GenAI4ELab/papercli-papers huggingface dataset:
On Transferability of Histological Tissue Labels in Computational Pathology
Authors: Mahdi S. Hosseini; Lyndon Chan; Weimin Huang; Yichen Wang; Danial Hasan; Corwyn Rowsell; Savvas Damaskinos; Konstantinos N. Plataniotis
Venue: ECCV 2020 (main)

Abstract: Deep learning tools in computational pathology, unlike natural vision tasks, face with limited histological tissue labels for classification. This is due to expensive procedure of annotation done by expert pathologist. As a result, the current models are limited to particular diagnostic task in mind where the training workflow is repeated for different organ sites and diseases. In this paper, we explore the possibility of transferring diagnostically-relevant histology labels from a source-domain into multiple target-domains to classify similar tissue structures and cancer grades. We achieve this by training a Convolutional Neural Network (CNN) model on a source-domain of diverse histological tissue labels for classification and then transfer them to different target domains for diagnosis without re-training/fine-tuning (zero-shot). We expedite this by an efficient color augmentation to account for color disparity across different tissue scans and conduct thorough experiments for evaluation."
Learning Actionness via Long-range Temporal Order Verification
Authors: Dimitri Zhukov; Jean-Baptiste Alayrac; Ivan Laptev; Josef Sivic
Venue: ECCV 2020 (main)

Abstract: Current methods for action recognition typically rely on supervision provided by manual labeling. Such methods, however, do not scale well given the high burden of manual video annotation and a very large number of possible actions. The annotation is particularly difficult for temporal action localization where large parts of the video present no action, or background. To address these challenges, we here propose a self-supervised and generic method to isolate actions from their background. We build on the observation that actions often follow a particular temporal order and, hence, can be predicted by other actions in the same video. As consecutive actions might be separated by minutes, differently to prior work on the arrow of time, we here exploit long-range temporal relations in 10-20 minutes long videos. To this end, we propose a new model that learns actionness via a self-supervised proxy task of order verification. The model assigns high actionness scores to clips which order is easy to predict from other clips in the video. To obtain a powerful and action-agnostic model, we train it on the large-scale unlabeled HowTo100M dataset with highly diverse actions from instructional videos. We validate our method on the task of action localization and demonstrate consistent improvements when combined with other recent weakly-supervised methods."
Fully Embedding Fast Convolutional Networks on Pixel Processor Arrays
Authors: Laurie Bose; Piotr Dudek; Jianing Chen; Stephen J. Carey; Walterio W. Mayol-Cuevas
Venue: ECCV 2020 (main)

Abstract: We present a novel method of CNN inference for pixel processor array (PPA) vision sensors, designed to take advantage of their massive parallelism and analog compute capabilities. PPA sensors consist of an array of processing elements (PEs), with each PE capable of light capture, data storage and computation, allowing various computer vision processing to be executed directly upon the sensor device. The key idea behind our approach is storing network weights ""in-pixel"" within the PEs of the PPA sensor itself to allow various computations, such as multiple different image convolutions, to be carried out in parallel. Our approach can perform convolutional layers, max pooling, ReLu, and a final fully connected layer entirely upon the PPA sensor, while leaving no untapped computational resources. This is in contrast to previous works that only use a sensor-level processing to sequentially compute image convolutions, and must transfer data to an external digital processor to complete the computation. We demonstrate our approach on the SCAMP-5 vision system, performing inference of a MNIST digit classification network at over 3000 frames per second and over 93% classification accuracy. This is the first work demonstrating CNN inference conducted entirely upon the processor array of a PPA vision sensor device, requiring no external processing."
Character Region Attention For Text Spotting
Authors: Youngmin Baek; Seung Shin; Jeonghun Baek; Sungrae Park; Junyeop Lee; Daehyun Nam; Hwalsuk Lee
Venue: ECCV 2020 (main)

