pwc-archive/papers-with-abstracts huggingface dataset:
Handling Data Heterogeneity with Generative Replay in Collaborative Learning for Medical Imaging
Authors: Liangqiong Qu, Niranjan Balachandar, Miao Zhang, Daniel Rubin
Date: 2021-06-24 00:00:00
Tasks: Image Generation, Privacy Preserving

Collaborative learning, which enables collaborative and decentralized training of deep neural networks at multiple institutions in a privacy-preserving manner, is rapidly emerging as a valuable technique in healthcare applications. However, its distributed nature often leads to significant heterogeneity in data distributions across institutions. In this paper, we present a novel generative replay strategy to address the challenge of data heterogeneity in collaborative learning methods. Different from traditional methods that directly aggregating the model parameters, we leverage generative adversarial learning to aggregate the knowledge from all the local institutions. Specifically, instead of directly training a model for task performance, we develop a novel dual model architecture: a primary model learns the desired task, and an auxiliary "generative replay model" allows aggregating knowledge from the heterogenous clients. The auxiliary model is then broadcasted to the central sever, to regulate the training of primary model with an unbiased target distribution. Experimental results demonstrate the capability of the proposed method in handling heterogeneous data across institutions. On highly heterogeneous data partitions, our model achieves ~4.88% improvement in the prediction accuracy on a diabetic retinopathy classification dataset, and ~49.8% reduction of mean absolution value on a Bone Age prediction dataset, respectively, compared to the state-of-the art collaborative learning methods.
When Differential Privacy Meets Interpretability: A Case Study
Authors: Rakshit Naidu, Aman Priyanshu, Aadith Kumar, Sasikanth Kotti, Haofan Wang, FatemehSadat Mireshghallah
Date: 2021-06-24 00:00:00

Given the increase in the use of personal data for training Deep Neural Networks (DNNs) in tasks such as medical imaging and diagnosis, differentially private training of DNNs is surging in importance and there is a large body of work focusing on providing better privacy-utility trade-off. However, little attention is given to the interpretability of these models, and how the application of DP affects the quality of interpretations. We propose an extensive study into the effects of DP training on DNNs, especially on medical imaging applications, on the APTOS dataset.
DROID: Driver-centric Risk Object Identification
Authors: Chengxi Li, Stanley H. Chan, Yi-Ting Chen
Date: 2021-06-24 00:00:00
Tasks: Causal Inference, Object

Identification of high-risk driving situations is generally approached through collision risk estimation or accident pattern recognition. In this work, we approach the problem from the perspective of subjective risk. We operationalize subjective risk assessment by predicting driver behavior changes and identifying the cause of changes. To this end, we introduce a new task called driver-centric risk object identification (DROID), which uses egocentric video to identify object(s) influencing a driver's behavior, given only the driver's response as the supervision signal. We formulate the task as a cause-effect problem and present a novel two-stage DROID framework, taking inspiration from models of situation awareness and causal inference. A subset of data constructed from the Honda Research Institute Driving Dataset (HDD) is used to evaluate DROID. We demonstrate state-of-the-art DROID performance, even compared with strong baseline models using this dataset. Additionally, we conduct extensive ablative studies to justify our design choices. Moreover, we demonstrate the applicability of DROID for risk assessment.
FitVid: Overfitting in Pixel-Level Video Prediction
Authors: Mohammad Babaeizadeh, Mohammad Taghi Saffar, Suraj Nair, Sergey Levine, Chelsea Finn, Dumitru Erhan
Date: 2021-06-24 00:00:00
Tasks: Image Augmentation, Prediction, Video Generation, Video Prediction

