pwc-archive/papers-with-abstracts huggingface dataset:
On Pursuit of Designing Multi-modal Transformer for Video Grounding
Authors: Meng Cao, Long Chen, Mike Zheng Shou, Can Zhang, Yuexian Zou
Date: 2021-09-13 00:00:00
Proceeding: EMNLP 2021 11
Tasks: All, Decoder, Sentence, Video Grounding

Video grounding aims to localize the temporal segment corresponding to a sentence query from an untrimmed video. Almost all existing video grounding methods fall into two frameworks: 1) Top-down model: It predefines a set of segment candidates and then conducts segment classification and regression. 2) Bottom-up model: It directly predicts frame-wise probabilities of the referential segment boundaries. However, all these methods are not end-to-end, i.e., they always rely on some time-consuming post-processing steps to refine predictions. To this end, we reformulate video grounding as a set prediction task and propose a novel end-to-end multi-modal Transformer model, dubbed as GTR. Specifically, GTR has two encoders for video and language encoding, and a cross-modal decoder for grounding prediction. To facilitate the end-to-end training, we use a Cubic Embedding layer to transform the raw videos into a set of visual tokens. To better fuse these two modalities in the decoder, we design a new Multi-head Cross-Modal Attention. The whole GTR is optimized via a Many-to-One matching loss. Furthermore, we conduct comprehensive studies to investigate different model design choices. Extensive results on three benchmarks have validated the superiority of GTR. All three typical GTR variants achieve record-breaking performance on all datasets and metrics, with several times faster inference speed.
Learning Indoor Inverse Rendering with 3D Spatially-Varying Lighting
Authors: Zian Wang, Jonah Philion, Sanja Fidler, Jan Kautz
Date: 2021-09-13 00:00:00
Proceeding: ICCV 2021 10
Tasks: Image-to-Image Translation, Inverse Rendering, Translation

In this work, we address the problem of jointly estimating albedo, normals, depth and 3D spatially-varying lighting from a single image. Most existing methods formulate the task as image-to-image translation, ignoring the 3D properties of the scene. However, indoor scenes contain complex 3D light transport where a 2D representation is insufficient. In this paper, we propose a unified, learning-based inverse rendering framework that formulates 3D spatially-varying lighting. Inspired by classic volume rendering techniques, we propose a novel Volumetric Spherical Gaussian representation for lighting, which parameterizes the exitant radiance of the 3D scene surfaces on a voxel grid. We design a physics based differentiable renderer that utilizes our 3D lighting representation, and formulates the energy-conserving image formation process that enables joint training of all intrinsic properties with the re-rendering constraint. Our model ensures physically correct predictions and avoids the need for ground-truth HDR lighting which is not easily accessible. Experiments show that our method outperforms prior works both quantitatively and qualitatively, and is capable of producing photorealistic results for AR applications such as virtual object insertion even for highly specular objects.
Learning to Ground Visual Objects for Visual Dialog
Authors: Feilong Chen, Xiuyi Chen, Can Xu, Daxin Jiang
Date: 2021-09-13 00:00:00
Proceeding: Findings (EMNLP) 2021 11
Tasks: Visual Dialog

Visual dialog is challenging since it needs to answer a series of coherent questions based on understanding the visual environment. How to ground related visual objects is one of the key problems. Previous studies utilize the question and history to attend to the image and achieve satisfactory performance, however these methods are not sufficient to locate related visual objects without any guidance. The inappropriate grounding of visual objects prohibits the performance of visual dialog models. In this paper, we propose a novel approach to Learn to Ground visual objects for visual dialog, which employs a novel visual objects grounding mechanism where both prior and posterior distributions over visual objects are used to facilitate visual objects grounding. Specifically, a posterior distribution over visual objects is inferred from both context (history and questions) and answers, and it ensures the appropriate grounding of visual objects during the training process. Meanwhile, a prior distribution, which is inferred from context only, is used to approximate the posterior distribution so that appropriate visual objects can be grounded even without answers during the inference process. Experimental results on the VisDial v0.9 and v1.0 datasets demonstrate that our approach improves the previous strong models in both generative and discriminative settings by a significant margin.
Mutual Supervision for Dense Object Detection
Authors: Ziteng Gao, LiMin Wang, Gangshan Wu
Date: 2021-09-13 00:00:00
Proceeding: ICCV 2021 10
Tasks: Classification, Dense Object Detection, Object, object-detection, Object Detection, regression

