pwc-archive/papers-with-abstracts huggingface dataset:
ALRe: Outlier Detection for Guided Refinement
Authors: Mingzhu Zhu, Zhang Gao, Junzhi Yu, Bingwei He, Jiantao Liu
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8
Tasks: Outlier Detection

Guided refinement is a popular procedure of various image post-processing applications. It produces output image based on input and guided images. Input images are usually flawed estimates containing kinds of noises and outliers, which undermine the edge consistency between input and guided images. As improvements, they are refined into output images with similar intensities of input images and consistent edges of guided images. However, outliers are usually untraceable and simply treated as zero-mean noises, limiting the quality of such refinement. In this paper, we propose a general outlier detection method for guided refinement. We assume local linear relationship between output and guided images to express the expected edge consistency, based on which, the outlier likelihoods of input pixels are measured. The metric is termed as ALRe (anchored linear residual) since it is essentially the residual of local linear regression with an equality constraint exerted on the measured pixel. Valuable features of the ALRe are discussed. Its effectiveness is proven by applications and experiment.
Weakly-Supervised Crowd Counting Learns from Sorting rather than Locations
Authors: Yifan Yang, Guorong Li, Zhe Wu, Li Su, Qingming Huang, Nicu Sebe
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8
Tasks: Crowd Counting

In crowd counting datasets, the location labels are costly, yet, they are not taken into the evaluation metrics. Besides, existing multi-task approaches employ high-level tasks to improve counting accuracy. This research tendency increases the demand for more annotations. In this paper, we propose a weakly-supervised counting network, which directly regresses the crowd numbers without the location supervision. Moreover, we train the network to count by exploiting the relationship among the images. We propose a soft-label sorting network along with the counting network, which sorts the given images by their crowd numbers. The sorting network drives the shared backbone CNN model to obtain density-sensitive ability explicitly. Therefore, the proposed method improves the counting accuracy by utilizing the information hidden in crowd numbers, rather than learning from extra labels, such as locations and perspectives.We evaluate our proposed method on three crowd counting datasets, and the performance of our method plays favorably against the fully supervised state-of-the-art approaches.
Enabling Deep Residual Networks for Weakly Supervised Object Detection
Authors: Yunhang Shen, Rongrong Ji, Yan Wang, Zhiwei Chen, Feng Zheng, Feiyue Huang, Yunsheng Wu
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8
Tasks: Object, object-detection, Object Detection, Weakly Supervised Object Detection

Weakly supervised object detection (WSOD) has attracted extensive research attention due to its great flexibility of exploiting large-scale image-level annotation for detector training. Whilst deep residual networks such as ResNet and DenseNet have become the standard backbones for many computer vision tasks, the cutting-edge WSOD methods still rely on plain networks, e.g., VGG, as backbones. It is indeed not trivial to employ deep residual networks for WSOD, which even shows significant deterioration of detection accuracy and non-convergence. In this paper, we discover the intrinsic root with sophisticated analysis and propose a sequence of design principles to take full advantages of deep residual learning for WSOD from the perspectives of adding redundancy, improving robustness and aligning features. First, a redundant adaptation neck is key for effective object instance localization and discriminative feature learning. Second, small-kernel convolutions and MaxPool down-samplings help improve the robustness of information flow, which gives finer object boundaries and make the detector more sensitivity to small objects. Third, dilated convolution is essential to align the proposal features and exploit diverse local information by extracting high-resolution feature maps. Extensive experiments show that the proposed principles enable deep residual networks to establishes new state-of-the-arts on PASCAL VOC and MS COCO.
Deep near-light photometric stereo for spatially varying reflectances
Authors: Hiroaki Santo, Michael Waechter, Yasuyuki Matsushita
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8