An agent that is capable of predicting what happens next can perform a variety of tasks through planning with no additional training. Furthermore, such an agent can internally represent the complex dynamics of the real-world and therefore can acquire a representation useful for a variety of visual perception tasks. This makes predicting the future frames of a video, conditioned on the observed past and potentially future actions, an interesting task which remains exceptionally challenging despite many recent advances. Existing video prediction models have shown promising results on simple narrow benchmarks but they generate low quality predictions on real-life datasets with more complicated dynamics or broader domain. There is a growing body of evidence that underfitting on the training data is one of the primary causes for the low quality predictions. In this paper, we argue that the inefficient use of parameters in the current video models is the main reason for underfitting. Therefore, we introduce a new architecture, named FitVid, which is capable of severe overfitting on the common benchmarks while having similar parameter count as the current state-of-the-art models. We analyze the consequences of overfitting, illustrating how it can produce unexpected outcomes such as generating high quality output by repeating the training data, and how it can be mitigated using existing image augmentation techniques. As a result, FitVid outperforms the current state-of-the-art models across four different video prediction benchmarks on four different metrics.
Q-space Conditioned Translation Networks for Directional Synthesis of Diffusion Weighted Images from Multi-modal Structural MRI
Authors: Mengwei Ren, Heejong Kim, Neel Dey, Guido Gerig
Date: 2021-06-24 00:00:00
Tasks: Diffusion  MRI, Translation

Current deep learning approaches for diffusion MRI modeling circumvent the need for densely-sampled diffusion-weighted images (DWIs) by directly predicting microstructural indices from sparsely-sampled DWIs. However, they implicitly make unrealistic assumptions of static $q$-space sampling during training and reconstruction. Further, such approaches can restrict downstream usage of variably sampled DWIs for usages including the estimation of microstructural indices or tractography. We propose a generative adversarial translation framework for high-quality DWI synthesis with arbitrary $q$-space sampling given commonly acquired structural images (e.g., B0, T1, T2). Our translation network linearly modulates its internal representations conditioned on continuous $q$-space information, thus removing the need for fixed sampling schemes. Moreover, this approach enables downstream estimation of high-quality microstructural maps from arbitrarily subsampled DWIs, which may be particularly important in cases with sparsely sampled DWIs. Across several recent methodologies, the proposed approach yields improved DWI synthesis accuracy and fidelity with enhanced downstream utility as quantified by the accuracy of scalar microstructure indices estimated from the synthesized images. Code is available at https://github.com/mengweiren/q-space-conditioned-dwi-synthesis.
Differential Morph Face Detection using Discriminative Wavelet Sub-bands
Authors: Baaria Chaudhary, Poorya Aghdaie, Sobhan Soleymani, Jeremy Dawson, Nasser M. Nasrabadi
Date: 2021-06-24 00:00:00
Tasks: Face Detection, Face Recognition, MORPH

Face recognition systems are extremely vulnerable to morphing attacks, in which a morphed facial reference image can be successfully verified as two or more distinct identities. In this paper, we propose a morph attack detection algorithm that leverages an undecimated 2D Discrete Wavelet Transform (DWT) for identifying morphed face images. The core of our framework is that artifacts resulting from the morphing process that are not discernible in the image domain can be more easily identified in the spatial frequency domain. A discriminative wavelet sub-band can accentuate the disparity between a real and a morphed image. To this end, multi-level DWT is applied to all images, yielding 48 mid and high-frequency sub-bands each. The entropy distributions for each sub-band are calculated separately for both bona fide and morph images. For some of the sub-bands, there is a marked difference between the entropy of the sub-band in a bona fide image and the identical sub-band's entropy in a morphed image. Consequently, we employ Kullback-Liebler Divergence (KLD) to exploit these differences and isolate the sub-bands that are the most discriminative. We measure how discriminative a sub-band is by its KLD value and the 22 sub-bands with the highest KLD values are chosen for network training. Then, we train a deep Siamese neural network using these 22 selected sub-bands for differential morph attack detection. We examine the efficacy of discriminative wavelet sub-bands for morph attack detection and show that a deep neural network trained on these sub-bands can accurately identify morph imagery.
Towards Fully Interpretable Deep Neural Networks: Are We There Yet?
Authors: Sandareka Wickramanayake, Wynne Hsu, Mong Li Lee
Date: 2021-06-24 00:00:00

