pwc-archive/papers-with-abstracts huggingface dataset:
SSEGEP: Small SEGment Emphasized Performance evaluation metric for medical image segmentation
Authors: Ammu R, Neelam Sinha
Date: 2021-09-08 00:00:00
Tasks: Image Segmentation, Medical Image Analysis, Medical Image Segmentation, Segmentation, Semantic Segmentation

Automatic image segmentation is a critical component of medical image analysis, and hence quantifying segmentation performance is crucial. Challenges in medical image segmentation are mainly due to spatial variations of regions to be segmented and imbalance in distribution of classes. Commonly used metrics treat all detected pixels, indiscriminately. However, pixels in smaller segments must be treated differently from pixels in larger segments, as detection of smaller ones aid in early treatment of associated disease and are also easier to miss. To address this, we propose a novel evaluation metric for segmentation performance, emphasizing smaller segments, by assigning higher weightage to smaller segment pixels. Weighted false positives are also considered in deriving the new metric named, "SSEGEP"(Small SEGment Emphasized Performance evaluation metric), (range : 0(Bad) to 1(Good)). The experiments were performed on diverse anatomies(eye, liver, pancreas and breast) from publicly available datasets to show applicability of the proposed metric across different imaging techniques. Mean opinion score (MOS) and statistical significance testing is used to quantify the relevance of proposed approach. Across 33 fundus images, where the largest exudate is 1.41%, and the smallest is 0.0002% of the image, the proposed metric is 30% closer to MOS, as compared to Dice Similarity Coefficient (DSC). Statistical significance testing resulted in promising p-value of order 10^{-18} with SSEGEP for hepatic tumor compared to DSC. The proposed metric is found to perform better for the images having multiple segments for a single label.
Mask is All You Need: Rethinking Mask R-CNN for Dense and Arbitrary-Shaped Scene Text Detection
Authors: Xugong Qin, Yu Zhou, Youhui Guo, Dayan Wu, Zhihong Tian, Ning Jiang, Hongbin Wang, Weiping Wang
Date: 2021-09-08 00:00:00
Tasks: All, Decoder, Instance Segmentation, object-detection, Object Detection, Scene Text Detection, Semantic Segmentation, Text Detection

Due to the large success in object detection and instance segmentation, Mask R-CNN attracts great attention and is widely adopted as a strong baseline for arbitrary-shaped scene text detection and spotting. However, two issues remain to be settled. The first is dense text case, which is easy to be neglected but quite practical. There may exist multiple instances in one proposal, which makes it difficult for the mask head to distinguish different instances and degrades the performance. In this work, we argue that the performance degradation results from the learning confusion issue in the mask head. We propose to use an MLP decoder instead of the "deconv-conv" decoder in the mask head, which alleviates the issue and promotes robustness significantly. And we propose instance-aware mask learning in which the mask head learns to predict the shape of the whole instance rather than classify each pixel to text or non-text. With instance-aware mask learning, the mask branch can learn separated and compact masks. The second is that due to large variations in scale and aspect ratio, RPN needs complicated anchor settings, making it hard to maintain and transfer across different datasets. To settle this issue, we propose an adaptive label assignment in which all instances especially those with extreme aspect ratios are guaranteed to be associated with enough anchors. Equipped with these components, the proposed method named MAYOR achieves state-of-the-art performance on five benchmarks including DAST1500, MSRA-TD500, ICDAR2015, CTW1500, and Total-Text.
RGB-D Salient Object Detection with Ubiquitous Target Awareness
Authors: Yifan Zhao, Jiawei Zhao, Jia Li, Xiaowu Chen
Date: 2021-09-08 00:00:00
Tasks: Object, object-detection, Object Detection, Representation Learning, RGB-D Salient Object Detection, Salient Object Detection, Thermal Image Segmentation