The classification and regression head are both indispensable components to build up a dense object detector, which are usually supervised by the same training samples and thus expected to have consistency with each other for detecting objects accurately in the detection pipeline. In this paper, we break the convention of the same training samples for these two heads in dense detectors and explore a novel supervisory paradigm, termed as Mutual Supervision (MuSu), to respectively and mutually assign training samples for the classification and regression head to ensure this consistency. MuSu defines training samples for the regression head mainly based on classification predicting scores and in turn, defines samples for the classification head based on localization scores from the regression head. Experimental results show that the convergence of detectors trained by this mutual supervision is guaranteed and the effectiveness of the proposed method is verified on the challenging MS COCO benchmark. We also find that tiling more anchors at the same location benefits detectors and leads to further improvements under this training scheme. We hope this work can inspire further researches on the interaction of the classification and regression task in detection and the supervision paradigm for detectors, especially separately for these two heads.
Balancing the Budget: Feature Selection and Tracking for Multi-Camera Visual-Inertial Odometry
Authors: Lintong Zhang, David Wisth, Marco Camurri, Maurice Fallon
Date: 2021-09-13 00:00:00
Tasks: feature selection

We present a multi-camera visual-inertial odometry system based on factor graph optimization which estimates motion by using all cameras simultaneously while retaining a fixed overall feature budget. We focus on motion tracking in challenging environments, such as narrow corridors, dark spaces with aggressive motions, and abrupt lighting changes. These scenarios cause traditional monocular or stereo odometry to fail. While tracking motion with extra cameras should theoretically prevent failures, it leads to additional complexity and computational burden. To overcome these challenges, we introduce two novel methods to improve multi-camera feature tracking. First, instead of tracking features separately in each camera, we track features continuously as they move from one camera to another. This increases accuracy and achieves a more compact factor graph representation. Second, we select a fixed budget of tracked features across the cameras to reduce back-end optimization time. We have found that using a smaller set of informative features can maintain the same tracking accuracy. Our proposed method was extensively tested using a hardware-synchronized device consisting of an IMU and four cameras (a front stereo pair and two lateral) in scenarios including: an underground mine, large open spaces, and building interiors with narrow stairs and corridors. Compared to stereo-only state-of-the-art visual-inertial odometry methods, our approach reduces the drift rate, relative pose error, by up to 80% in translation and 39% in rotation.
Task Guided Compositional Representation Learning for ZDA
Authors: Shuang Liu, Mete Ozay
Date: 2021-09-13 00:00:00
Tasks: Domain Adaptation, image-classification, Image Classification, Representation Learning

Zero-shot domain adaptation (ZDA) methods aim to transfer knowledge about a task learned in a source domain to a target domain, while data from target domain are not available. In this work, we address learning feature representations which are invariant to and shared among different domains considering task characteristics for ZDA. To this end, we propose a method for task-guided ZDA (TG-ZDA) which employs multi-branch deep neural networks to learn feature representations exploiting their domain invariance and shareability properties. The proposed TG-ZDA models can be trained end-to-end without requiring synthetic tasks and data generated from estimated representations of target domains. The proposed TG-ZDA has been examined using benchmark ZDA tasks on image classification datasets. Experimental results show that our proposed TG-ZDA outperforms state-of-the-art ZDA methods for different domains and tasks.
Low-Light Image Enhancement with Normalizing Flow
Authors: YuFei Wang, Renjie Wan, Wenhan Yang, Haoliang Li, Lap-Pui Chau, Alex C. Kot
Date: 2021-09-13 00:00:00
Tasks: Image Enhancement, Low-Light Image Enhancement