Despite the remarkable performance, Deep Neural Networks (DNNs) behave as black-boxes hindering user trust in Artificial Intelligence (AI) systems. Research on opening black-box DNN can be broadly categorized into post-hoc methods and inherently interpretable DNNs. While many surveys have been conducted on post-hoc interpretation methods, little effort is devoted to inherently interpretable DNNs. This paper provides a review of existing methods to develop DNNs with intrinsic interpretability, with a focus on Convolutional Neural Networks (CNNs). The aim is to understand the current progress towards fully interpretable DNNs that can cater to different interpretation requirements. Finally, we identify gaps in current work and suggest potential research directions.
Learning by Planning: Language-Guided Global Image Editing
Authors: Jing Shi, Ning Xu, Yihang Xu, Trung Bui, Franck Dernoncourt, Chenliang Xu
Date: 2021-06-24 00:00:00
Proceeding: CVPR 2021 1

Recently, language-guided global image editing draws increasing attention with growing application potentials. However, previous GAN-based methods are not only confined to domain-specific, low-resolution data but also lacking in interpretability. To overcome the collective difficulties, we develop a text-to-operation model to map the vague editing language request into a series of editing operations, e.g., change contrast, brightness, and saturation. Each operation is interpretable and differentiable. Furthermore, the only supervision in the task is the target image, which is insufficient for a stable training of sequential decisions. Hence, we propose a novel operation planning algorithm to generate possible editing sequences from the target image as pseudo ground truth. Comparison experiments on the newly collected MA5k-Req dataset and GIER dataset show the advantages of our methods. Code is available at https://jshi31.github.io/T2ONet.
Comparison of Consecutive and Re-stained Sections for Image Registration in Histopathology
Authors: Johannes Lotz, Nick Weiss, Jeroen van der Laak, Stefan Heldmann
Date: 2021-06-24 00:00:00
Tasks: Image Registration

Purpose: In digital histopathology, virtual multi-staining is important for diagnosis and biomarker research. Additionally, it provides accurate ground-truth for various deep-learning tasks. Virtual multi-staining can be obtained using different stains for consecutive sections or by re-staining the same section. Both approaches require image registration to compensate tissue deformations, but little attention has been devoted to comparing their accuracy. Approach: We compare variational image registration of consecutive and re-stained sections and analyze the effect of the image resolution which influences accuracy and required computational resources. We present a new hybrid dataset of re-stained and consecutive sections (HyReCo, 81 slide pairs, approx. 3000 landmarks) that we made publicly available and compare its image registration results to the automatic non-rigid histological image registration (ANHIR) challenge data (230 consecutive slide pairs). Results: We obtain a median landmark error after registration of 7.1 {\mu}m (HyReCo) and 16.0 {\mu}m (ANHIR) between consecutive sections. Between re-stained sections, the median registration error is 2.3 {\mu}m and 0.9 {\mu}m in the two subsets of the HyReCo dataset. We observe that deformable registration leads to lower landmark errors than affine registration in both cases, though the effect is smaller in re-stained sections. Conclusion: Deformable registration of consecutive and re-stained sections is a valuable tool for the joint analysis of different stains. Significance: While the registration of re-stained sections allows nucleus-level alignment which allows for a direct analysis of interacting biomarkers, consecutive sections only allow the transfer of region-level annotations. The latter can be achieved at low computational cost using coarser image resolutions.
FaDIV-Syn: Fast Depth-Independent View Synthesis using Soft Masks and Implicit Blending
Authors: Andre Rochow, Max Schwarz, Michael Weinmann, Sven Behnke
Date: 2021-06-24 00:00:00
Tasks: Depth Estimation, Novel View Synthesis