Conventional RGB-D salient object detection methods aim to leverage depth as complementary information to find the salient regions in both modalities. However, the salient object detection results heavily rely on the quality of captured depth data which sometimes are unavailable. In this work, we make the first attempt to solve the RGB-D salient object detection problem with a novel depth-awareness framework. This framework only relies on RGB data in the testing phase, utilizing captured depth data as supervision for representation learning. To construct our framework as well as achieving accurate salient detection results, we propose a Ubiquitous Target Awareness (UTA) network to solve three important challenges in RGB-D SOD task: 1) a depth awareness module to excavate depth information and to mine ambiguous regions via adaptive depth-error weights, 2) a spatial-aware cross-modal interaction and a channel-aware cross-level interaction, exploiting the low-level boundary cues and amplifying high-level salient channels, and 3) a gated multi-scale predictor module to perceive the object saliency in different contextual scales. Besides its high performance, our proposed UTA network is depth-free for inference and runs in real-time with 43 FPS. Experimental evidence demonstrates that our proposed network not only surpasses the state-of-the-art methods on five public RGB-D SOD benchmarks by a large margin, but also verifies its extensibility on five public RGB SOD benchmarks.
YouRefIt: Embodied Reference Understanding with Language and Gesture
Authors: Yixin Chen, Qing Li, Deqian Kong, Yik Lun Kei, Song-Chun Zhu, Tao Gao, Yixin Zhu, Siyuan Huang
Date: 2021-09-08 00:00:00
Proceeding: ICCV 2021 10

We study the understanding of embodied reference: One agent uses both language and gesture to refer to an object to another agent in a shared physical environment. Of note, this new visual task requires understanding multimodal cues with perspective-taking to identify which object is being referred to. To tackle this problem, we introduce YouRefIt, a new crowd-sourced dataset of embodied reference collected in various physical scenes; the dataset contains 4,195 unique reference clips in 432 indoor scenes. To the best of our knowledge, this is the first embodied reference dataset that allows us to study referring expressions in daily physical scenes to understand referential behavior, human communication, and human-robot interaction. We further devise two benchmarks for image-based and video-based embodied reference understanding. Comprehensive baselines and extensive experiments provide the very first result of machine perception on how the referring expressions and gestures affect the embodied reference understanding. Our results provide essential evidence that gestural cues are as critical as language cues in understanding the embodied reference.
GTT-Net: Learned Generalized Trajectory Triangulation
Authors: Xiangyu Xu, Enrique Dunn
Date: 2021-09-08 00:00:00
Proceeding: ICCV 2021 10
Tasks: 3D geometry, Event Segmentation

We present GTT-Net, a supervised learning framework for the reconstruction of sparse dynamic 3D geometry. We build on a graph-theoretic formulation of the generalized trajectory triangulation problem, where non-concurrent multi-view imaging geometry is known but global image sequencing is not provided. GTT-Net learns pairwise affinities modeling the spatio-temporal relationships among our input observations and leverages them to determine 3D geometry estimates. Experiments reconstructing 3D motion-capture sequences show GTT-Net outperforms the state of the art in terms of accuracy and robustness. Within the context of articulated motion reconstruction, our proposed architecture is 1) able to learn and enforce semantic 3D motion priors for shared training and test domains, while being 2) able to generalize its performance across different training and test domains. Moreover, GTT-Net provides a computationally streamlined framework for trajectory triangulation with applications to multi-instance reconstruction and event segmentation.
Master Face Attacks on Face Recognition Systems
Authors: Huy H. Nguyen, Sébastien Marcel, Junichi Yamagishi, Isao Echizen
Date: 2021-09-08 00:00:00
Tasks: Face Recognition

Face authentication is now widely used, especially on mobile devices, rather than authentication using a personal identification number or an unlock pattern, due to its convenience. It has thus become a tempting target for attackers using a presentation attack. Traditional presentation attacks use facial images or videos of the victim. Previous work has proven the existence of master faces, i.e., faces that match multiple enrolled templates in face recognition systems, and their existence extends the ability of presentation attacks. In this paper, we perform an extensive study on latent variable evolution (LVE), a method commonly used to generate master faces. We run an LVE algorithm for various scenarios and with more than one database and/or face recognition system to study the properties of the master faces and to understand in which conditions strong master faces could be generated. Moreover, through analysis, we hypothesize that master faces come from some dense areas in the embedding spaces of the face recognition systems. Last but not least, simulated presentation attacks using generated master faces generally preserve the false-matching ability of their original digital forms, thus demonstrating that the existence of master faces poses an actual threat.
Learning to Discriminate Information for Online Action Detection: Analysis and Application
Authors: Sumin Lee, Hyunjun Eun, Jinyoung Moon, Seokeon Choi, Yoonhyung Kim, Chanho Jung, Changick Kim
Date: 2021-09-08 00:00:00
Tasks: Action Anticipation, Action Detection, Online Action Detection