To enhance low-light images to normally-exposed ones is highly ill-posed, namely that the mapping relationship between them is one-to-many. Previous works based on the pixel-wise reconstruction losses and deterministic processes fail to capture the complex conditional distribution of normally exposed images, which results in improper brightness, residual noise, and artifacts. In this paper, we investigate to model this one-to-many relationship via a proposed normalizing flow model. An invertible network that takes the low-light images/features as the condition and learns to map the distribution of normally exposed images into a Gaussian distribution. In this way, the conditional distribution of the normally exposed images can be well modeled, and the enhancement process, i.e., the other inference direction of the invertible network, is equivalent to being constrained by a loss function that better describes the manifold structure of natural images during the training. The experimental results on the existing benchmark datasets show our method achieves better quantitative and qualitative results, obtaining better-exposed illumination, less noise and artifact, and richer colors.
Evolving Architectures with Gradient Misalignment toward Low Adversarial Transferability
Authors: Kevin Richard G. Operiano, Wanchalerm Pora, Hitoshi Iba, Hiroshi Kera
Date: 2021-09-13 00:00:00

Deep neural network image classifiers are known to be susceptible not only to adversarial examples created for them but even those created for others. This phenomenon poses a potential security risk in various black-box systems relying on image classifiers. The reason behind such transferability of adversarial examples is not yet fully understood and many studies have proposed training methods to obtain classifiers with low transferability. In this study, we address this problem from a novel perspective through investigating the contribution of the network architecture to transferability. Specifically, we propose an architecture searching framework that employs neuroevolution to evolve network architectures and the gradient misalignment loss to encourage networks to converge into dissimilar functions after training. Our experiments show that the proposed framework successfully discovers architectures that reduce transferability from four standard networks including ResNet and VGG, while maintaining a good accuracy on unperturbed images. In addition, the evolved networks trained with gradient misalignment exhibit significantly lower transferability compared to standard networks trained with gradient misalignment, which indicates that the network architecture plays an important role in reducing transferability. This study demonstrates that designing or exploring proper network architectures is a promising approach to tackle the transferability issue and train adversarially robust image classifiers.
WeakSTIL: Weak whole-slide image level stromal tumor infiltrating lymphocyte scores are all you need
Authors: Yoni Schirris, Mendel Engelaer, Andreas Panteli, Hugo Mark Horlings, Efstratios Gavves, Jonas Teuwen
Date: 2021-09-13 00:00:00
Tasks: All, Decision Making, Multiple Instance Learning, Self-Supervised Learning, whole slide images

We present WeakSTIL, an interpretable two-stage weak label deep learning pipeline for scoring the percentage of stromal tumor infiltrating lymphocytes (sTIL%) in H&E-stained whole-slide images (WSIs) of breast cancer tissue. The sTIL% score is a prognostic and predictive biomarker for many solid tumor types. However, due to the high labeling efforts and high intra- and interobserver variability within and between expert annotators, this biomarker is currently not used in routine clinical decision making. WeakSTIL compresses tiles of a WSI using a feature extractor pre-trained with self-supervised learning on unlabeled histopathology data and learns to predict precise sTIL% scores for each tile in the tumor bed by using a multiple instance learning regressor that only requires a weak WSI-level label. By requiring only a weak label, we overcome the large annotation efforts required to train currently existing TIL detection methods. We show that WeakSTIL is at least as good as other TIL detection methods when predicting the WSI-level sTIL% score, reaching a coefficient of determination of $0.45\pm0.15$ when compared to scores generated by an expert pathologist, and an AUC of $0.89\pm0.05$ when treating it as the clinically interesting sTIL-high vs sTIL-low classification task. Additionally, we show that the intermediate tile-level predictions of WeakSTIL are highly interpretable, which suggests that WeakSTIL pays attention to latent features related to the number of TILs and the tissue type. In the future, WeakSTIL may be used to provide consistent and interpretable sTIL% predictions to stratify breast cancer patients into targeted therapy arms.
Nonlocal Patch-Based Fully-Connected Tensor Network Decomposition for Remote Sensing Image Inpainting
Authors: Wen-Jie Zheng, Xi-Le Zhao, Yu-Bang Zheng, Zhi-Feng Pang
Date: 2021-09-13 00:00:00
Tasks: Image Inpainting

