GenAI4ELab/papercli-papers huggingface dataset:
Polarized Optical-Flow Gyroscope
Authors: Masada Tzabari; Yoav Y. Schechner
Venue: ECCV 2020 (main)

Abstract: We merge by generalization two principles of passive optical sensing of motion. One is common spatially resolved imaging, where motion induces temporal readout changes at high-contrast spatial features, as used in traditional optical-flow. The other is the polarization compass, where axial rotation induces temporal readout changes due to the change of incoming polarization angle, relative to the camera frame. The latter has traditionally been modeled for uniform objects. This merger generalizes the brightness constancy assumption and optical-flow, to handle polarization. It also generalizes the polarization compass concept to handle arbitrarily textured objects. This way, scene regions having partial polarization contribute to motion estimation, irrespective of their texture and non-uniformity. As an application, we derive and demonstrate passive sensing of differential ego-rotation around the camera optical axis. "
Online Meta-Learning for Multi-Source and Semi-Supervised Domain Adaptation
Authors: Da Li; Timothy Hospedales
Venue: ECCV 2020 (main)

Abstract: Domain adaptation (DA) is the topical problem of adapting models from labelled source datasets so that they perform well on target datasets where only unlabelled or partially labelled data is available. Many methods have been proposed to address this problem through different ways to minimise the domain shift between source and target datasets. In this paper we take an orthogonal perspective and propose a framework to further enhance performance by meta-learning the initial conditions of existing DA algorithms. This is challenging compared to the more widely considered setting of few-shot meta-learning, due to the length of the computation graph involved. Therefore we propose an online shortest-path meta-learning framework that is both computationally tractable and practically effective for improving DA performance. We present variants for both multi-source unsupervised domain adaptation (MSDA), and semi-supervised domain adaptation (SSDA). Importantly, our approach is agnostic to the base adaptation algorithm, and can be applied to improve many techniques. Experimentally, we demonstrate improvements on classic (DANN) and recent (MCD and MME) techniques for MSDA and SSDA, and ultimately achieve state of the art results on several DA benchmarks including the largest scale DomainNet."
An Ensemble of Epoch-wise Empirical Bayes for Few-shot Learning
Authors: Yaoyao Liu; Bernt Schiele; Qianru Sun
Venue: ECCV 2020 (main)

Abstract: Few-shot learning aims to train efficient predictive models with a few examples. The lack of training data leads to poor models that perform high-variance or low-confidence predictions. In this paper, we propose to meta-learn the ensemble of epoch-wise empirical Bayes models (E3BM) to achieve robust predictions. ""Epoch-wise"" means that each training epoch has a Bayes model whose parameters are specifically learned and deployed. ""Empirical"" means that the hyperparameters, e.g., used for learning and ensembling the epoch-wise models, are generated by hyperprior learners conditional on task-specific data. We introduce four kinds of hyperprior learners by considering inductive vs. transductive, and epoch-dependent vs. epoch-independent, in the paradigm of meta-learning. We conduct extensive experiments for five-class few-shot learning tasks on three challenging benchmarks: miniImageNet, tieredImageNet, and FC100, and achieve top performance using the epoch-dependent transductive hyperprior learner, which captures the richest information. Our ablation study shows that both ""epoch-wise ensemble"" and ""empirical"" encourage high efficiency and robustness in the model performance."
On the Effectiveness of Image Rotation for Open Set Domain Adaptation
Authors: Silvia Bucci; Mohammad Reza Loghmani; Tatiana Tommasi
Venue: ECCV 2020 (main)

Abstract: Open Set Domain Adaptation (OSDA) bridges the domain gap between a labeled source domain and an unlabeled target domain, while also rejecting target classes that are not present in the source. To avoid negative transfer, OSDA can be tackled by first separating the known/unknown target samples and then aligning known target samples with the source data. We propose a novel method to addresses both these problems using the self-supervised task of rotation recognition. Moreover, we assess the performance with a new open set metric that properly balances the contribution of recognizing the known classes and rejecting the unknown samples. Comparative experiments with existing OSDA methods on the standard Office-31 and Office-Home benchmarks show that: (i) our method outperforms its competitors, (ii) reproducibility for this field is a crucial issue to tackle, (iii) our metric provides a reliable tool to allow fair open set evaluation."
Combining Task Predictors via Enhancing Joint Predictability
Authors: Kwang In Kim; Christian Richardt; Hyung Jin Chang
Venue: ECCV 2020 (main)