Abstract: A scene text spotter is composed of text detection and recognition modules. Many studies have been conducted to unify these modules into an end-to-end trainable model to achieve better performance. A typical architecture places detection and recognition modules into separate branches, and a RoI pooling is commonly used to let the branches share a visual feature. However, there still exists a chance of establishing a more complimentary connection between the modules when adopting recognizer that uses attention-based decoder and detector that represents spatial information of the character regions. This is possible since the two modules share a common sub-task which is to find the location of the character regions. Based on the insight, we construct a tightly coupled single pipeline model. This architecture is formed by utilizing detection outputs in the recognizer and propagating the recognition loss through the detection stage. The use of character score map helps the recognizer attend better to the character center points, and the recognition loss propagation to the detector module enhances the localization of the character regions. Also, a strengthened sharing stage allows feature rectification and boundary localization of arbitrary-shaped text regions. Extensive experiments demonstrate state-of-the-art performance in publicly available straight and curved benchmark dataset."
Stable Low-rank Tensor Decomposition for Compression of Convolutional Neural Network
Authors: Anh-Huy Phan; Konstantin Sobolev; Konstantin Sozykin; Dmitry Ermilov; Julia Gusak; Petr Tichavský; Valeriy Glukhov; Ivan Oseledets; Andrzej Cichocki
Venue: ECCV 2020 (main)

Abstract: Most state-of-the-art deep neural networks are overparameterized and exhibit a high computational cost. A straightforward approach to this problem is to replace convolutional kernels with its low-rank tensor approximations, whereas the Canonical Polyadic tensor Decomposition is one of the most suited models. However, fitting the convolutional tensors by numerical optimization algorithms often encounters diverging components, i.e., extremely large rank-one tensors but canceling each other. Such degeneracy often causes the non-interpretable result and numerical instability for the neural network ﬁne-tuning. This paper is the first study on degeneracy in the tensor decomposition of convolutional kernels. We present a novel method, which can stabilize the low-rank approximation of convolutional kernels and ensure efficient compression while preserving the high-quality performance of the neural networks. We evaluate our approach on popular CNN architectures for image classification and show that our method results in much lower accuracy degradation and provides consistent performance."
Dual Mixup Regularized Learning for Adversarial Domain Adaptation
Authors: Yuan Wu; Diana Inkpen; Ahmed El-Roby
Venue: ECCV 2020 (main)

Abstract: Recent advances on unsupervised domain adaptation (UDA) rely on adversarial learning to disentangle the explanatory and transferable features for domain adaptation. However, there are two issues with the existing methods. First, the discriminability of the latent space cannot be fully guaranteed without considering the class-aware information in the target domain. Second, samples from the source and target domains alone are not sufficient for domain-invariant feature extracting in the latent space. In order to alleviate the above issues, we propose a dual mixup regularized learning (DMRL) method for UDA, which not only guides the classifier in enhancing consistent predictions in-between samples, but also enriches the intrinsic structures of the latent space. The DMRL jointly conducts category and domain mixup regularizations on pixel level to improve the effectiveness of models. A series of empirical studies on four domain adaptation benchmarks demonstrate that our approach can achieve the state-of-the-art."
Robust and On-the-fly Dataset Denoising for Image Classification
Authors: Jiaming Song; Yann Dauphin; Michael Auli; Tengyu Ma
Venue: ECCV 2020 (main)