This paper presents a near-light photometric stereo method for spatially varying reflectances. Recent studies in photometric stereo proposed learning-based approaches to handle diverse real-world reflectances and achieve high accuracy compared to conventional methods. However, they assume distant (i.e., parallel) lights, which can in practical settings only be approximately realized, and they fail in near-light conditions. Near-light photometric stereo methods address near-light conditions but previous works are limited to over-simplified reflectances, such as Lambertian reflectance. The proposed method takes a hybrid approach of distant- and near-light models, where the surface normal of a small area (corresponding to a pixel) is computed locally with a distant light assumption, and the reconstruction error is assessed based on a near-light image formation model. This paper is the first work to solve unknown, spatially varying, diverse reflectances in near-light photometric stereo.
Regression of Instance Boundary by Aggregated CNN and GCN
Authors: Yanda Meng, Wei Meng, Dongxu Gao, Yitian Zhao, Xiaoyun Yang, Xiaowei Huang, Yalin Zheng
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8
Tasks: Image Segmentation, regression, Semantic Segmentation

This paper proposes a straightforward, intuitive deep learning approach for (biomedical) image segmentation tasks. Different from the existing dense pixel classification methods, we develop a novel multilevel aggregation network to directly regress the coordinates of the boundary of instances in an end-to-end manner. The network seamlessly combines standard convolution neural network (CNN) with Attention Refinement Module (ARM) and Graph Convolution Network (GCN). By iteratively and hierarchically fusing the features across different layers of the CNN, our approach gains sufficient semantic information from the input image and pays special attention to the local boundaries with the help of ARM and GCN. In particular, thanks to the proposed aggregation GCN, our network benefits from direct feature learning of the instances’ boundary locations and the spatial information propagation across the image. Experiments on several challenging datasets demonstrate that our method achieves comparable results with state-of-the-art approaches but requires less inference time on the segmentation of fetal head in ultrasound images and of optic disc and optic cup in color fundus images.
3PointTM: Faster Measurement of High-Dimensional Transmission Matrices
Authors: Yujun Chen, Manoj Kumar Sharma, Ashutosh Sabharwal, Ashok Veeraraghavan, Aswin C. Sankaranarayanan
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8
Tasks: Image Reconstruction, Retrieval, Vocal Bursts Intensity Prediction

A transmission matrix (TM) describes the linear relationship between input and output phasor fields when a coherent wave passes through a scattering medium. Measurement of the TM enables numerous applications, but is challenging and time-intensive for an arbitrary medium. State-of-the-art methods, including phase-shifting holography and double phase retrieval, require significant amounts of measurements, and post-capture reconstruction that is often computationally intensive. In this paper, we propose 3PointTM, an approach for sensing TMs that uses a minimal number of measurements per pixel - reducing the measurement budget by a factor of two as compared to state of the art in phase-shifting holography for measuring TMs - and has a low computational complexity as compared to phase retrieval. We validate our approach on real and simulated data, and show successful focusing of light and image reconstruction on dense scattering media.
Beyond 3DMM Space: Towards Fine-grained 3D Face Reconstruction
Authors: Xiangyu Zhu, Fan Yang, Di Huang, Chang Yu, Hao Wang, Jianzhu Guo, Zhen Lei, Stan Z. Li
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8
Tasks: 3D Face Reconstruction, Face Reconstruction

Recently, deep learning based 3D face reconstruction methods have shown promising results in both quality and efficiency. However, most of their training data is constructed by 3D Morphable Model, whose space spanned is only a small part of the shape space. As a result, the reconstruction results lose the fine-grained geometry and look different from real faces. To alleviate this issue, we first propose a solution to construct large-scale fine-grained 3D data from RGB-D images, which are expected to be massively collected as the proceeding of hand-held depth camera. A new dataset Fine-Grained 3D face (FG3D) with 200k samples is constructed to provide sufficient data for neural network training. Secondly, we propose a Fine-Grained reconstruction Network (FGNet) that can concentrate on shape modification by warping the network input and output to the UV space. Through FG3D and FGNet, we successfully generate reconstruction results with fine-grained geometry. The experiments on several benchmarks validate the effectiveness of our method compared to several baselines and other state-of-the-art methods.
AtlantaNet: Inferring the 3D Indoor Layout from a Single 360(∘) Image beyond the Manhattan World Assumption
Authors: Giovanni Pintore, Marco Agus, Enrico Gobbetti
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8
Tasks: 3D Room Layouts From A Single RGB Panorama, Decoder

