pwc-archive/papers-with-abstracts huggingface dataset:
Leveraging Long-Range Temporal Relationships Between Proposals for Video Object Detection
Authors: Mykhailo Shvets,  Wei Liu,  Alexander C. Berg
Date: 2019-10-01 00:00:00
Proceeding: ICCV 2019 10
Tasks: Object, object-detection, Object Detection, Video Object Detection

Single-frame object detectors perform well on videos sometimes, even without temporal context. However, challenges such as occlusion, motion blur, and rare poses of objects are hard to resolve without temporal awareness. Thus, there is a strong need to improve video object detection by considering long-range temporal dependencies. In this paper, we present a light-weight modification to a single-frame detector that accounts for arbitrary long dependencies in a video. It improves the accuracy of a single-frame detector significantly with negligible compute overhead. The key component of our approach is a novel temporal relation module, operating on object proposals, that learns the similarities between proposals from different frames and selects proposals from past and/or future to support current proposals. Our final "causal" model, without any offline post-processing steps, runs at a similar speed as a single-frame detector and achieves state-of-the-art video object detection on ImageNet VID dataset.
Deep Supervised Hashing With Anchor Graph
Authors: Yudong Chen,  Zhihui Lai,  Yujuan Ding,  Kaiyi Lin,  Wai Keung Wong
Date: 2019-10-01 00:00:00
Proceeding: ICCV 2019 10
Tasks: Deep Hashing, Retrieval

Recently, a series of deep supervised hashing methods were proposed for binary code learning. However, due to the high computation cost and the limited hardware's memory, these methods will first select a subset from the training set, and then form a mini-batch data to update the network in each iteration. Therefore, the remaining labeled data cannot be fully utilized and the model cannot directly obtain the binary codes of the entire training set for retrieval. To address these problems, this paper proposes an interesting regularized deep model to seamlessly integrate the advantages of deep hashing and efficient binary code learning by using the anchor graph. As such, the deep features and label matrix can be jointly used to optimize the binary codes, and the network can obtain more discriminative feedback from the linear combinations of the learned bits. Moreover, we also reveal the algorithm mechanism and its computation essence. Experiments on three large-scale datasets indicate that the proposed method achieves better retrieval performance with less training time compared to previous deep hashing methods.
Hierarchical Encoding of Sequential Data With Compact and Sub-Linear Storage Cost
Authors: Huu Le,  Ming Xu,  Tuan Hoang,  Michael Milford
Date: 2019-10-01 00:00:00
Proceeding: ICCV 2019 10
Tasks: Quantization, Simultaneous Localization and Mapping, Visual Localization

 Snapshot-based visual localization is an important problem in several computer vision and robotics applications such as Simultaneous Localization And Mapping (SLAM). To achieve real-time performance in very large-scale environments with massive amounts of training and map data, techniques such as approximate nearest neighbor search (ANN) algorithms are used. While several state-of-the-art variants of quantization and indexing techniques have demonstrated to be efficient in practice, their theoretical memory cost still scales at least linearly with the training data (i.e., O(n) where n is the number of instances in the database), since each data point must be associated with at least one code vector. To address these limitations, in this paper we present a totally new hierarchical encoding approach that enables a sub-linear storage scale. The algorithm exploits the widespread sequential nature of sensor information streams in robotics and autonomous vehicle applications and achieves, both theoretically and experimentally, sub-linear scalability in storage required for a given environment size. Furthermore, the associated query time of our algorithm is also of sub-linear complexity. We benchmark the performance of the proposed algorithm on several real-world benchmark datasets and experimentally validate the theoretical sub-linearity of our approach, while also showing that our approach yields competitive absolute storage performance as well.
C-MIDN: Coupled Multiple Instance Detection Network With Segmentation Guidance for Weakly Supervised Object Detection
Authors: Yan Gao,  Boxiao Liu,  Nan Guo,  Xiaochun Ye,  Fang Wan,  Haihang You,  Dongrui Fan
Date: 2019-10-01 00:00:00
Proceeding: ICCV 2019 10
Tasks: Multiple Instance Learning, object-detection, Object Detection, Weakly Supervised Object Detection