Abstract: Memorization in over-parameterized neural networks could severely hurt generalization in the presence of mislabeled examples. However, mislabeled examples are hard to avoid in extremely large datasets collected with weak supervision. We address this problem by reasoning counterfactually about the loss distribution of examples with uniform random labels had they were trained with the real examples, and use this information to remove noisy examples from the training set. First, we observe that examples with uniform random labels have higher losses when trained with stochastic gradient descent under large learning rates. Then, we propose to model the loss distribution of the counterfactual examples using only the network parameters, which is able to model such examples with remarkable success. Finally, we propose to remove examples whose loss exceeds a certain quantile of the modeled loss distribution.This leads to our approach, On-the-fly Data Denoising (ODD), a simple yet effective algorithm that is robust to mislabeled examples, while introducing almost zero computational overhead compared to standard training. ODD is able to achieve state-of-the-art results on a wide range of datasets including real-world ones such as WebVision and Clothing1M."
Imaging Behind Occluders Using Two-Bounce Light
Authors: Connor Henley; Tomohiro Maeda; Tristan Swedish; Ramesh Raskar
Venue: ECCV 2020 (main)

Abstract: We introduce the new non-line-of-sight imaging problem of mph{imaging behind an occluder}. The behind-an-occluder problem can be solved if the hidden space is flanked by opposing visible surfaces. We illuminate one surface and observe light that scatters off of the opposing surface after traveling through the hidden space. Hidden objects attenuate light that passes through the hidden space, leaving an observable signature that can be used to reconstruct their shape. Our method is experimentally simple--we use an eye-safe laser pointer as a light source, and off-the-shelf RGB or RGB-D cameras to estimate the geometry of relay surfaces and observe two-bounce light. We analyze the photometric and geometric challenges of this new imaging problem, and develop a robust method that produces high-quality 3D reconstructions in uncontrolled settings where relay surfaces may be non-planar."
Improving Object Detection with Selective Self-Supervised Self-Training
Authors: Yandong Li; Di Huang; Danfeng Qin; Liqiang Wang; Boqing Gong
Venue: ECCV 2020 (main)

Abstract: We study how to leverage Web images to augment human-curated object detection datasets. Our approach is two-pronged. On the one hand, we retrieve Web images by image-to-image search, which incurs less domain shift from the curated data than other search methods. The Web images are diverse, supplying a wide variety of object poses, appearances, their interactions with the context, etc. On the other hand, we propose a novel learning method motivated by two parallel lines of work that explore unlabeled data for image classification: self-training and self-supervised learning. They fail to improve object detectors in their vanilla forms due to the domain gap between the Web images and curated datasets. To tackle this challenge, we propose a selective net to rectify the supervision signals in Web images. It not only identifies positive bounding boxes but also creates a safe zone for mining hard negative boxes. We report state-of-the-art results on detecting backpacks and chairs from everyday scenes, along with other challenging object classes. "
Deep Local Shapes: Learning Local SDF Priors for Detailed 3D Reconstruction
Authors: Rohan Chabra; Jan E. Lenssen; Eddy Ilg; Tanner Schmidt; Julian Straub; Steven Lovegrove; Richard Newcombe
Venue: ECCV 2020 (main)

Abstract: Efficiently reconstructing complex and intricate surfaces at scale is a long-standing goal in machine perception. To address this problem we introduce Deep Local Shapes (DeepLS), a deep shape representation that enables high-quality 3D shape representation without prohibitive memory requirements. DeepLS replaces the dense volumetric signed distance function (SDF) representation used in traditional surface reconstruction systems with a learned continuous SDF defined by a neural network, inspired by recent work such as DeepSDF. Unlike DeepSDF, which represents an object-level SDF with a neural network and a single latent code, we store a grid of independent latent codes, each responsible for storing information about surfaces in a small local neighborhood. This decomposition of scenes into local shapes simplifies the prior distribution that the network must learn, and also enables efficient inference. %We enforce global shape consistency implicitly by fitting local shape codes from an receptive field that overlaps with neighboring voxels. We demonstrate the effectiveness and generalization power of DeepLS by showing object shape encoding and reconstructions of full scenes, where DeepLS delivers high compression, accuracy, and local shape completion."
Info3D: Representation Learning on 3D Objects using Mutual Information Maximization and Contrastive Learning
Authors: Aditya Sanghi
Venue: ECCV 2020 (main)