Novel view synthesis is required in many robotic applications, such as VR teleoperation and scene reconstruction. Existing methods are often too slow for these contexts, cannot handle dynamic scenes, and are limited by their explicit depth estimation stage, where incorrect depth predictions can lead to large projection errors. Our proposed method runs in real time on live streaming data and avoids explicit depth estimation by efficiently warping input images into the target frame for a range of assumed depth planes. The resulting plane sweep volume (PSV) is directly fed into our network, which first estimates soft PSV masks in a self-supervised manner, and then directly produces the novel output view. This improves efficiency and performance on transparent, reflective, thin, and feature-less scene parts. FaDIV-Syn can perform both interpolation and extrapolation tasks at 540p in real-time and outperforms state-of-the-art extrapolation methods on the large-scale RealEstate10k dataset. We thoroughly evaluate ablations, such as removing the Soft-Masking network, training from fewer examples as well as generalization to higher resolutions and stronger depth discretization. Our implementation is available.
Exploring Corruption Robustness: Inductive Biases in Vision Transformers and MLP-Mixers
Authors: Katelyn Morrison, Benjamin Gilby, Colton Lipchak, Adam Mattioli, Adriana Kovashka
Date: 2021-06-24 00:00:00
Tasks: Data Augmentation

Recently, vision transformers and MLP-based models have been developed in order to address some of the prevalent weaknesses in convolutional neural networks. Due to the novelty of transformers being used in this domain along with the self-attention mechanism, it remains unclear to what degree these architectures are robust to corruptions. Despite some works proposing that data augmentation remains essential for a model to be robust against corruptions, we propose to explore the impact that the architecture has on corruption robustness. We find that vision transformer architectures are inherently more robust to corruptions than the ResNet-50 and MLP-Mixers. We also find that vision transformers with 5 times fewer parameters than a ResNet-50 have more shape bias. Our code is available to reproduce.
VOLO: Vision Outlooker for Visual Recognition
Authors: Li Yuan, Qibin Hou, Zihang Jiang, Jiashi Feng, Shuicheng Yan
Date: 2021-06-24 00:00:00
Tasks: Domain Generalization, Image Classification, Semantic Segmentation

Visual recognition has been dominated by convolutional neural networks (CNNs) for years. Though recently the prevailing vision transformers (ViTs) have shown great potential of self-attention based models in ImageNet classification, their performance is still inferior to that of the latest SOTA CNNs if no extra data are provided. In this work, we try to close the performance gap and demonstrate that attention-based models are indeed able to outperform CNNs. We find a major factor limiting the performance of ViTs for ImageNet classification is their low efficacy in encoding fine-level features into the token representations. To resolve this, we introduce a novel outlook attention and present a simple and general architecture, termed Vision Outlooker (VOLO). Unlike self-attention that focuses on global dependency modeling at a coarse level, the outlook attention efficiently encodes finer-level features and contexts into tokens, which is shown to be critically beneficial to recognition performance but largely ignored by the self-attention. Experiments show that our VOLO achieves 87.1% top-1 accuracy on ImageNet-1K classification, which is the first model exceeding 87% accuracy on this competitive benchmark, without using any extra training data In addition, the pre-trained VOLO transfers well to downstream tasks, such as semantic segmentation. We achieve 84.3% mIoU score on the cityscapes validation set and 54.3% on the ADE20K validation set. Code is available at \url{https://github.com/sail-sg/volo}.
Sparse Needlets for Lighting Estimation with Spherical Transport Loss
Authors: Fangneng Zhan, Changgong Zhang, WenBo Hu, Shijian Lu, Feiying Ma, Xuansong Xie, Ling Shao
Date: 2021-06-24 00:00:00
Proceeding: ICCV 2021 10
Tasks: Lighting Estimation

Accurate lighting estimation is challenging yet critical to many computer vision and computer graphics tasks such as high-dynamic-range (HDR) relighting. Existing approaches model lighting in either frequency domain or spatial domain which is insufficient to represent the complex lighting conditions in scenes and tends to produce inaccurate estimation. This paper presents NeedleLight, a new lighting estimation model that represents illumination with needlets and allows lighting estimation in both frequency domain and spatial domain jointly. An optimal thresholding function is designed to achieve sparse needlets which trims redundant lighting parameters and demonstrates superior localization properties for illumination representation. In addition, a novel spherical transport loss is designed based on optimal transport theory which guides to regress lighting representation parameters with consideration of the spatial information. Furthermore, we propose a new metric that is concise yet effective by directly evaluating the estimated illumination maps rather than rendered images. Extensive experiments show that NeedleLight achieves superior lighting estimation consistently across multiple evaluation metrics as compared with state-of-the-art methods.
ChaLearn Looking at People: Inpainting and Denoising challenges
Authors: Sergio Escalera, Marti Soler, Stephane Ayache, Umut Guclu, Jun Wan, Meysam Madadi, Xavier Baro, Hugo Jair Escalante, Isabelle Guyon
Date: 2021-06-24 00:00:00
Tasks: Denoising, Pose Estimation