Weakly supervised object detection (WSOD) that only needs image-level annotations has obtained much attention recently. By combining convolutional neural network with multiple instance learning method, Multiple Instance Detection Network (MIDN) has become the most popular method to address the WSOD problem and been adopted as the initial model in many works. We argue that MIDN inclines to converge to the most discriminative object parts, which limits the performance of methods based on it. In this paper, we propose a novel Coupled Multiple Instance Detection Network (C-MIDN) to address this problem. Specifically, we use a pair of MIDNs, which work in a complementary manner with proposal removal. The localization information of the MIDNs is further coupled to obtain tighter bounding boxes and localize multiple objects. We also introduce a Segmentation Guided Proposal Removal (SGPR) algorithm to guarantee the MIL constraint after the removal and ensure the robustness of C-MIDN. Through a simple implementation of the C-MIDN with online detector refinement, we obtain 53.6% and 50.3% mAP on the challenging PASCAL VOC 2007 and 2012 benchmarks respectively, which significantly outperform the previous state-of-the-arts.
Subspace Structure-Aware Spectral Clustering for Robust Subspace Clustering
Authors: Masataka Yamaguchi,  Go Irie,  Takahito Kawanishi,  Kunio Kashino
Date: 2019-10-01 00:00:00
Proceeding: ICCV 2019 10
Tasks: Clustering, Graph Clustering, graph construction

Subspace clustering is the problem of partitioning data drawn from a union of multiple subspaces. The most popular subspace clustering framework in recent years is the graph clustering-based approach, which performs subspace clustering in two steps: graph construction and graph clustering. Although both steps are equally important for accurate clustering, the vast majority of work has focused on improving the graph construction step rather than the graph clustering step. In this paper, we propose a novel graph clustering framework for robust subspace clustering. By incorporating a geometry-aware term with the spectral clustering objective, we encourage our framework to be robust to noise and outliers in given affinity matrices. We also develop an efficient expectation-maximization-based algorithm for optimization. Through extensive experiments on four real-world datasets, we demonstrate that the proposed method outperforms existing methods.
Order-Preserving Wasserstein Discriminant Analysis
Authors: Bing Su,  Jiahuan Zhou,  Ying Wu
Date: 2019-10-01 00:00:00
Proceeding: ICCV 2019 10
Tasks: 3D Action Recognition, Action Recognition, Dimensionality Reduction, Supervised dimensionality reduction

Supervised dimensionality reduction for sequence data projects the observations in sequences onto a low-dimensional subspace to better separate different sequence classes. It is typically more challenging than conventional dimensionality reduction for static data, because measuring the separability of sequences involves non-linear procedures to manipulate the temporal structures. This paper presents a linear method, namely Order-preserving Wasserstein Discriminant Analysis (OWDA), which learns the projection by maximizing the inter-class distance and minimizing the intra-class scatter. For each class, OWDA extracts the order-preserving Wasserstein barycenter and constructs the intra-class scatter as the dispersion of the training sequences around the barycenter. The inter-class distance is measured as the order-preserving Wasserstein distance between the corresponding barycenters. OWDA is able to concentrate on the distinctive differences among classes by lifting the geometric relations with temporal constraints. Experiments show that OWDA achieves competitive results on three 3D action recognition datasets.
Robust Variational Bayesian Point Set Registration
Authors: Jie Zhou,  Xinke Ma,  Li Liang,  Yang Yang,  Shijin Xu,  Yuhe Liu,  Sim-Heng Ong
Date: 2019-10-01 00:00:00
Proceeding: ICCV 2019 10
Tasks: Variational Inference