Abstract: A major endeavor of computer vision is to represent, understand and extract structure from 3D data. Towards this goal, unsupervised learning is a powerful and necessary tool. Most current unsupervised methods for 3D shape analysis use datasets that are aligned, require objects to be reconstructed and suffer from deteriorated performance on downstream tasks. To solve these issues we propose to extend the InfoMax and contrastive learning principles on 3D shapes. We show that we can maximize the mutual information between 3D objects and their ""chunks"" to improve the representations in aligned datasets. Furthermore, we can achieve rotation invariance in SO(3) group by maximizing the mutual information between the 3D objects and their geometric transformed versions. Finally, we conduct several experiments such as clustering, transfer learning, shape retrieval, and achieve state of art results."
Adversarial Data Augmentation via Deformation Statistics
Authors: Sahin Olut; Zhengyang Shen; Zhenlin Xu; Samuel Gerber; Marc Niethammer
Venue: ECCV 2020 (main)

Abstract: Deep learning models have been successful in computer vision and medical image analysis. However, training these models frequently requires large labeled image sets whose creation is often very time and labor intensive, for example, in the context of 3D segmentations. Approaches capable of training deep segmentation networks with a limited number of labeled samples are therefore highly desirable. Data augmentation or semi-supervised approaches are commonly used to cope with limited labeled training data. However, the augmentation strategies for many existing approaches are either hand-engineered or require computationally demanding searches. To that end, we explore an augmentation strategy which builds statistical deformation models from unlabeled data via principal component analysis and uses the resulting statistical deformation space to augment the labeled training samples. Specifically, we obtain transformations via deep registration models. This allows for an intuitive control over plausible deformation magnitudes via the statistical model and, if combined with an appropriate deformation model, yields spatially regular transformations. To optimally augment a dataset we use an adversarial strategy integrated into our statistical deformation model. We demonstrate the effectiveness of our approach for the segmentation of knee cartilage from 3D magnetic resonance images. We show favorable performance to state-of-the-art augmentation approaches."
Neural Predictor for Neural Architecture Search
Authors: Wei Wen; Hanxiao Liu; Yiran Chen; Hai Li; Gabriel Bender; Pieter-Jan Kindermans
Venue: ECCV 2020 (main)

Abstract: Neural Architecture Search methods are effective but often use complex algorithms to come up with the best architecture. We propose an approach with three basic steps that is conceptually much simpler. First we train $N$ random architectures to generate $N$ (architecture, validation accuracy) pairs and use them to train a regression model that predicts accuracies for architectures. Next, we use this regression model to predict the validation accuracies of a large number of random architectures. Finally, we train the top-$K$ predicted architectures and deploy the model with the best validation result. While this approach seems simple, it is more than $20 imes$ as sample efficient as Regularized Evolution on the NASBench-101 benchmark. On ImageNet, it approaches the efficiency of more complex and restrictive approaches based on weight sharing such as ProxylessNAS while being fully (embarrassingly) parallelizable and friendly to hyper-parameter tuning."
Learning Permutation Invariant Representations using Memory Networks
Authors: Shivam Kalra; Mohammed Adnan; Graham Taylor; H.R. Tizhoosh
Venue: ECCV 2020 (main)

Abstract: Many real-world tasks such as classification of digital histopathological images and 3D object detection involve learning from a set of instances. In these cases, only a group of instances or a set, collectively, contains meaningful information and therefore only the sets have labels, and not individual data instances. In this work, we present a permutation invariant neural network called Memory-based Exchangeable Model (MEM) for learning universal set functions. The MEM model consists of memory units that embed an input sequence to high-level features enabling it to learn inter-dependencies among instances through a self-attention mechanism. We evaluated the learning ability of MEM on various toy datasets, point cloud classification, and classification of whole slide images (WSIs) into two subtypes of the lung cancer--Lung Adenocarcinoma, and Lung Squamous Cell Carcinoma. We systematically extracted patches from WSIs of the lung, downloaded from The Cancer Genome Atlas (TCGA) dataset, the largest public repository of WSIs, achieving a competitive accuracy of 84.84% for classification of two sub-types of lung cancer. The results on other datasets are promising as well, and demonstrate the efficacy of our model. \keywords{Permutation Invariant Models, Multi Instance Learning, Whole Slide Image Classification, Medical Images}"
Feature Space Augmentation for Long-Tailed Data
Authors: Peng Chu; Xiao Bian; Shaopeng Liu; Haibin Ling
Venue: ECCV 2020 (main)

