pwc-archive/papers-with-abstracts huggingface dataset:
Progressive Fusion Video Super-Resolution Network via Exploiting Non-Local Spatio-Temporal Correlations
Authors: Peng Yi,  Zhongyuan Wang,  Kui Jiang,  Junjun Jiang,  Jiayi Ma
Date: 2019-10-01 00:00:00
Proceeding: ICCV 2019 10
Tasks: Motion Compensation, Motion Estimation, Super-Resolution, Video Super-Resolution

Most previous fusion strategies either fail to fully utilize temporal information or cost too much time, and how to effectively fuse temporal information from consecutive frames plays an important role in video super-resolution (SR). In this study, we propose a novel progressive fusion network for video SR, which is designed to make better use of spatio-temporal information and is proved to be more efficient and effective than the existing direct fusion, slow fusion or 3D convolution strategies. Under this progressive fusion framework, we further introduce an improved non-local operation to avoid the complex motion estimation and motion compensation (ME&MC) procedures as in previous video SR approaches. Extensive experiments on public datasets demonstrate that our method surpasses state-of-the-art with 0.96 dB in average, and runs about 3 times faster, while requires only about half of the parameters.
Dynamic PET Image Reconstruction Using Nonnegative Matrix Factorization Incorporated With Deep Image Prior
Authors: Tatsuya Yokota,  Kazuya Kawai,  Muneyuki Sakata,  Yuichi Kimura,  Hidekata Hontani
Date: 2019-10-01 00:00:00
Proceeding: ICCV 2019 10
Tasks: Image Reconstruction

We propose a method that reconstructs dynamic positron emission tomography (PET) images from given sinograms by using non-negative matrix factorization (NMF) incorporated with a deep image prior (DIP) for appropriately constraining the spatial patterns of resultant images. The proposed method can reconstruct dynamic PET images with higher signal-to-noise ratio (SNR) and blindly decompose an image matrix into pairs of spatial and temporal factors. The former represent homogeneous tissues with different kinetic parameters and the latter represent the time activity curves that are observed in the corresponding homogeneous tissues. We employ U-Nets combined in parallel for DIP and each of the U-nets is used to extract each spatial factor decomposed from the data matrix. Experimental results show that the proposed method outperforms conventional methods and can extract spatial factors that represent the homogeneous tissues.
Seeing Motion in the Dark
Authors: Chen Chen,  Qifeng Chen,  Minh N. Do,  Vladlen Koltun
Date: 2019-10-01 00:00:00
Proceeding: ICCV 2019 10
Tasks: Low-Light Image Enhancement

Deep learning has recently been applied with impressive results to extreme low-light imaging. Despite the success of single-image processing, extreme low-light video processing is still intractable due to the difficulty of collecting raw video data with corresponding ground truth. Collecting long-exposure ground truth, as was done for single-image processing, is not feasible for dynamic scenes. In this paper, we present deep processing of very dark raw videos: on the order of one lux of illuminance. To support this line of work, we collect a new dataset of raw low-light videos, in which high-resolution raw data is captured at video rate. At this level of darkness, the signal-to-noise ratio is extremely low (negative if measured in dB) and the traditional image processing pipeline generally breaks down. A new method is presented to address this challenging problem. By carefully designing a learning-based pipeline and introducing a new loss function to encourage temporal stability, we train a siamese network on static raw videos, for which ground truth is available, such that the network generalizes to videos of dynamic scenes at test time. Experimental results demonstrate that the presented approach outperforms state-of-the-art models for burst processing, per-frame processing, and blind temporal consistency.
Self-Supervised Representation Learning From Multi-Domain Data
Authors: Zeyu Feng,  Chang Xu,  Dacheng Tao
Date: 2019-10-01 00:00:00
Proceeding: ICCV 2019 10
Tasks: Representation Learning, Self-Supervised Learning, Transfer Learning