Online action detection, which aims to identify an ongoing action from a streaming video, is an important subject in real-world applications. For this task, previous methods use recurrent neural networks for modeling temporal relations in an input sequence. However, these methods overlook the fact that the input image sequence includes not only the action of interest but background and irrelevant actions. This would induce recurrent units to accumulate unnecessary information for encoding features on the action of interest. To overcome this problem, we propose a novel recurrent unit, named Information Discrimination Unit (IDU), which explicitly discriminates the information relevancy between an ongoing action and others to decide whether to accumulate the input information. This enables learning more discriminative representations for identifying an ongoing action. In this paper, we further present a new recurrent unit, called Information Integration Unit (IIU), for action anticipation. Our IIU exploits the outputs from IDU as pseudo action labels as well as RGB frames to learn enriched features of observed actions effectively. In experiments on TVSeries and THUMOS-14, the proposed methods outperform state-of-the-art methods by a significant margin in online action detection and action anticipation. Moreover, we demonstrate the effectiveness of the proposed units by conducting comprehensive ablation studies.
Capturing the objects of vision with neural networks
Authors: Benjamin Peters, Nikolaus Kriegeskorte
Date: 2021-09-07 00:00:00
Tasks: Object, Object Recognition

Human visual perception carves a scene at its physical joints, decomposing the world into objects, which are selectively attended, tracked, and predicted as we engage our surroundings. Object representations emancipate perception from the sensory input, enabling us to keep in mind that which is out of sight and to use perceptual content as a basis for action and symbolic cognition. Human behavioral studies have documented how object representations emerge through grouping, amodal completion, proto-objects, and object files. Deep neural network (DNN) models of visual object recognition, by contrast, remain largely tethered to the sensory input, despite achieving human-level performance at labeling objects. Here, we review related work in both fields and examine how these fields can help each other. The cognitive literature provides a starting point for the development of new experimental tasks that reveal mechanisms of human object perception and serve as benchmarks driving development of deep neural network models that will put the object into object recognition.
Certifiably Optimal Outlier-Robust Geometric Perception: Semidefinite Relaxations and Scalable Global Optimization
Authors: Heng Yang, Luca Carlone
Date: 2021-09-07 00:00:00
Tasks: global-optimization, Pose Estimation

We propose the first general and scalable framework to design certifiable algorithms for robust geometric perception in the presence of outliers. Our first contribution is to show that estimation using common robust costs, such as truncated least squares (TLS), maximum consensus, Geman-McClure, Tukey's biweight, among others, can be reformulated as polynomial optimization problems (POPs). By focusing on the TLS cost, our second contribution is to exploit sparsity in the POP and propose a sparse semidefinite programming (SDP) relaxation that is much smaller than the standard Lasserre's hierarchy while preserving empirical exactness, i.e., the SDP recovers the optimizer of the nonconvex POP with an optimality certificate. Our third contribution is to solve the SDP relaxations at an unprecedented scale and accuracy by presenting STRIDE, a solver that blends global descent on the convex SDP with fast local search on the nonconvex POP. Our fourth contribution is an evaluation of the proposed framework on six geometric perception problems including single and multiple rotation averaging, point cloud and mesh registration, absolute pose estimation, and category-level object pose and shape estimation. Our experiments demonstrate that (i) our sparse SDP relaxation is empirically exact with up to 60%-90% outliers across applications; (ii) while still being far from real-time, STRIDE is up to 100 times faster than existing SDP solvers on medium-scale problems, and is the only solver that can solve large-scale SDPs with hundreds of thousands of constraints to high accuracy; (iii) STRIDE safeguards existing fast heuristics for robust estimation (e.g., RANSAC or Graduated Non-Convexity), i.e., it certifies global optimality if the heuristic estimates are optimal, or detects and allows escaping local optima when the heuristic estimates are suboptimal.
Multi-Branch Deep Radial Basis Function Networks for Facial Emotion Recognition
Authors: Fernanda Hernández-Luquin, Hugo Jair Escalante
Date: 2021-09-07 00:00:00
Tasks: Emotion Recognition, Face Model, Facial Emotion Recognition