We introduce a novel end-to-end approach to predict a 3D room layout from a single panoramic image. Compared to recent state-of-the-art works, our method is not limited to Manhattan World environments, and can reconstruct rooms bounded by vertical walls that do not form right angles or are curved -- i.e., Atlanta World models. In our approach, we project the original gravity-aligned panoramic image on two horizontal planes, one above and one below the camera. This representation encodes all the information needed to recover the Atlanta World 3D bounding surfaces of the room in the form of a 2D room footprint on the floor plan and a room height. To predict the 3D layout, we propose an encoder-decoder neural network architecture, leveraging Recurrent Neural Networks (RNNs) to capture long-range geometric patterns, and exploiting a customized training strategy based on domain-specific knowledge. The experimental results demonstrate that our method outperforms state-of-the-art solutions in prediction accuracy, in particular in cases of complex wall layouts or curved wall footprints.
n-Reference Transfer Learning for Saliency Prediction
Authors: Yan Luo, Yongkang Wong, Mohan S. Kankanhalli, Qi Zhao
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8
Tasks: Prediction, Saliency Prediction, Transfer Learning

Benefiting from deep learning research and large-scale datasets, saliency prediction has achieved significant success in the past decade. However, it still remains challenging to predict saliency maps on images in new domains that lack sufficient data for data-hungry models. To solve this problem, we propose a few-shot transfer learning paradigm for saliency prediction, which enables efficient transfer of knowledge learned from the existing large-scale saliency datasets to a target domain with limited labeled examples. Specifically, very few target domain examples are used as the reference to train a model with a source domain dataset such that the training process can converge to a local minimum in favor of the target domain. Then, the learned model is further fine-tuned with the reference. The proposed framework is gradient-based and model-agnostic. We conduct comprehensive experiments and ablation study on various source domain and target domain pairs. The results show that the proposed framework achieves a significant performance improvement. The code is publicly available at https://github.com/luoyan407/n-reference.
On Modulating the Gradient for Meta-Learning
Authors: Christian Simon, Piotr Koniusz, Richard Nock, Mehrtash Harandi
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8
Tasks: Meta-Learning

Inspired by optimization techniques, we propose a novel meta-learning algorithm with gradient modulation to encourage fast-adaptation of neural networks in the absence of abundant data. Our method, termed ModGrad, is designed to circumvent the noisy nature of the gradients which is prevalent in low-data regimes. Furthermore and having the scalability concern in mind, we formulate ModGrad via low-rank approximations, which in turn enables us to employ ModGrad to adapt hefty neural networks. We thoroughly assess and contrast ModGrad against a large family of meta-learning techniques and observe that the proposed algorithm outperforms baselines comfortably while enjoying faster convergence.
Deep Transferring Quantization
Authors: Zheng Xie, Zhiquan Wen, Jing Liu, Zhi-Qiang Liu, Xixian Wu, Mingkui Tan
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8
Tasks: Face Recognition, image-classification, Image Classification, Quantization, Transfer Learning

Network quantization is an effective method for network compression. Existing methods train a low-precision network by fine-tuning from a pre-trained model. However, training a low-precision network often requires large-scale labeled data to achieve superior performance. In many real-world scenarios, only limited labeled data are available due to expensive labeling costs or privacy protection. With limited training data, fine-tuning methods may suffer from the overfitting issue and substantial accuracy loss. To alleviate these issues, we introduce transfer learning into network quantization to obtain an accurate low-precision model. Specifically, we propose a method named deep transferring quantization (DTQ) to effectively exploit the knowledge in a pre-trained full-precision model. To this end, we propose a learnable attentive transfer module to identify the informative channels for alignment. In addition, we introduce the Kullback–Leibler (KL) divergence to further help train a low-precision model. Extensive experiments on both image classification and face recognition demonstrate the effectiveness of DTQ.
Deep Credible Metric Learning for Unsupervised Domain Adaptation Person Re-identification
Authors: Guangyi Chen, Yuhao Lu, Jiwen Lu, Jie Zhou
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8
Tasks: Domain Adaptation, Metric Learning, Person Re-Identification, Pseudo Label, Unsupervised Domain Adaptation