We present an information-theoretically motivated constraint for self-supervised representation learning from multiple related domains. In contrast to previous self-supervised learning methods, our approach learns from multiple domains, which has the benefit of decreasing the build-in bias of individual domain, as well as leveraging information and allowing knowledge transfer across multiple domains. The proposed mutual information constraints encourage neural network to extract common invariant information across domains and to preserve peculiar information of each domain simultaneously. We adopt tractable upper and lower bounds of mutual information to make the proposed constraints solvable. The learned representation is more unbiased and robust toward the input images. Extensive experimental results on both multi-domain and large-scale datasets demonstrate the necessity and advantage of multi-domain self-supervised learning with mutual information constraints. Representations learned in our framework on state-of-the-art methods achieve improved performance than those learned on a single domain.
LAP-Net: Level-Aware Progressive Network for Image Dehazing
Authors: Yunan Li,  Qiguang Miao,  Wanli Ouyang,  Zhenxin Ma,  Huijuan Fang,  Chao Dong,  Yining Quan
Date: 2019-10-01 00:00:00
Proceeding: ICCV 2019 10
Tasks: Image Dehazing, Single Image Dehazing

In this paper, we propose a level-aware progressive network (LAP-Net) for single image dehazing. Unlike previous multi-stage algorithms that generally learn in a coarse-to-fine fashion, each stage of LAP-Net learns different levels of haze with different supervision. Then the network can progressively learn the gradually aggravating haze. With this design, each stage can focus on a region with specific haze level and restore clear details. To effectively fuse the results of varying haze levels at different stages, we develop an adaptive integration strategy to yield the final dehazed image. This strategy is achieved by a hierarchical integration scheme, which is in cooperation with the memory network and the domain knowledge of dehazing to highlight the best-restored regions of each stage. Extensive experiments on both real-world images and two dehazing benchmarks validate the effectiveness of our proposed method.
O2U-Net: A Simple Noisy Label Detection Approach for Deep Neural Networks
Authors: Jinchi Huang,  Lie Qu,  Rongfei Jia,  Binqiang Zhao
Date: 2019-10-01 00:00:00
Proceeding: ICCV 2019 10
Tasks: Active Learning, Learning with noisy labels

This paper proposes a novel noisy label detection approach, named O2U-net, for deep neural networks without human annotations. Different from prior work which requires specifically designed noise-robust loss functions or networks, O2U-net is easy to implement but effective. It only requires adjusting the hyper-parameters of the deep network to make its status transfer from overfitting to underfitting (O2U) cyclically. The losses of each sample are recorded during iterations. The higher the normalized average loss of a sample, the higher the probability of being noisy labels. O2U-net is naturally compatible with active learning and other human annotation approaches. This introduces extra flexibility for learning with noisy labels. We conduct sufficient experiments on multiple datasets in various settings. The experimental results prove the state-of-the-art of O2S-net.
Global Feature Guided Local Pooling
Authors: Takumi Kobayashi
Date: 2019-10-01 00:00:00
Proceeding: ICCV 2019 10
Tasks: General Classification, image-classification, Image Classification

In deep convolutional neural networks (CNNs), local pooling operation is a key building block to effectively downsize feature maps for reducing computation cost as well as increasing robustness against input variation. There are several types of pooling operation, such as average/max-pooling, from which one has to be manually selected for building CNNs. The optimal pooling type would be dependent on characteristics of features in CNNs and classification tasks, making it hard to find out the proper pooling module in advance. In this paper, we propose a flexible pooling method which adaptively tunes the pooling functionality based on input features without manually fixing it beforehand. In the proposed method, the parameterized pooling form is derived from a probabilistic perspective to flexibly represent various types of pooling and then the parameters are estimated by means of global statistics in the input feature map. Thus, the proposed local pooling guided by global features effectively works in the CNNs trained in an end-to-end manner. The experimental results on image classification tasks demonstrate the effectiveness of the proposed pooling method in various deep CNNs.
Information Entropy Based Feature Pooling for Convolutional Neural Networks
Authors: Weitao Wan,  Jiansheng Chen,  Tianpeng Li,  Yiqing Huang,  Jingqi Tian,  Cheng Yu,  Youze Xue
Date: 2019-10-01 00:00:00
Proceeding: ICCV 2019 10
Tasks: General Classification, Object Localization, Segmentation, Semantic Segmentation, Weakly-Supervised Object Localization, Weakly supervised Semantic Segmentation, Weakly-Supervised Semantic Segmentation