Abstract: Real-world data often follow a long-tailed distribution as the frequency of each class is typically different. For example, a dataset can have a large number of under-represented classes and a few classes with more than sufficient data. However, a model to represent the dataset is usually expected to have reasonably homogeneous performances across classes. Introducing class-balanced loss and advanced methods on data re-sampling and augmentation are among the best practices to alleviate the data imbalance problem. However, the other part of the problem about the under-represented classes will have to rely on additional knowledge to recover the missing information.In this work, we present a novel approach to address the long-tailed problem by augmenting the under-represented classes in the feature space with the features learned from the classes with ample samples. In particular, we decompose the features of each class into a class-generic component and a class-specific component using class activation map. Novel samples of under-represented classes are then generated on the fly during training stages by fusing the class-specific features from the under-represented classes with the class-generic features from confusing classes. Our results on different datasets such as iNaturalist and a long-tailed version of CIFAR have shown superior performance compared to common practice and the state of the arts."
Laying the Foundations of Deep Long-Term Crowd Flow Prediction
Authors: Samuel S. Sohn; Honglu Zhou; Seonghyeon Moon; Sejong Yoon; Vladimir Pavlovic; Mubbasir Kapadia
Venue: ECCV 2020 (main)

Abstract: Predicting the crowd behavior in complex environments is a key requirement for crowd and disaster management, architectural design, and urban planning. Given a crowd's immediate state, current approaches must be successively repeated over multiple time-steps for long-term predictions, leading to compute expensive and error-prone results. However, most applications require the ability to accurately predict hundreds of possible simulation outcomes (e.g., under different environment and crowd situations) at real-time rates, for which these approaches are prohibitively expensive. We propose the first deep framework to instantly predict the long-term flow of crowds in arbitrarily large, realistic environments. Central to our approach are a novel representation CAGE, which efficiently encodes crowd scenarios into compact, fixed-size representations that losslessly represent the environment, and a modified SegNet architecture for instant long-term crowd flow prediction. We conduct comprehensive experiments on novel synthetic and real datasets. Our results indicate that our approach is able to capture the essence of real crowd movement over very long time periods, while generalizing to never-before-seen environments and crowd contexts."
Weakly-Supervised Action Localization with Expectation-Maximization Multi-Instance Learning
Authors: Zhekun Luo; Devin Guillory; Baifeng Shi; Wei Ke; Fang Wan; Trevor Darrell; Huijuan Xu
Venue: ECCV 2020 (main)

Abstract: Weakly-supervised action localization requires training a model to localize the action segments in the video given only video level action label. It can be solved under the Multiple Instance Learning (MIL) framework, where a bag (video) contains multiple instances (action segments). Since only the bag's label is known, the main challenge is assigning which key instances within the bag to trigger the bag's label. Most previous models use attention-based approaches applying attentions to generate the bag's representation from instances, and then train it via the bag's classification. These models, however, implicitly violate the MIL assumption that instances in negative bags should be uniformly negative. In this work, we explicitly model the key instances assignment as a hidden variable and adopt an Expectation-Maximization (EM) framework. We derive two pseudo-label generation schemes to model the E and M process and iteratively optimize the likelihood lower bound. We show that our EM-MIL approach more accurately models both the learning objective and the MIL assumptions. It achieves state-of-the-art performance on two standard benchmarks, THUMOS14 and ActivityNet1.2."