Remote sensing image (RSI) inpainting plays an important role in real applications. Recently, fully-connected tensor network (FCTN) decomposition has been shown the remarkable ability to fully characterize the global correlation. Considering the global correlation and the nonlocal self-similarity (NSS) of RSIs, this paper introduces the FCTN decomposition to the whole RSI and its NSS groups, and proposes a novel nonlocal patch-based FCTN (NL-FCTN) decomposition for RSI inpainting. Different from other nonlocal patch-based methods, the NL-FCTN decomposition-based method, which increases tensor order by stacking similar small-sized patches to NSS groups, cleverly leverages the remarkable ability of FCTN decomposition to deal with higher-order tensors. Besides, we propose an efficient proximal alternating minimization-based algorithm to solve the proposed NL-FCTN decomposition-based model with a theoretical convergence guarantee. Extensive experiments on RSIs demonstrate that the proposed method achieves the state-of-the-art inpainting performance in all compared methods.
Graph-Based 3D Multi-Person Pose Estimation Using Multi-View Images
Authors: Size Wu, Sheng Jin, Wentao Liu, Lei Bai, Chen Qian, Dong Liu, Wanli Ouyang
Date: 2021-09-13 00:00:00
Proceeding: ICCV 2021 10
Tasks: 3D Multi-Person Pose Estimation, 3D Pose Estimation, Multi-Person Pose Estimation, Pose Estimation

This paper studies the task of estimating the 3D human poses of multiple persons from multiple calibrated camera views. Following the top-down paradigm, we decompose the task into two stages, i.e. person localization and pose estimation. Both stages are processed in coarse-to-fine manners. And we propose three task-specific graph neural networks for effective message passing. For 3D person localization, we first use Multi-view Matching Graph Module (MMG) to learn the cross-view association and recover coarse human proposals. The Center Refinement Graph Module (CRG) further refines the results via flexible point-based prediction. For 3D pose estimation, the Pose Regression Graph Module (PRG) learns both the multi-view geometry and structural relations between human joints. Our approach achieves state-of-the-art performance on CMU Panoptic and Shelf datasets with significantly lower computation complexity.
Conditional MoCoGAN for Zero-Shot Video Generation
Authors: Shun Kimura, Kazuhiko Kawamoto
Date: 2021-09-13 00:00:00
Tasks: Generative Adversarial Network, Image Generation, Video Generation

We propose a conditional generative adversarial network (GAN) model for zero-shot video generation. In this study, we have explored zero-shot conditional generation setting. In other words, we generate unseen videos from training samples with missing classes. The task is an extension of conditional data generation. The key idea is to learn disentangled representations in the latent space of a GAN. To realize this objective, we base our model on the motion and content decomposed GAN and conditional GAN for image generation. We build the model to find better-disentangled representations and to generate good-quality videos. We demonstrate the effectiveness of our proposed model through experiments on the Weizmann action database and the MUG facial expression database.
Variational Disentanglement for Domain Generalization
Authors: YuFei Wang, Haoliang Li, Hao Cheng, Bihan Wen, Lap-Pui Chau, Alex C. Kot
Date: 2021-09-13 00:00:00
Tasks: Disentanglement, Domain Generalization, Variational Inference

Domain generalization aims to learn an invariant model that can generalize well to the unseen target domain. In this paper, we propose to tackle the problem of domain generalization by delivering an effective framework named Variational Disentanglement Network (VDN), which is capable of disentangling the domain-specific features and task-specific features, where the task-specific features are expected to be better generalized to unseen but related test data. We further show the rationale of our proposed method by proving that our proposed framework is equivalent to minimize the evidence upper bound of the divergence between the distribution of task-specific features and its invariant ground truth derived from variational inference. We conduct extensive experiments to verify our method on three benchmarks, and both quantitative and qualitative results illustrate the effectiveness of our method.
Improving the Robustness of Adversarial Attacks Using an Affine-Invariant Gradient Estimator
Authors: Wenzhao Xiang, Hang Su, Chang Liu, Yandong Guo, Shibao Zheng
Date: 2021-09-13 00:00:00
Tasks: Adversarial Attack