In this work, we propose a hierarchical Bayesian network based point set registration method to solve missing correspondences and various massive outliers. We construct this network first using the finite Student s t latent mixture model (TLMM), in which distributions of latent variables are estimated by a tree-structured variational inference (VI) so that to obtain a tighter lower bound under the Bayesian framework. We then divide the TLMM into two different mixtures with isotropic and anisotropic covariances for correspondences recovering and outliers identification, respectively. Finally, the parameters of mixing proportion and covariances are both taken as latent variables, which benefits explaining of missing correspondences and heteroscedastic outliers. In addition, a cooling schedule is adopted to anneal prior on covariances and scale variables within designed two phases of transformation, it anneal priors on global and local variables to perform a coarse-to- fine registration. In experiments, our method outperforms five state-of-the-art methods in synthetic point set and realistic imaging registrations.
Meta-Learning to Detect Rare Objects
Authors: Yu-Xiong Wang,  Deva Ramanan,  Martial Hebert
Date: 2019-10-01 00:00:00
Proceeding: ICCV 2019 10
Tasks: Few-Shot Learning, Few-Shot Object Detection, General Classification, Meta-Learning, Novel Concepts, object-detection, Object Detection

Few-shot learning, i.e., learning novel concepts from few examples, is fundamental to practical visual recognition systems. While most of existing work has focused on few-shot classification, we make a step towards few-shot object detection, a more challenging yet under-explored task. We develop a conceptually simple but powerful meta-learning based framework that simultaneously tackles few-shot classification and few-shot localization in a unified, coherent way. This framework leverages meta-level knowledge about "model parameter generation" from base classes with abundant data to facilitate the generation of a detector for novel classes. Our key insight is to disentangle the learning of category-agnostic and category-specific components in a CNN based detection model. In particular, we introduce a weight prediction meta-model that enables predicting the parameters of category-specific components from few examples. We systematically benchmark the performance of modern detectors in the small-sample size regime. Experiments in a variety of realistic scenarios, including within-domain, cross-domain, and long-tailed settings, demonstrate the effectiveness and generality of our approach under different notions of novel classes.
New Convex Relaxations for MRF Inference With Unknown Graphs
Authors: Zhenhua Wang,  Tong Liu,  Qinfeng Shi,  M. Pawan Kumar,  Jianhua Zhang
Date: 2019-10-01 00:00:00
Proceeding: ICCV 2019 10
Tasks: Activity Recognition, Human Activity Recognition

Treating graph structures of Markov random fields as unknown and estimating them jointly with labels have been shown to be useful for modeling human activity recognition and other related tasks. We propose two novel relaxations for solving this problem. The first is a linear programming (LP) relaxation, which is provably tighter than the existing LP relaxation. The second is a non-convex quadratic programming (QP) relaxation, which admits an efficient concave-convex procedure (CCCP). The CCCP algorithm is initialized by solving a convex QP relaxation of the problem, which is obtained by modifying the diagonal of the matrix that specifies the non-convex QP relaxation. We show that our convex QP relaxation is optimal in the sense that it minimizes the L1 norm of the diagonal modification vector. While the convex QP relaxation is not as tight as the existing and the new LP relaxations, when used in conjunction with the CCCP algorithm for the non-convex QP relaxation, it provides accurate solutions. We demonstrate the efficacy of our new relaxations for both synthetic data and human activity recognition.
GEOBIT: A Geodesic-Based Binary Descriptor Invariant to Non-Rigid Deformations for RGB-D Images
Authors: Erickson R. Nascimento,  Guilherme Potje,  Renato Martins,  Felipe Cadar,  Mario F. M. Campos,  Ruzena Bajcsy
Date: 2019-10-01 00:00:00
Proceeding: ICCV 2019 10