In convolutional neural networks (CNNs), we propose to estimate the importance of a feature vector at a spatial location in the feature maps by the network's uncertainty on its class prediction, which can be quantified using the information entropy. Based on this idea, we propose the entropy-based feature weighting method for semantics-aware feature pooling which can be readily integrated into various CNN architectures for both training and inference. We demonstrate that such a location-adaptive feature weighting mechanism helps the network to concentrate on semantically important image regions, leading to improvements in the large-scale classification and weakly-supervised semantic segmentation tasks. Furthermore, the generated feature weights can be utilized in visual tasks such as weakly-supervised object localization. We conduct extensive experiments on different datasets and CNN architectures, outperforming recently proposed pooling methods and attention mechanisms in ImageNet classification as well as achieving state-of-the-arts in weakly-supervised semantic segmentation on PASCAL VOC 2012 dataset.
DiscoNet: Shapes Learning on Disconnected Manifolds for 3D Editing
Authors: Eloi Mehr,  Ariane Jourdan,  Nicolas Thome,  Matthieu Cord,  Vincent Guitteny
Date: 2019-10-01 00:00:00
Proceeding: ICCV 2019 10

Editing 3D models is a very challenging task, as it requires complex interactions with the 3D shape to reach the targeted design, while preserving the global consistency and plausibility of the shape. In this work, we present an intelligent and user-friendly 3D editing tool, where the edited model is constrained to lie onto a learned manifold of realistic shapes. Due to the topological variability of real 3D models, they often lie close to a disconnected manifold, which cannot be learned with a common learning algorithm. Therefore, our tool is based on a new deep learning model, DiscoNet, which extends 3D surface autoencoders in two ways. Firstly, our deep learning model uses several autoencoders to automatically learn each connected component of a disconnected manifold, without any supervision. Secondly, each autoencoder infers the output 3D surface by deforming a pre-learned 3D template specific to each connected component. Both advances translate into improved 3D synthesis, thus enhancing the quality of our 3D editing tool.
Dynamic Multi-Scale Filters for Semantic Segmentation
Authors: Junjun He,  Zhongying Deng,  Yu Qiao
Date: 2019-10-01 00:00:00
Proceeding: ICCV 2019 10
Tasks: Scene Parsing, Segmentation, Semantic Segmentation

Multi-scale representation provides an effective way to address scale variation of objects and stuff in semantic segmentation. Previous works construct multi-scale representation by utilizing different filter sizes, expanding filter sizes with dilated filters or pooling grids, and the parameters of these filters are fixed after training. These methods often suffer from heavy computational cost or have more parameters, and are not adaptive to the input image during inference. To address these problems, this paper proposes a Dynamic Multi-scale Network (DMNet) to adaptively capture multi-scale contents for predicting pixel-level semantic labels. DMNet is composed of multiple Dynamic Convolutional Modules (DCMs) arranged in parallel, each of which exploits context-aware filters to estimate semantic representation for a specific scale. The outputs of multiple DCMs are further integrated for final segmentation. We conduct extensive experiments to evaluate our DMNet on three challenging semantic segmentation and scene parsing datasets, PASCAL VOC 2012, Pascal-Context, and ADE20K. DMNet achieves a new record 84.4% mIoU on PASCAL VOC 2012 test set without MS COCO pre-trained and post-processing, and also obtains state-of-the-art performance on Pascal-Context and ADE20K.
Deep Multiple-Attribute-Perceived Network for Real-World Texture Recognition
Authors: Wei Zhai,  Yang Cao,  Jing Zhang,  Zheng-Jun Zha
Date: 2019-10-01 00:00:00
Proceeding: ICCV 2019 10
Tasks: Attribute