Abstract: Predictor combination aims to improve a (target) predictor of a learning task based on the (reference) predictors of potentially relevant tasks, without having access to the internals of individual predictors. We present a new predictor combination algorithm that improves the target by i) measuring the relevance of references based on their capabilities in predicting the target, and ii) strengthening such estimated relevance. Unlike existing predictor combination approaches that only exploit pairwise relationships between the target and each reference, and thereby ignore potentially useful dependence among references, our algorithm jointly assesses the relevance of all references by adopting a Bayesian framework. This also offers a rigorous way to automatically select only relevant references. Based on experiments on six real-world datasets from visual attribute ranking and multi-class classification scenarios, we demonstrate that our algorithm offers a significant performance gain and broadens the application range of existing predictor combination approaches."
Multi-Scale Positive Sample Refinement for Few-Shot Object Detection
Authors: Jiaxi Wu; Songtao Liu; Di Huang; Yunhong Wang
Venue: ECCV 2020 (main)

Abstract: Few-shot object detection (FSOD) helps detectors adapt to unseen classes with few training instances, and is useful when manual annotation is time-consuming or data acquisition is limited. Unlike previous attempts that exploit few-shot classification techniques to facilitate FSOD, this work highlights the necessity of handling the problem of scale variations, which is challenging due to the unique sample distribution. To this end, we propose a Multi-scale Positive Sample Refinement (MPSR) approach to enrich object scales in FSOD. It generates multi-scale positive samples as object pyramids and refines the prediction at various scales. We demonstrate its advantage by integrating it as an auxiliary branch to the popular architecture of Faster R-CNN with FPN, delivering a strong FSOD solution. Several experiments are conducted on PASCAL VOC and MS COCO, and the proposed approach achieves state of the art results and significantly outperforms other counterparts, which shows its effectiveness."
Single-Image Depth Prediction Makes Feature Matching Easier
Authors: Carl Toft; Daniyar Turmukhambetov; Torsten Sattler; Fredrik Kahl; Gabriel J. Brostow
Venue: ECCV 2020 (main)

Abstract: Good local features improve the robustness of many 3D re-localization and multi-view reconstruction pipelines. The problem is that viewing angle and distance severely impact the recognizability of a local feature. Attempts to improve appearance invariance by choosing better local feature points or by leveraging outside information, have come with pre-requisites that made some of them impractical. In this paper, we propose a surprisingly effective enhancement to local feature extraction, which improves matching. We show that CNN-based depths inferred from single RGB images, are quite helpful, despite their flaws. They allow us to pre-warp images and rectify perspective distortions, to significantly enhance SIFT and BRISK features, enabling more good matches, even when cameras are looking at the same scene but in opposite directions. "
Deep Reinforced Attention Learning for Quality-Aware Visual Recognition
Authors: Duo Li; Qifeng Chen
Venue: ECCV 2020 (main)

Abstract: In this paper, we build upon the weakly-supervised generation mechanism of intermediate attention maps in any convolutional neural networks and disclose the effectiveness of attention modules more straightforwardly to fully exploit their potential. Given an existing neural network equipped with arbitrary attention modules, we introduce a meta critic network to evaluate the quality of attention maps in the main network. Due to the discreteness of our designed reward, the proposed learning method is arranged in a reinforcement learning setting, where the attention actors and recurrent critics are alternately optimized to provide instant critique and revision for the temporary attention representation, hence coined as Deep REinforced Attention Learning (DREAL). It could be applied universally to network architectures with different types of attention modules and promotes their expressive ability by maximizing the relative gain of the final recognition performance arising from each individual attention module, as demonstrated by extensive experiments on both category and instance recognition benchmarks."
CFAD: Coarse-to-Fine Action Detector for Spatiotemporal Action Localization
Authors: Yuxi Li; Weiyao Lin; John See; Ning Xu Shugong Xu; Ke Yan; Cong Yang
Venue: ECCV 2020 (main)