At the core of most three-dimensional alignment and tracking tasks resides the critical problem of point correspondence. In this context, the design of descriptors that efficiently and uniquely identifies keypoints, to be matched, is of central importance. Numerous descriptors have been developed for dealing with affine/perspective warps, but few can also handle non-rigid deformations. In this paper, we introduce a novel binary RGB-D descriptor invariant to isometric deformations. Our method uses geodesic isocurves on smooth textured manifolds. It combines appearance and geometric information from RGB-D images to tackle non-rigid transformations. We used our descriptor to track multiple textured depth maps and demonstrate that it produces reliable feature descriptors even in the presence of strong non-rigid deformations and depth noise. The experiments show that our descriptor outperforms different state-of-the-art descriptors in both precision-recall and recognition rate metrics. We also provide to the community a new dataset composed of annotated RGB-D images of different objects (shirts, cloths, paintings, bags), subjected to strong non-rigid deformations, to evaluate point correspondence algorithms.
CDTB: A Color and Depth Visual Object Tracking Dataset and Benchmark
Authors: Alan Lukezic,  Ugur Kart,  Jani Kapyla,  Ahmed Durmush,  Joni-Kristian Kamarainen,  Jiri Matas,  Matej Kristan
Date: 2019-10-01 00:00:00
Proceeding: ICCV 2019 10
Tasks: Object Tracking, Visual Object Tracking

We propose a new color-and-depth general visual object tracking benchmark (CDTB). CDTB is recorded by several passive and active RGB-D setups and contains indoor as well as outdoor sequences acquired in direct sunlight. The CDTB dataset is the largest and most diverse dataset in RGB-D tracking, with an order of magnitude larger number of frames than related datasets. The sequences have been carefully recorded to contain significant object pose change, clutter, occlusion, and periods of long-term target absence to enable tracker evaluation under realistic conditions. Sequences are per-frame annotated with 13 visual attributes for detailed analysis. Experiments with RGB and RGB-D trackers show that CDTB is more challenging than previous datasets. State-of-the-art RGB trackers outperform the recent RGB-D trackers, indicating a large gap between the two fields, which has not been previously detected by the prior benchmarks. Based on the results of the analysis we point out opportunities for future research in RGB-D tracker design.
M2FPA: A Multi-Yaw Multi-Pitch High-Quality Dataset and Benchmark for Facial Pose Analysis
Authors: Pei-Pei Li,  Xiang Wu,  Yibo Hu,  Ran He,  Zhenan Sun
Date: 2019-10-01 00:00:00
Proceeding: ICCV 2019 10
Tasks: Attribute, Face Generation, Face Recognition, Pose Estimation, Robust Face Recognition

Facial images in surveillance or mobile scenarios often have large view-point variations in terms of pitch and yaw angles. These jointly occurred angle variations make face recognition challenging. Current public face databases mainly consider the case of yaw variations. In this paper, a new large-scale Multi-yaw Multi-pitch high-quality database is proposed for Facial Pose Analysis (M2FPA), including face frontalization, face rotation, facial pose estimation and pose-invariant face recognition. It contains 397,544 images of 229 subjects with yaw, pitch, attribute, illumination and accessory. M2FPA is the most comprehensive multi-view face database for facial pose analysis. Further, we provide an effective benchmark for face frontalization and pose-invariant face recognition on M2FPA with several state-of-the-art methods, including DR-GAN, TP-GAN and CAPG-GAN. We believe that the new database and benchmark can significantly push forward the advance of facial pose analysis in real-world applications. Moreover, a simple yet effective parsing guided discriminator is introduced to capture the local consistency during GAN optimization. Extensive quantitative and qualitative results on M2FPA and Multi-PIE demonstrate the superiority of our face frontalization method. Baseline results for both face synthesis and face recognition from state-of-the-art methods demonstrate the challenge offered by this new database.
Fair Loss: Margin-Aware Reinforcement Learning for Deep Face Recognition
Authors: Bingyu Liu,  Weihong Deng,  Yaoyao Zhong,  Mei Wang,  Jiani Hu,  Xunqiang Tao,  Yaohai Huang
Date: 2019-10-01 00:00:00
Proceeding: ICCV 2019 10
Tasks: Face Recognition, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Recently, large-margin softmax loss methods, such as angular softmax loss (SphereFace), large margin cosine loss (CosFace), and additive angular margin loss (ArcFace), have demonstrated impressive performance on deep face recognition. These methods incorporate a fixed additive margin to all the classes, ignoring the class imbalance problem. However, imbalanced problem widely exists in various real-world face datasets, in which samples from some classes are in a higher number than others. We argue that the number of a class would influence its demand for the additive margin. In this paper, we introduce a new margin-aware reinforcement learning based loss function, namely fair loss, in which each class will learn an appropriate adaptive margin by Deep Q-learning. Specifically, we train an agent to learn a margin adaptive strategy for each class, and make the additive margins for different classes more reasonable. Our method has better performance than present large-margin loss functions on three benchmarks, Labeled Face in the Wild (LFW), Youtube Faces (YTF) and MegaFace, which demonstrates that our method could learn better face representation on imbalanced face datasets.
Through-Wall Human Mesh Recovery Using Radio Signals
Authors: Ming-Min Zhao,  Yingcheng Liu,  Aniruddh Raghu,  Tianhong Li,  Hang Zhao,  Antonio Torralba,  Dina Katabi
Date: 2019-10-01 00:00:00
Proceeding: ICCV 2019 10
Tasks: Human Mesh Recovery