Emotion recognition (ER) from facial images is one of the landmark tasks in affective computing with major developments in the last decade. Initial efforts on ER relied on handcrafted features that were used to characterize facial images and then feed to standard predictive models. Recent methodologies comprise end-to-end trainable deep learning methods that simultaneously learn both, features and predictive model. Perhaps the most successful models are based on convolutional neural networks (CNNs). While these models have excelled at this task, they still fail at capturing local patterns that could emerge in the learning process. We hypothesize these patterns could be captured by variants based on locally weighted learning. Specifically, in this paper we propose a CNN based architecture enhanced with multiple branches formed by radial basis function (RBF) units that aims at exploiting local information at the final stage of the learning process. Intuitively, these RBF units capture local patterns shared by similar instances using an intermediate representation, then the outputs of the RBFs are feed to a softmax layer that exploits this information to improve the predictive performance of the model. This feature could be particularly advantageous in ER as cultural / ethnicity differences may be identified by the local units. We evaluate the proposed method in several ER datasets and show the proposed methodology achieves state-of-the-art in some of them, even when we adopt a pre-trained VGG-Face model as backbone. We show it is the incorporation of local information what makes the proposed model competitive.
Self-Supervised Representation Learning using Visual Field Expansion on Digital Pathology
Authors: Joseph Boyd, Mykola Liashuha, Eric Deutsch, Nikos Paragios, Stergios Christodoulidis, Maria Vakalopoulou
Date: 2021-09-07 00:00:00
Tasks: Representation Learning

The examination of histopathology images is considered to be the gold standard for the diagnosis and stratification of cancer patients. A key challenge in the analysis of such images is their size, which can run into the gigapixels and can require tedious screening by clinicians. With the recent advances in computational medicine, automatic tools have been proposed to assist clinicians in their everyday practice. Such tools typically process these large images by slicing them into tiles that can then be encoded and utilized for different clinical models. In this study, we propose a novel generative framework that can learn powerful representations for such tiles by learning to plausibly expand their visual field. In particular, we developed a progressively grown generative model with the objective of visual field expansion. Thus trained, our model learns to generate different tissue types with fine details, while simultaneously learning powerful representations that can be used for different clinical endpoints, all in a self-supervised way. To evaluate the performance of our model, we conducted classification experiments on CAMELYON17 and CRC benchmark datasets, comparing favorably to other self-supervised and pre-trained strategies that are commonly used in digital pathology. Our code is available at https://github.com/jcboyd/cdpath21-gan.
Simple Video Generation using Neural ODEs
Authors: David Kanaa, Vikram Voleti, Samira Ebrahimi Kahou, Christopher Pal
Date: 2021-09-07 00:00:00
Tasks: Video Generation

Despite having been studied to a great extent, the task of conditional generation of sequences of frames, or videos, remains extremely challenging. It is a common belief that a key step towards solving this task resides in modelling accurately both spatial and temporal information in video signals. A promising direction to do so has been to learn latent variable models that predict the future in latent space and project back to pixels, as suggested in recent literature. Following this line of work and building on top of a family of models introduced in prior work, Neural ODE, we investigate an approach that models time-continuous dynamics over a continuous latent space with a differential equation with respect to time. The intuition behind this approach is that these trajectories in latent space could then be extrapolated to generate video frames beyond the time steps for which the model is trained. We show that our approach yields promising results in the task of future frame prediction on the Moving MNIST dataset with 1 and 2 digits.
MRI Reconstruction Using Deep Energy-Based Model
Authors: Yu Guan, Zongjiang Tu, Shanshan Wang, Qiegen Liu, Yuhao Wang, Dong Liang
Date: 2021-09-07 00:00:00
Tasks: Image Generation, model, MRI Reconstruction