Abstract: Most current pipelines for spatiotemporal action localization connect frame-wise or clip-wise detection results to generate action proposals. In this paper, we propose Coarse-to-Fine Action Detector (CFAD), an original end-to-end trainable framework for efficient spatiotemporal action localization. The CFAD introduces a new paradigm that first estimates coarse spatiotemporal action tubes from video streams, and then refines the tubes’ location based on key timestamps. This concept is implemented by two key components, the Coarse and Refine Modules in our frame-work. The parameterized modeling of long temporal information in the Coarse Module helps obtain accurate classification and initial tube estimation, while the Refine Module selectively adjusts the tube location under the guidance of key timestamps. Against other methods, the proposed CFAD achieves state-of-the-art results on action detection bench-marks of UCF101-24, UCFSports and JHMDB-21 with inference speed that is 3.3× faster than the nearest competitor."
Learning Joint Spatial-Temporal Transformations for Video Inpainting
Authors: Yanhong Zeng; Jianlong Fu; Hongyang Chao
Venue: ECCV 2020 (main)

Abstract: High-quality video inpainting that completes missing regions in video frames is a promising yet challenging task. State-of-the-art approaches adopt attention models to complete a frame by searching missing contents from reference frames, and further complete whole videos frame by frame. However, these approaches can suffer from inconsistent attention results along spatial and temporal dimensions, which often leads to blurriness and temporal artifacts in videos. In this paper, we propose to learn a joint Spatial-Temporal Transformer Network (STTN) for video inpainting. Specifically, we simultaneously fill missing regions in all input frames by self-attention, and propose to optimize STTN by a spatial-temporal adversarial loss. To show the superiority of the proposed model, we conduct both quantitative and qualitative evaluations by using standard stationary masks and more realistic moving object masks. Demo videos are available at https://github.com/researchmm/STTN."
Single Path One-Shot Neural Architecture Search with Uniform Sampling
Authors: Zichao Guo; Xiangyu Zhang; Haoyuan Mu; Wen Heng; Zechun Liu; Yichen Wei; Jian Sun
Venue: ECCV 2020 (main)

Abstract: We revisit the one-shot Neural Architecture Search (NAS) paradigm and analyze its advantages over existing NAS approaches. Existing one-shot method, however, is hard to train and not yet effective on large scale datasets like ImageNet. This work propose a Single Path One-Shot model to address the challenge in the training. Our central idea is to construct a simplified supernet, where all architectures are single paths so that weight co-adaption problem is alleviated. Training is performed by uniform path sampling. All architectures (and their weights) are trained fully and equally. Comprehensive experiments verify that our approach is flexible and effective. It is easy to train and fast to search. It effortlessly supports complex search spaces (e.g., building blocks, channel, mixed-precision quantization) and different search constraints (e.g., FLOPs, latency). It is thus convenient to use for various needs. It achieves start-of-the-art performance on the large dataset ImageNet."
Learning to Generate Novel Domains for Domain Generalization
Authors: Kaiyang Zhou; Yongxin Yang; Timothy Hospedales; Tao Xiang
Venue: ECCV 2020 (main)

Abstract: This paper focuses on domain generalization (DG), the task of learning from multiple source domains a model that generalizes well to unseen domains. A main challenge for DG is that the available source domains often exhibit limited diversity, hampering the model's ability to learn to generalize. We therefore employ a data generator to synthesize data from pseudo-novel domains to augment the source domains. This explicitly increases the diversity of available training domains and leads to a more generalizable model. To train the generator, we model the distribution divergence between source and synthesized pseudo-novel domains using optimal transport, and maximize the divergence. To ensure that semantics are preserved in the synthesized data, we further impose cycle-consistency and classification losses on the generator. Our method, L2A-OT (Learning to Augment by Optimal Transport) outperforms current state-of-the-art DG methods on four benchmark datasets."
Continuous Adaptation for Interactive Object Segmentation by Learning from Corrections
Authors: Theodora Kontogianni; Michael Gygli; Jasper Uijlings; Vittorio Ferrari
Venue: ECCV 2020 (main)

