasks: Object, object-detection, Object Detection

We present ObjectBox, a novel single-stage anchor-free and highly generalizable object detection approach. As opposed to both existing anchor-based and anchor-free detectors, which are more biased toward specific object scales in their label assignments, we use only object center locations as positive samples and treat all objects equally in different feature levels regardless of the objects' sizes or shapes. Specifically, our label assignment strategy considers the object center locations as shape- and size-agnostic anchors in an anchor-free fashion, and allows learning to occur at all scales for every object. To support this, we define new regression targets as the distances from two corners of the center cell location to the four sides of the bounding box. Moreover, to handle scale-variant objects, we propose a tailored IoU loss to deal with boxes with different sizes. As a result, our proposed object detector does not need any dataset-dependent hyperparameters to be tuned across datasets. We evaluate our method on MS-COCO 2017 and PASCAL VOC 2012 datasets, and compare our results to state-of-the-art methods. We observe that ObjectBox performs favorably in comparison to prior works. Furthermore, we perform rigorous ablation experiments to evaluate different components of our method. Our code is available at: https://github.com/MohsenZand/ObjectBox.
Learning Discriminative Representation via Metric Learning for Imbalanced Medical Image Classification
Authors: Chenghua Zeng, Huijuan Lu, Kanghao Chen, Ruixuan Wang, Wei-Shi Zheng
Date: 2022-07-14 00:00:00
Tasks: Classification, image-classification, Image Classification, Medical Image Classification, Metric Learning

Data imbalance between common and rare diseases during model training often causes intelligent diagnosis systems to have biased predictions towards common diseases. The state-of-the-art approaches apply a two-stage learning framework to alleviate the class-imbalance issue, where the first stage focuses on training of a general feature extractor and the second stage focuses on fine-tuning the classifier head for class rebalancing. However, existing two-stage approaches do not consider the fine-grained property between different diseases, often causing the first stage less effective for medical image classification than for natural image classification tasks. In this study, we propose embedding metric learning into the first stage of the two-stage framework specially to help the feature extractor learn to extract more discriminative feature representations. Extensive experiments mainly on three medical image datasets show that the proposed approach consistently outperforms existing onestage and two-stage approaches, suggesting that metric learning can be used as an effective plug-in component in the two-stage framework for fine-grained class-imbalanced image classification tasks.
DASS: Differentiable Architecture Search for Sparse neural networks
Authors: Hamid Mousavi, Mohammad Loni, Mina Alibeigi, Masoud Daneshtalab
Date: 2022-07-14 00:00:00
Tasks: Network Pruning

The deployment of Deep Neural Networks (DNNs) on edge devices is hindered by the substantial gap between performance requirements and available processing power. While recent research has made significant strides in developing pruning methods to build a sparse network for reducing the computing overhead of DNNs, there remains considerable accuracy loss, especially at high pruning ratios. We find that the architectures designed for dense networks by differentiable architecture search methods are ineffective when pruning mechanisms are applied to them. The main reason is that the current method does not support sparse architectures in their search space and uses a search objective that is made for dense networks and does not pay any attention to sparsity. In this paper, we propose a new method to search for sparsity-friendly neural architectures. We do this by adding two new sparse operations to the search space and modifying the search objective. We propose two novel parametric SparseConv and SparseLinear operations in order to expand the search space to include sparse operations. In particular, these operations make a flexible search space due to using sparse parametric versions of linear and convolution operations. The proposed search objective lets us train the architecture based on the sparsity of the search space operations. Quantitative analyses demonstrate that our search architectures outperform those used in the stateof-the-art sparse networks on the CIFAR-10 and ImageNet datasets. In terms of performance and hardware effectiveness, DASS increases the accuracy of the sparse version of MobileNet-v2 from 73.44% to 81.35% (+7.91% improvement) with 3.87x faster inference time.
Scene Text Recognition with Permuted Autoregressive Sequence Models
Authors: Darwin Bautista, Rowel Atienza
Date: 2022-07-14 00:00:00
Tasks: Language Modeling, Language Modelling, Scene Text Recognition