As designers of artificial intelligence try to outwit hackers, both sides continue to hone in on AI's inherent vulnerabilities. Designed and trained from certain statistical distributions of data, AI's deep neural networks (DNNs) remain vulnerable to deceptive inputs that violate a DNN's statistical, predictive assumptions. Before being fed into a neural network, however, most existing adversarial examples cannot maintain malicious functionality when applied to an affine transformation. For practical purposes, maintaining that malicious functionality serves as an important measure of the robustness of adversarial attacks. To help DNNs learn to defend themselves more thoroughly against attacks, we propose an affine-invariant adversarial attack, which can consistently produce more robust adversarial examples over affine transformations. For efficiency, we propose to disentangle current affine-transformation strategies from the Euclidean geometry coordinate plane with its geometric translations, rotations and dilations; we reformulate the latter two in polar coordinates. Afterwards, we construct an affine-invariant gradient estimator by convolving the gradient at the original image with derived kernels, which can be integrated with any gradient-based attack methods. Extensive experiments on ImageNet, including some experiments under physical condition, demonstrate that our method can significantly improve the affine invariance of adversarial examples and, as a byproduct, improve the transferability of adversarial examples, compared with alternative state-of-the-art methods.
Self supervised learning improves dMMR/MSI detection from histology slides across multiple cancers
Authors: Charlie Saillard, Olivier Dehaene, Tanguy Marchand, Olivier Moindrot, Aurélie Kamoun, Benoit Schmauch, Simon Jegou
Date: 2021-09-13 00:00:00
Proceeding: MICCAI Workshop COMPAY 2021 9
Tasks: Self-Supervised Learning

Microsatellite instability (MSI) is a tumor phenotype whose diagnosis largely impacts patient care in colorectal cancers (CRC), and is associated with response to immunotherapy in all solid tumors. Deep learning models detecting MSI tumors directly from H&E stained slides have shown promise in improving diagnosis of MSI patients. Prior deep learning models for MSI detection have relied on neural networks pretrained on ImageNet dataset, which does not contain any medical image. In this study, we leverage recent advances in self-supervised learning by training neural networks on histology images from the TCGA dataset using MoCo V2. We show that these networks consistently outperform their counterparts pretrained using ImageNet and obtain state-of-the-art results for MSI detection with AUCs of 0.92 and 0.83 for CRC and gastric tumors, respectively. These models generalize well on an external CRC cohort (0.97 AUC on PAIP) and improve transfer from one organ to another. Finally we show that predictive image regions exhibit meaningful histological patterns, and that the use of MoCo features highlighted more relevant patterns according to an expert pathologist.
Leveraging Clinical Characteristics for Improved Deep Learning-Based Kidney Tumor Segmentation on CT
Authors: Christina B. Lund, Bas H. M. van der Velden
Date: 2021-09-13 00:00:00
Tasks: Computed Tomography (CT), Segmentation, Tumor Segmentation

This paper assesses whether using clinical characteristics in addition to imaging can improve automated segmentation of kidney cancer on contrast-enhanced computed tomography (CT). A total of 300 kidney cancer patients with contrast-enhanced CT scans and clinical characteristics were included. A baseline segmentation of the kidney cancer was performed using a 3D U-Net. Input to the U-Net were the contrast-enhanced CT images, output were segmentations of kidney, kidney tumors, and kidney cysts. A cognizant sampling strategy was used to leverage clinical characteristics for improved segmentation. To this end, a Least Absolute Shrinkage and Selection Operator (LASSO) was used. Segmentations were evaluated using Dice and Surface Dice. Improvement in segmentation was assessed using Wilcoxon signed rank test. The baseline 3D U-Net showed a segmentation performance of 0.90 for kidney and kidney masses, i.e., kidney, tumor, and cyst, 0.29 for kidney masses, and 0.28 for kidney tumor, while the 3D U-Net trained with cognizant sampling enhanced the segmentation performance and reached Dice scores of 0.90, 0.39, and 0.38 respectively. To conclude, the cognizant sampling strategy leveraging the clinical characteristics significantly improved kidney cancer segmentation.