Purpose: Although recent deep energy-based generative models (EBMs) have shown encouraging results in many image generation tasks, how to take advantage of the self-adversarial cogitation in deep EBMs to boost the performance of Magnetic Resonance Imaging (MRI) reconstruction is still desired. Methods: With the successful application of deep learning in a wide range of MRI reconstruction, a line of emerging research involves formulating an optimization-based reconstruction method in the space of a generative model. Leveraging this, a novel regularization strategy is introduced in this article which takes advantage of self-adversarial cogitation of the deep energy-based model. More precisely, we advocate for alternative learning a more powerful energy-based model with maximum likelihood estimation to obtain the deep energy-based information, represented as image prior. Simultaneously, implicit inference with Langevin dynamics is a unique property of re-construction. In contrast to other generative models for reconstruction, the proposed method utilizes deep energy-based information as the image prior in reconstruction to improve the quality of image. Results: Experiment results that imply the proposed technique can obtain remarkable performance in terms of high reconstruction accuracy that is competitive with state-of-the-art methods, and does not suffer from mode collapse. Conclusion: Algorithmically, an iterative approach was presented to strengthen EBM training with the gradient of energy network. The robustness and the reproducibility of the algorithm were also experimentally validated. More importantly, the proposed reconstruction framework can be generalized for most MRI reconstruction scenarios.
Reconstructing High-resolution Turbulent Flows Using Physics-Guided Neural Networks
Authors: Shengyu Chen, Shervin Sammak, Peyman Givi, Joseph P. Yurko1, Xiaowei Jia
Date: 2021-09-06 00:00:00
Tasks: Super-Resolution, Vocal Bursts Intensity Prediction

Direct numerical simulation (DNS) of turbulent flows is computationally expensive and cannot be applied to flows with large Reynolds numbers. Large eddy simulation (LES) is an alternative that is computationally less demanding, but is unable to capture all of the scales of turbulent transport accurately. Our goal in this work is to build a new data-driven methodology based on super-resolution techniques to reconstruct DNS data from LES predictions. We leverage the underlying physical relationships to regularize the relationships amongst different physical variables. We also introduce a hierarchical generative process and a reverse degradation process to fully explore the correspondence between DNS and LES data. We demonstrate the effectiveness of our method through a single-snapshot experiment and a cross-time experiment. The results confirm that our method can better reconstruct high-resolution DNS data over space and over time in terms of pixel-wise reconstruction error and structural similarity. Visual comparisons show that our method performs much better in capturing fine-level flow dynamics.
Real-World Adversarial Examples involving Makeup Application
Authors: Chang-Sheng Lin, Chia-Yi Hsu, Pin-Yu Chen, Chia-Mu Yu
Date: 2021-09-04 00:00:00
Tasks: Adversarial Attack, Face Recognition, image-classification, Image Classification

Deep neural networks have developed rapidly and have achieved outstanding performance in several tasks, such as image classification and natural language processing. However, recent studies have indicated that both digital and physical adversarial examples can fool neural networks. Face-recognition systems are used in various applications that involve security threats from physical adversarial examples. Herein, we propose a physical adversarial attack with the use of full-face makeup. The presence of makeup on the human face is a reasonable possibility, which possibly increases the imperceptibility of attacks. In our attack framework, we combine the cycle-adversarial generative network (cycle-GAN) and a victimized classifier. The Cycle-GAN is used to generate adversarial makeup, and the architecture of the victimized classifier is VGG 16. Our experimental results show that our attack can effectively overcome manual errors in makeup application, such as color and position-related errors. We also demonstrate that the approaches used to train the models can influence physical attacks; the adversarial perturbations crafted from the pre-trained model are affected by the corresponding training data.