Context-aware STR methods typically use internal autoregressive (AR) language models (LM). Inherent limitations of AR models motivated two-stage methods which employ an external LM. The conditional independence of the external LM on the input image may cause it to erroneously rectify correct predictions, leading to significant inefficiencies. Our method, PARSeq, learns an ensemble of internal AR LMs with shared weights using Permutation Language Modeling. It unifies context-free non-AR and context-aware AR inference, and iterative refinement using bidirectional context. Using synthetic training data, PARSeq achieves state-of-the-art (SOTA) results in STR benchmarks (91.9% accuracy) and more challenging datasets. It establishes new SOTA results (96.0% accuracy) when trained on real data. PARSeq is optimal on accuracy vs parameter count, FLOPS, and latency because of its simple, unified structure and parallel token processing. Due to its extensive use of attention, it is robust on arbitrarily-oriented text which is common in real-world images. Code, pretrained weights, and data are available at: https://github.com/baudm/parseq.
AutoMerge: A Framework for Map Assembling and Smoothing in City-scale Environments
Authors: Peng Yin, Haowen Lai, Shiqi Zhao, Ruohai Ge, Ji Zhang, Howie Choset, Sebastian Scherer
Date: 2022-07-14 00:00:00
Tasks: Loop Closure Detection, Retrieval

We present AutoMerge, a LiDAR data processing framework for assembling a large number of map segments into a complete map. Traditional large-scale map merging methods are fragile to incorrect data associations, and are primarily limited to working only offline. AutoMerge utilizes multi-perspective fusion and adaptive loop closure detection for accurate data associations, and it uses incremental merging to assemble large maps from individual trajectory segments given in random order and with no initial estimations. Furthermore, after assembling the segments, AutoMerge performs fine matching and pose-graph optimization to globally smooth the merged map. We demonstrate AutoMerge on both city-scale merging (120km) and campus-scale repeated merging (4.5km x 8). The experiments show that AutoMerge (i) surpasses the second- and third- best methods by 14% and 24% recall in segment retrieval, (ii) achieves comparable 3D mapping accuracy for 120 km large-scale map assembly, (iii) and it is robust to temporally-spaced revisits. To the best of our knowledge, AutoMerge is the first mapping approach that can merge hundreds of kilometers of individual segments without the aid of GPS.
Learning Implicit Templates for Point-Based Clothed Human Modeling
Authors: Siyou Lin, Hongwen Zhang, Zerong Zheng, Ruizhi Shao, Yebin Liu
Date: 2022-07-14 00:00:00

We present FITE, a First-Implicit-Then-Explicit framework for modeling human avatars in clothing. Our framework first learns implicit surface templates representing the coarse clothing topology, and then employs the templates to guide the generation of point sets which further capture pose-dependent clothing deformations such as wrinkles. Our pipeline incorporates the merits of both implicit and explicit representations, namely, the ability to handle varying topology and the ability to efficiently capture fine details. We also propose diffused skinning to facilitate template training especially for loose clothing, and projection-based pose-encoding to extract pose information from mesh templates without predefined UV map or connectivity. Our code is publicly available at https://github.com/jsnln/fite.
Tackling Background Distraction in Video Object Segmentation
Authors: Suhwan Cho, Heansung Lee, Minhyeok Lee, Chaewon Park, Sungjun Jang, Minjung Kim, Sangyoun Lee
Date: 2022-07-14 00:00:00
Tasks: Object, Semantic Segmentation, Semi-Supervised Video Object Segmentation, Video Object Segmentation, Video Semantic Segmentation

Semi-supervised video object segmentation (VOS) aims to densely track certain designated objects in videos. One of the main challenges in this task is the existence of background distractors that appear similar to the target objects. We propose three novel strategies to suppress such distractors: 1) a spatio-temporally diversified template construction scheme to obtain generalized properties of the target objects; 2) a learnable distance-scoring function to exclude spatially-distant distractors by exploiting the temporal consistency between two consecutive frames; 3) swap-and-attach augmentation to force each object to have unique features by providing training samples containing entangled objects. On all public benchmark datasets, our model achieves a comparable performance to contemporary state-of-the-art approaches, even with real-time performance. Qualitative results also demonstrate the superiority of our approach over existing methods. We believe our approach will be widely used for future VOS research.
Real-time Streaming Video Denoising with Bidirectional Buffers
Authors: Chenyang Qi, Junming Chen, Xin Yang, Qifeng Chen
Date: 2022-07-14 00:00:00
Tasks: Denoising, Video Denoising