The trained person re-identification systems fundamentally need to be deployed on different target environments. Learning the cross-domain model has great potential for the scalability of real-world applications. In this paper, we propose a deep credible metric learning (DCML) method for unsupervised domain adaptation person re-identification. Unlike existing methods that directly finetune the model in the target domain with pseudo labels generated by the source pre-trained model, our DCML method adaptively mines credible samples for training to avoid the misleading from noise labels. Specifically, we design two credibility metrics for sample mining including the k-Nearest Neighbor similarity for density evaluation and the prototype similarity for centrality evaluation. As the increasing of the pseudo label credibility, we progressively adjust the sampling strategy in the training process. In addition, we propose an instance margin spreading loss to further increase instance-wise discrimination. Experimental results demonstrate that our DCML method explores credible and valuable training data and improves the performance of unsupervised domain adaptation.
Temporal Coherence or Temporal Motion: Which is More Critical for Video-based Person Re-identification?
Authors: Guangyi Chen, Yongming Rao, Jiwen Lu, Jie zhou
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8
Tasks: Person Re-Identification, Video-Based Person Re-Identification

Video-based person re-identification aims to match pedestrians with the consecutive video sequences. While a rich line of work focuses solely on extracting the motion features from pedestrian videos, we show in this paper that the temporal coherence plays a more critical role. To distill the temporal coherence part of video representation from frame representations, we propose a simple yet effective Adversarial Feature Augmentation (AFA) method, which highlights the temporal coherence features by introducing adversarial augmented temporal motion noise. Specifically, we disentangle the video representation into the temporal coherence and motion parts and randomly change the scale of the temporal motion features as the adversarial noise. The proposed AFA method is a general lightweight component that can be readily incorporated into various methods with negligible cost. We conduct extensive experiments on three challenging datasets including MARS, iLIDS-VID, and DukeMTMC-VideoReID, and the experimental results verify our argument and demonstrate the effectiveness of the proposed method.
Learning Event-Driven Video Deblurring and Interpolation
Authors: Songnan Lin, Jiawei Zhang, Jinshan Pan, Zhe Jiang, Dongqing Zou, Yongtian Wang, Jing Chen, Jimmy Ren
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8
Tasks: Deblurring, Video Deblurring

Event-based sensors, which have a response if the change of pixel intensity exceeds a triggering threshold, can capture high-speed motion with microsecond accuracy. Assisted by an event camera, we can generate high frame-rate sharp videos from low frame-rate blurry ones captured by an intensity camera. In this paper, we propose an effective event-driven video deblurring and interpolation algorithm based on deep convolutional neural networks (CNNs). Motivated by the physical model that the residuals between a blurry image and sharp frames are the integrals of events, the proposed network uses events to estimate the residuals for the sharp frame restoration. As the triggering threshold varies spatially, we develop an effective method to estimate dynamic filters to solve this problem. To utilize the temporal information, the sharp frames restored from the previous blurry frame are also considered. The proposed algorithm achieves superior performance against state-of-the-art methods on both synthetic and real datasets.
Monocular 3D Object Detection via Feature Domain Adaptation
Authors: Lele Chen, Guofeng Cui, Celong Liu, Zhong Li, Ziyi Kou, Yi Xu, Chenliang Xu
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8
Tasks: 3D Object Detection, Domain Adaptation, Foreground Segmentation, Monocular 3D Object Detection, Object, object-detection, Object Detection