Abstract: In interactive object segmentation a user collaborates with a computer vision model to segment an object. Recent works employ convolutional neural networks for this task: Given an image and a set of corrections made by the user as input, they output a segmentation mask. These approaches achieve strong performance by training on large datasets but they keep the model parameters unchanged at test time. Instead, we recognize that user corrections can serve as sparse training examples and we propose a method that capitalizes on that idea to update the model parameters on-the-fly to the data at hand. Our approach enables the adaptation to a particular object and its background, to distributions shifts in a test set, to specific object classes, and even to large domain changes, where the imaging modality changes between training and testing. We perform extensive experiments on 8 diverse datasets and show: Compared to a model with frozen parameters, our method reduces the required corrections (i) by 9%-30% when distribution shifts are small between training and testing; (ii) by 12%-44% when specializing to a specific class; (iii) and by 60% and 77% when we completely change domain between training and testing."
Impact of base dataset design on few-shot image classification
Authors: Othman Sbai; Camille Couprie; Mathieu Aubry
Venue: ECCV 2020 (main)

Abstract: The quality and generality of deep image features is crucially determined by the data they have been trained on, but little is known about this often overlooked effect. In this paper, we systematically study the effect of variations in the training data by evaluating deep features trained on different image sets in a few-shot classification setting. The experimental protocol we define allows to explore key practical questions. What is the influence of the similarity between base and test classes? Given a fixed annotation budget, what is the optimal trade-off between the number of images per class and the number of classes? Given a fixed dataset, can features be improved by splitting or combining different classes? Should simple or diverse classes be annotated? In a wide range of experiments, we provide clear answers to these questions on the miniImageNet, ImageNet and CUB-200 datasets. We also show how the base dataset design can improve performance in few-shot classification more drastically than replacing a simple baseline by an advanced state of the art algorithm."
Invertible Zero-Shot Recognition Flows
Authors: Yuming Shen; Jie Qin; Lei Huang; Li Liu; Fan Zhu; Ling Shao
Venue: ECCV 2020 (main)

Abstract: Deep generative models have been successfully applied to Zero-Shot Learning (ZSL) recently. However, the underlying drawbacks of GANs and VAEs (e.g., the hardness of training with ZSL-oriented regularizers and the limited generation quality) hinder the existing generative ZSL models from fully bypassing the seen-unseen bias. To tackle the above limitations, for the first time, this work incorporates a new family of generative models (i.e., flow-based models) into ZSL. The proposed Invertible Zero-shot Flow (IZF) learns factorized data embeddings (i.e., the semantic factors and the non-semantic ones) with the forward pass of an invertible flow network, while the reverse pass generates data samples. This procedure theoretically extends conventional generative flows to a conditional scheme. To explicitly solve the bias problem, our model enlarges the seen-unseen distributional discrepancy based on negative sample-based distance measurement. Notably, IZF works flexibly with either a naive Bayesian classifier or a held-out trainable one for zero-shot recognition. Experiments on widely-adopted ZSL benchmarks demonstrate the significant performance gain of IZF over existing methods, in terms of both classic and generalized settings."
GeoLayout: Geometry Driven Room Layout Estimation Based on Depth Maps of Planes
Authors: Weidong Zhang; Wei Zhang; Yinda Zhang
Venue: ECCV 2020 (main)