Video streams are delivered continuously to save the cost of storage and device memory. Real-time denoising algorithms are typically adopted on the user device to remove the noise involved during the shooting and transmission of video streams. However, sliding-window-based methods feed multiple input frames for a single output and lack computation efficiency. Recent multi-output inference works propagate the bidirectional temporal feature with a parallel or recurrent framework, which either suffers from performance drops on the temporal edges of clips or can not achieve online inference. In this paper, we propose a Bidirectional Streaming Video Denoising (BSVD) framework, to achieve high-fidelity real-time denoising for streaming videos with both past and future temporal receptive fields. The bidirectional temporal fusion for online inference is considered not applicable in the MoViNet. However, we introduce a novel Bidirectional Buffer Block as the core module of our BSVD, which makes it possible during our pipeline-style inference. In addition, our method is concise and flexible to be utilized in both non-blind and blind video denoising. We compare our model with various state-of-the-art video denoising models qualitatively and quantitatively on synthetic and real noise. Our method outperforms previous methods in terms of restoration fidelity and runtime. Our source code is publicly available at https://github.com/ChenyangQiQi/BSVD
Factorized and Controllable Neural Re-Rendering of Outdoor Scene for Photo Extrapolation
Authors: Boming Zhao, Bangbang Yang, Zhenyang Li, Zuoyue Li, Guofeng Zhang, Jiashu Zhao, Dawei Yin, Zhaopeng Cui, Hujun Bao
Date: 2022-07-14 00:00:00

Expanding an existing tourist photo from a partially captured scene to a full scene is one of the desired experiences for photography applications. Although photo extrapolation has been well studied, it is much more challenging to extrapolate a photo (i.e., selfie) from a narrow field of view to a wider one while maintaining a similar visual style. In this paper, we propose a factorized neural re-rendering model to produce photorealistic novel views from cluttered outdoor Internet photo collections, which enables the applications including controllable scene re-rendering, photo extrapolation and even extrapolated 3D photo generation. Specifically, we first develop a novel factorized re-rendering pipeline to handle the ambiguity in the decomposition of geometry, appearance and illumination. We also propose a composited training strategy to tackle the unexpected occlusion in Internet images. Moreover, to enhance photo-realism when extrapolating tourist photographs, we propose a novel realism augmentation process to complement appearance details, which automatically propagates the texture details from a narrow captured photo to the extrapolated neural rendered image. The experiments and photo editing examples on outdoor scenes demonstrate the superior performance of our proposed method in both photo-realism and downstream applications.
E2FIF: Push the limit of Binarized Deep Imagery Super-resolution using End-to-end Full-precision Information Flow
Authors: Zhiqiang Lang, Chongxing Song, Lei Zhang, Wei Wei
Date: 2022-07-14 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

Binary neural network (BNN) provides a promising solution to deploy parameter-intensive deep single image super-resolution (SISR) models onto real devices with limited storage and computational resources. To achieve comparable performance with the full-precision counterpart, most existing BNNs for SISR mainly focus on compensating the information loss incurred by binarizing weights and activations in the network through better approximations to the binarized convolution. In this study, we revisit the difference between BNNs and their full-precision counterparts and argue that the key for good generalization performance of BNNs lies on preserving a complete full-precision information flow as well as an accurate gradient flow passing through each binarized convolution layer. Inspired by this, we propose to introduce a full-precision skip connection or its variant over each binarized convolution layer across the entire network, which can increase the forward expressive capability and the accuracy of back-propagated gradient, thus enhancing the generalization performance. More importantly, such a scheme is applicable to any existing BNN backbones for SISR without introducing any additional computation cost. To testify its efficacy, we evaluate it using four different backbones for SISR on four benchmark datasets and report obviously superior performance over existing BNNs and even some 4-bit competitors.
Immunofluorescence Capillary Imaging Segmentation: Cases Study
Authors: Runpeng Hou, Ziyuan Ye, Chengyu Yang, Linhao Fu, Chao Liu, Quanying Liu
Date: 2022-07-14 00:00:00
Tasks: Benchmarking, Image Segmentation, Segmentation, Semantic Segmentation