Monocular 3D object detection is a challenging task due to unreliable depth, resulting in a distinct performance gap between monocular and LiDAR-based approaches. In this paper, we propose a novel domain adaptation based monocular 3D object detection framework named DA-3Ddet, which adapts the feature from unsound image-based pseudo-LiDAR domain to the accurate real LiDAR domain for performance boosting. In order to solve the overlooked problem of inconsistency between the foreground mask of pseudo and real LiDAR caused by inaccurately estimated depth, we also introduce a context-aware foreground segmentation module which helps to involve relevant points for foreground masking. Extensive experiments on KITTI dataset demonstrate that our simple yet effective framework outperforms other state-of-the-arts by a large margin.
Relative Pose Estimation of Calibrated Cameras with Known SE(3) Invariants
Authors: Bo Li, Evgeniy Martyushev, Gim Hee Lee
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8
Tasks: Pose Estimation, Translation

The $\mathrm{SE}(3)$ invariants of a pose include its rotation angle and screw translation. In this paper, we present a complete comprehensive study of the relative pose estimation problem for a calibrated camera constrained by known $\mathrm{SE}(3)$ invariant, which involves 5 minimal problems in total. These problems reduces the minimal number of point pairs for relative pose estimation and improves the estimation efficiency and robustness. The $\mathrm{SE}(3)$ invariant constraints can come from extra sensor measurements or motion assumption. Different from conventional relative pose estimation with extra constraints, no extrinsic calibration is required to transform the constraints to the camera frame. This advantage comes from the invariance of $\mathrm{SE}(3)$ invariants cross different coordinate systems on a rigid body and makes the solvers more convenient and flexible in practical applications. Besides proposing the concept of relative pose estimation constrained by $\mathrm{SE}(3)$ invariants, we present a comprehensive study of existing polynomial formulations for relative pose estimation and discover their relationship. Different formulations are carefully chosen for each proposed problems to achieve best efficiency. Experiments on synthetic and real data shows performance improvement compared to conventional relative pose estimation methods.
Sequential Convolution and Runge-Kutta Residual Architecture for Image Compressed Sensing
Authors: Runkai Zheng, Yinqi Zhang, Daolang Huang, Qingliang Chen
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8
Tasks: compressed sensing, Image Compressed Sensing, Image Compression

In recent years, Deep Neural Networks (DNN) have empowered Compressed Sensing (CS) substantially and have achieved high reconstruction quality and speed far exceeding traditional CS methods. However, there are still lots of issues to be further explored before it can be practical enough. There are mainly two challenging problems in CS, one is to achieve efficient data sampling, and the other is to reconstruct images with high-quality. To address the two challenges, this paper proposes a novel Runge-Kutta Convolutional Compressed Sensing Network (RK-CCSNet). In the sensing stage, RK-CCSNet applies Sequential Convolutional Module (SCM) to gradually compact measurements through a series of convolution filters. In the reconstruction stage, RK-CCSNet establishes a novel Learned Runge-Kutta Block (LRKB) based on the famous Runge-Kutta methods, reformulating the process of image reconstruction as a discrete dynamical system. Finally, the implementation of RK-CCSNet achieves state-of-the-art performance on influential benchmarks with respect to prestigious baselines, and all the codes are available at https://github.com/rkteddy/RK-CCSNet.
Contrastive Learning for Unpaired Image-to-Image Translation
Authors: Taesung Park Alexei A. Efros Richard Zhang Jun-Yan Zhu
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8
Tasks: Contrastive Learning, Image Generation, Image-to-Image Translation, Translation

In image-to-image translation, each patch in the output should reflect the content of the corresponding patch in the input, independent of domain. We propose a straightforward method for doing so -- maximizing mutual information between the two, using a framework based on contrastive learning. The method encourages two elements (corresponding patches) to map to a similar point in a learned feature space, relative to other elements (other patches) in the dataset, referred to as negatives. We explore several critical design choices for making contrastive learning effective in the image synthesis setting. Notably, we use a multilayer, patch-based approach, rather than operate on entire images. Furthermore, we draw negatives from within the input image itself, rather than from the rest of the dataset. We demonstrate that our framework enables one-sided translation in the unpaired image-to-image translation setting, while improving quality and reducing training time. In addition, our method can even be extended to the training setting where each ``domain'' is only a single image.