Dealing with incomplete information is a well studied problem in the context of machine learning and computational intelligence. However, in the context of computer vision, the problem has only been studied in specific scenarios (e.g., certain types of occlusions in specific types of images), although it is common to have incomplete information in visual data. This chapter describes the design of an academic competition focusing on inpainting of images and video sequences that was part of the competition program of WCCI2018 and had a satellite event collocated with ECCV2018. The ChaLearn Looking at People Inpainting Challenge aimed at advancing the state of the art on visual inpainting by promoting the development of methods for recovering missing and occluded information from images and video. Three tracks were proposed in which visual inpainting might be helpful but still challenging: human body pose estimation, text overlays removal and fingerprint denoising. This chapter describes the design of the challenge, which includes the release of three novel datasets, and the description of evaluation metrics, baselines and evaluation protocol. The results of the challenge are analyzed and discussed in detail and conclusions derived from this event are outlined.
Advancing biological super-resolution microscopy through deep learning: a brief review
Authors: Tianjie Yang, Yaoru Luo, Wei Ji, Ge Yang
Date: 2021-06-24 00:00:00
Tasks: Deep Learning, Specificity, Super-Resolution

Super-resolution microscopy overcomes the diffraction limit of conventional light microscopy in spatial resolution. By providing novel spatial or spatio-temporal information on biological processes at nanometer resolution with molecular specificity, it plays an increasingly important role in life sciences. However, its technical limitations require trade-offs to balance its spatial resolution, temporal resolution, and light exposure of samples. Recently, deep learning has achieved breakthrough performance in many image processing and computer vision tasks. It has also shown great promise in pushing the performance envelope of super-resolution microscopy. In this brief Review, we survey recent advances in using deep learning to enhance performance of super-resolution microscopy. We focus primarily on how deep learning ad-vances reconstruction of super-resolution images. Related key technical challenges are discussed. Despite the challenges, deep learning is set to play an indispensable and transformative role in the development of super-resolution microscopy. We conclude with an outlook on how deep learning could shape the future of this new generation of light microscopy technology.
AudioCLIP: Extending CLIP to Image, Text and Audio
Authors: Andrey Guzhov, Federico Raue, Jörn Hees, Andreas Dengel
Date: 2021-06-24 00:00:00
Tasks: Classification, Environmental Sound Classification, Sound Classification, Zero-Shot Environment Sound Classification

In the past, the rapidly evolving field of sound classification greatly benefited from the application of methods from other domains. Today, we observe the trend to fuse domain-specific tasks and approaches together, which provides the community with new outstanding models. In this work, we present an extension of the CLIP model that handles audio in addition to text and images. Our proposed model incorporates the ESResNeXt audio-model into the CLIP framework using the AudioSet dataset. Such a combination enables the proposed model to perform bimodal and unimodal classification and querying, while keeping CLIP's ability to generalize to unseen datasets in a zero-shot inference fashion. AudioCLIP achieves new state-of-the-art results in the Environmental Sound Classification (ESC) task, out-performing other approaches by reaching accuracies of 90.07% on the UrbanSound8K and 97.15% on the ESC-50 datasets. Further it sets new baselines in the zero-shot ESC-task on the same datasets 68.78% and 69.40%, respectively). Finally, we also assess the cross-modal querying performance of the proposed model as well as the influence of full and partial training on the results. For the sake of reproducibility, our code is published.