Texture recognition is a challenging visual task as multiple perceptual attributes may be perceived from the same texture image when combined with different spatial context. Some recent works building upon Convolutional Neural Network (CNN) incorporate feature encoding with orderless aggregating to provide invariance to spatial layouts. However, these existing methods ignore visual texture attributes, which are important cues for describing the real-world texture images, resulting in incomplete description and inaccurate recognition. To address this problem, we propose a novel deep Multiple-Attribute-Perceived Network (MAP-Net) by progressively learning visual texture attributes in a mutually reinforced manner. Specifically, a multi-branch network architecture is devised, in which cascaded global contexts are learned by introducing similarity constraint at each branch, and leveraged as guidance of spatial feature encoding at next branch through an attribute transfer scheme. To enhance the modeling capability of spatial transformation, a deformable pooling strategy is introduced to augment the spatial sampling with adaptive offsets to the global context, leading to perceive new visual attributes. An attribute fusion module is then introduced to jointly utilize the perceived visual attributes and the abstracted semantic concepts at each branch. Experimental results on the five most challenging texture recognition datasets have demonstrated the superiority of the proposed model against the state-of-the-arts.
Person Search by Text Attribute Query As Zero-Shot Learning
Authors: Qi Dong,  Shaogang Gong,  Xiatian Zhu
Date: 2019-10-01 00:00:00
Proceeding: ICCV 2019 10
Tasks: Attribute, Person Search, Zero-Shot Learning

Existing person search methods predominantly assume the availability of at least one-shot imagery sample of the queried person. This assumption is limited in circumstances where only a brief textual (or verbal) description of the target person is available. In this work, we present a deep learning method for attribute text description based person search without any query imagery. Whilst conventional cross-modality matching methods, such as global visual-textual embedding based zero-shot learning and local individual attribute recognition, are functionally applicable, they are limited by several assumptions invalid to person search in deployment scale, data quality, and/or category name semantics. We overcome these issues by formulating an Attribute-Image Hierarchical Matching (AIHM) model. It is able to more reliably match text attribute descriptions with noisy surveillance person images by jointly learning global category-level and local attribute-level textual-visual embedding as well as matching. Extensive evaluations demonstrate the superiority of our AIHM model over a wide variety of state-of-the-art methods on three publicly available attribute labelled surveillance person search benchmarks: Market-1501, DukeMTMC, and PA100K.
Enhancing 2D Representation via Adjacent Views for 3D Shape Retrieval
Authors: Cheng Xu,  Zhaoqun Li,  Qiang Qiu,  Biao Leng,  Jingfei Jiang
Date: 2019-10-01 00:00:00
Proceeding: ICCV 2019 10
Tasks: 3D Shape Classification, 3D Shape Retrieval, Retrieval

Multi-view shape descriptors obtained from various 2D images are commonly adopted in 3D shape retrieval. One major challenge is that significant shape information are discarded during 2D view rendering through projection. In this paper, we propose a convolutional neural network based method, CenterNet, to enhance each individual 2D view using its neighboring ones. By exploiting cross-view correlations, CenterNet learns how adjacent views can be maximally incorporated for an enhanced 2D representation to effectively describe shapes. We observe that a very small amount of, e.g., six, enhanced 2D views, are already sufficient for a panoramic shape description. Thus, by simply aggregating features from six enhanced 2D views, we arrive at a highly compact yet discriminative shape descriptor. The proposed shape descriptor significantly outperforms state-of-the-art 3D shape retrieval methods on the ModelNet and ShapeNetCore55 benchmarks, and also exhibits robustness against object occlusion.
Adversarial Fine-Grained Composition Learning for Unseen Attribute-Object Recognition
Authors: Kun Wei,  Muli Yang,  Hao Wang,  Cheng Deng,  Xianglong Liu
Date: 2019-10-01 00:00:00
Proceeding: ICCV 2019 10
Tasks: Attribute, Object, Object Recognition