Nonunion is one of the challenges faced by orthopedics clinics for the technical difficulties and high costs in photographing interosseous capillaries. Segmenting vessels and filling capillaries are critical in understanding the obstacles encountered in capillary growth. However, existing datasets for blood vessel segmentation mainly focus on the large blood vessels of the body, and the lack of labeled capillary image datasets greatly limits the methodological development and applications of vessel segmentation and capillary filling. Here, we present a benchmark dataset, named IFCIS-155, consisting of 155 2D capillary images with segmentation boundaries and vessel fillings annotated by biomedical experts, and 19 large-scale, high-resolution 3D capillary images. To obtain better images of interosseous capillaries, we leverage state-of-the-art immunofluorescence imaging techniques to highlight the rich vascular morphology of interosseous capillaries. We conduct comprehensive experiments to verify the effectiveness of the dataset and the benchmarking deep learning models (\eg UNet/UNet++ and the modified UNet/UNet++). Our work offers a benchmark dataset for training deep learning models for capillary image segmentation and provides a potential tool for future capillary research. The IFCIS-155 dataset and code are all publicly available at \url{https://github.com/ncclabsustech/IFCIS-55}.
AIParsing: Anchor-free Instance-level Human Parsing
Authors: Sanyi Zhang, Xiaochun Cao, Guo-Jun Qi, Zhanjie Song, Jie zhou
Date: 2022-07-14 00:00:00
Tasks: Human Parsing, object-detection, Object Detection

Most state-of-the-art instance-level human parsing models adopt two-stage anchor-based detectors and, therefore, cannot avoid the heuristic anchor box design and the lack of analysis on a pixel level. To address these two issues, we have designed an instance-level human parsing network which is anchor-free and solvable on a pixel level. It consists of two simple sub-networks: an anchor-free detection head for bounding box predictions and an edge-guided parsing head for human segmentation. The anchor-free detector head inherits the pixel-like merits and effectively avoids the sensitivity of hyper-parameters as proved in object detection applications. By introducing the part-aware boundary clue, the edge-guided parsing head is capable to distinguish adjacent human parts from among each other up to 58 parts in a single human instance, even overlapping instances. Meanwhile, a refinement head integrating box-level score and part-level parsing quality is exploited to improve the quality of the parsing results. Experiments on two multiple human parsing datasets (i.e., CIHP and LV-MHP-v2.0) and one video instance-level human parsing dataset (i.e., VIP) show that our method achieves the best global-level and instance-level performance over state-of-the-art one-stage top-down alternatives.
Deep Dictionary Learning with An Intra-class Constraint
Authors: Xia Yuan, Jianping Gou, Baosheng Yu, Jiali Yu, Zhang Yi
Date: 2022-07-14 00:00:00
Tasks: Dictionary Learning, Representation Learning

In recent years, deep dictionary learning (DDL)has attracted a great amount of attention due to its effectiveness for representation learning and visual recognition.~However, most existing methods focus on unsupervised deep dictionary learning, failing to further explore the category information.~To make full use of the category information of different samples, we propose a novel deep dictionary learning model with an intra-class constraint (DDLIC) for visual classification. Specifically, we design the intra-class compactness constraint on the intermediate representation at different levels to encourage the intra-class representations to be closer to each other, and eventually the learned representation becomes more discriminative.~Unlike the traditional DDL methods, during the classification stage, our DDLIC performs a layer-wise greedy optimization in a similar way to the training stage. Experimental results on four image datasets show that our method is superior to the state-of-the-art methods.
Enforcing connectivity of 3D linear structures using their 2D projections
Authors: Doruk Oner, Hussein Osman, Mateusz Kozinski, Pascal Fua
Date: 2022-07-14 00:00:00

Many biological and medical tasks require the delineation of 3D curvilinear structures such as blood vessels and neurites from image volumes. This is typically done using neural networks trained by minimizing voxel-wise loss functions that do not capture the topological properties of these structures. As a result, the connectivity of the recovered structures is often wrong, which lessens their usefulness. In this paper, we propose to improve the 3D connectivity of our results by minimizing a sum of topology-aware losses on their 2D projections. This suffices to increase the accuracy and to reduce the annotation effort required to provide the required annotated training data. Code is available at https://github.com/doruk-oner/ConnectivityOnProjections.