Abstract: The task of room layout estimation is to locate the wall-floor, wall-ceiling, and wall-wall boundaries. Most recent methods solve this problem based on edge/keypoint detection or semantic segmentation. However, these approaches have shown limited attention on the geometry of the dominant planes and the intersection between them, which has significant impact on room layout. In this work, we propose to incorporate geometric reasoning to deep learning for layout estimation. Our approach learns to infer the depth maps of the dominant planes in the scene by predicting the pixel-level surface parameters, and the layout can be generated by the intersection of the depth maps. Moreover, we present a new dataset with pixel-level depth annotation of dominant planes. It is larger than the existing datasets and contains both cuboid and non-cuboid rooms. Experimental results show that our approach produces considerable performance gains on both 2D and 3D datasets."
Location Sensitive Image Retrieval and Tagging
Authors: Raul Gomez; Jaume Gibert; Lluis Gomez; Dimosthenis Karatzas
Venue: ECCV 2020 (main)

Abstract: People from different parts of the globe describe objects and concepts in distinct manners. Visual appearance can thus vary across different geographic locations, which makes location a relevant contextual information when analysing visual data. In this work, we address the task of image retrieval related to a given tag conditioned on a certain location on Earth. We present LocSens, a model that learns to rank triplets of images, tags and coordinates by plausibility, and two training strategies to balance the location influence in the final ranking. LocSens learns to fuse textual and location information of multimodal queries to retrieve related images at different levels of location granularity, and successfully utilizes location information to improve image tagging. Code and models will be available to ensure reproducibility."
Joint 3D Layout and Depth Prediction from a Single Indoor Panorama Image
Authors: Wei Zeng; Sezer Karaoglu; Theo Gevers
Venue: ECCV 2020 (main)

Abstract: In this paper, we propose a method which jointly learns layout prediction and depth estimation from a single indoor panorama image. Previous methods have considered layout prediction and depth estimation from a single panorama image separately. However, these two tasks are tightly intertwined. Leveraging the layout depth map as an intermediate representation, our proposed method outperforms existing methods for both panorama layout prediction and depth estimation. Experiments on the challenging real-world dataset of Stanford 2D-3D demonstrate that our approach obtains superior performance for both the layout prediction tasks (3D IoU: 85.81% v.s. 79.79%) and the depth estimation (Abs Rel: 0.068 v.s. 0.079)."
Guessing State Tracking for Visual Dialogue
Authors: Wei Pang; Xiaojie Wang
Venue: ECCV 2020 (main)

Abstract: The Guesser is a task of visual grounding in GuessWhat?! like visual dialogue. It locates the target object in an image supposed by an Oracle oneself over a question-answer based dialogue between a Questioner and the Oracle. Most existing guessers make one and only one guess after receiving all question-answer pairs in a dialogue with the predefined number of rounds. This paper proposes a guessing state for the Guesser, and regards guess as a process with change of guessing state through a dialogue. A guessing state tracking based guess model is therefore proposed. The guessing state is defined as a distribution on objects in the image. With that in hand, two loss functions are defined as supervisions for model training. Early supervision brings supervision to Guesser at early rounds, and incremental supervision brings monotonicity to the guessing state. Experimental results on GuessWhat?! dataset show that our model significantly outperforms previous models, achieves new state-of-the-art, especially the success rate of guessing 83.3% is approaching the human-level accuracy of 84.4%."
Memory-Efficient Incremental Learning Through Feature Adaptation
Authors: Ahmet Iscen; Jeffrey Zhang; Svetlana Lazebnik; Cordelia Schmid
Venue: ECCV 2020 (main)

Abstract: We introduce an approach for incremental learning that preserves feature descriptors of training images from previously learned classes, instead of the images themselves, unlike most existing work. Keeping the much lower-dimensional feature embeddings of images reduces the memory footprint significantly. We assume that the model is updated incrementally for new classes as new data becomes available sequentially.This requires adapting the previously stored feature vectors to the updated feature space without having access to the corresponding original training images. Feature adaptation is learned with a multi-layer perceptron, which is trained on feature pairs corresponding to the outputs of the original and updated network on a training image. We validate experimentally that such a transformation generalizes well to the features of the previous set of classes, and maps features to a discriminative subspace in the feature space. As a result, the classifier is optimized jointly over new and old classes without requiring old class images. Experimental results show that our method achieves state-of-the-art classification accuracy in incremental learning benchmarks, while having at least an order of magnitude lower memory footprint compared to image-preserving strategies."