Recognizing unseen attribute-object pairs never appearing in the training data is a challenging task, since an object often refers to a specific entity while an attribute is an abstract semantic description. Besides, attributes are highly correlated to objects, i.e., an attribute tends to describe different visual features of various objects. Existing methods mainly employ two classifiers to recognize attribute and object separately, or simply simulate the composition of attribute and object, which ignore the inherent discrepancy and correlation between them. In this paper, we propose a novel adversarial fine-grained composition learning model for unseen attribute-object pair recognition. Considering their inherent discrepancy, we leverage multi-scale feature integration to capture discriminative fine-grained features from a given image. Besides, we devise a quintuplet loss to depict more accurate correlations between attributes and objects. Adversarial learning is employed to model the discrepancy and correlations among attributes and objects. Extensive experiments on two challenging benchmarks indicate that our method consistently outperforms state-of-the-art competitors by a large margin.
Second-Order Non-Local Attention Networks for Person Re-Identification
Authors: Bryan (Ning) Xia,  Yuan Gong,  Yizhe Zhang,  Christian Poellabauer
Date: 2019-10-01 00:00:00
Proceeding: ICCV 2019 10
Tasks: Person Re-Identification

Recent efforts have shown promising results for person re-identification by designing part-based architectures to allow a neural network to learn discriminative representations from semantically coherent parts. Some efforts use soft attention to reallocate distant outliers to their most similar parts, while others adjust part granularity to incorporate more distant positions for learning the relationships. Others seek to generalize part-based methods by introducing a dropout mechanism on consecutive regions of the feature map to enhance distant region relationships. However, only few prior efforts model the distant or non-local positions of the feature map directly for the person re-ID task. In this paper, we propose a novel attention mechanism to directly model long-range relationships via second-order feature statistics. When combined with a generalized DropBlock module, our method performs equally to or better than state-of-the-art results for mainstream person re-identification datasets, including Market1501, CUHK03, and DukeMTMC-reID.
Fast Computation of Content-Sensitive Superpixels and Supervoxels Using Q-Distances
Authors: Zipeng Ye,  Ran Yi,  Minjing Yu,  Yong-Jin Liu,  Ying He
Date: 2019-10-01 00:00:00
Proceeding: ICCV 2019 10
Tasks: Superpixels

State-of-the-art researches model the data of images and videos as low-dimensional manifolds and generate superpixels/supervoxels in a content-sensitive way, which is achieved by computing geodesic centroidal Voronoi tessellation (GCVT) on manifolds. However, computing exact GCVTs is slow due to computationally expensive geodesic distances. In this paper, we propose a much faster queue-based graph distance (called q-distance). Our key idea is that for manifold regions in which q-distances are different from geodesic distances, GCVT is prone to placing more generators in them, and therefore after few iterations, the q-distance-induced tessellation is an exact GCVT. This idea works well in practice and we also prove it theoretically under moderate assumption. Our method is simple and easy to implement. It runs 6-8 times faster than state-of-the-art GCVT computation, and has an optimal approximation ratio O(1) and a linear time complexity O(N) for N-pixel images or N-voxel videos. A thorough evaluation of 31 superpixel methods on five image datasets and 8 supervoxel methods on four video datasets shows that our method consistently achieves the best over-segmentation accuracy. We also demonstrate the advantage of our method on one image and two video applications.
Online Unsupervised Learning of the 3D Kinematic Structure of Arbitrary Rigid Bodies
Authors: Urbano Miguel Nunes,  Yiannis Demiris
Date: 2019-10-01 00:00:00
Proceeding: ICCV 2019 10

This work addresses the problem of 3D kinematic structure learning of arbitrary articulated rigid bodies from RGB-D data sequences. Typically, this problem is addressed by offline methods that process a batch of frames, assuming that complete point trajectories are available. However, this approach is not feasible when considering scenarios that require continuity and fluidity, for instance, human-robot interaction. In contrast, we propose to tackle this problem in an online unsupervised fashion, by recursively maintaining the metric distance of the scene's 3D structure, while achieving real-time performance. The influence of noise is mitigated by building a similarity measure based on a linear embedding representation and incorporating this representation into the original metric distance. The kinematic structure is then estimated based on a combination of implicit motion and spatial properties. The proposed approach achieves competitive performance both quantitatively and qualitatively in terms of estimation accuracy, even compared to offline methods.