This paper presents RF-Avatar, a neural network model that can estimate 3D meshes of the human body in the presence of occlusions, baggy clothes, and bad lighting conditions. We leverage that radio frequency (RF) signals in the WiFi range traverse clothes and occlusions and bounce off the human body. Our model parses such radio signals and recovers 3D body meshes. Our meshes are dynamic and smoothly track the movements of the corresponding people. Further, our model works both in single and multi-person scenarios. Inferring body meshes from radio signals is a highly under-constrained problem. Our model deals with this challenge using: 1) a combination of strong and weak supervision, 2) a multi-headed self-attention mechanism that attends differently to temporal information in the radio signal, and 3) an adversarially trained temporal discriminator that imposes a prior on the dynamics of human motion. Our results show that RF-Avatar accurately recovers dynamic 3D meshes in the presence of occlusions, baggy clothes, bad lighting conditions, and even through walls.
Discriminatively Learned Convex Models for Set Based Face Recognition
Authors: Hakan Cevikalp,  Golara Ghorban Dordinejad
Date: 2019-10-01 00:00:00
Proceeding: ICCV 2019 10
Tasks: Face Recognition

Majority of the image set based face recognition methods use a generatively learned model for each person that is learned independently by ignoring the other persons in the gallery set. In contrast to these methods, this paper introduces a novel method that searches for discriminative convex models that best fit to an individual's face images but at the same time are as far as possible from the images of other persons in the gallery. We learn discriminative convex models for both affine and convex hulls of image sets. During testing, distances from the query set images to these models are computed efficiently by using simple matrix multiplications, and the query set is assigned to the person in the gallery whose image set is closest to the query images. The proposed method significantly outperforms other methods using generative convex models in terms of both accuracy and testing time, and achieves the state-of-the-art results on four of the five tested datasets. Especially, the accuracy improvement is significant on the challenging PaSC, COX and ESOGU video datasets.
Deep Head Pose Estimation Using Synthetic Images and Partial Adversarial Domain Adaption for Continuous Label Spaces
Authors: Felix Kuhnke,  Jorn Ostermann
Date: 2019-10-01 00:00:00
Proceeding: ICCV 2019 10
Tasks: Domain Adaptation, Head Pose Estimation, Pose Estimation

Head pose estimation aims at predicting an accurate pose from an image. Current approaches rely on supervised deep learning, which typically requires large amounts of labeled data. Manual or sensor-based annotations of head poses are prone to errors. A solution is to generate synthetic training data by rendering 3D face models. However, the differences (domain gap) between rendered (source-domain) and real-world (target-domain) images can cause low performance. Advances in visual domain adaptation allow reducing the influence of domain differences using adversarial neural networks, which match the feature spaces between domains by enforcing domain-invariant features. While previous work on visual domain adaptation generally assumes discrete and shared label spaces, these assumptions are both invalid for pose estimation tasks. We are the first to present domain adaptation for head pose estimation with a focus on partially shared and continuous label spaces. More precisely, we adapt the predominant weighting approaches to continuous label spaces by applying a weighted resampling of the source domain during training. To evaluate our approach, we revise and extend existing datasets resulting in a new benchmark for visual domain adaption. Our experiments show that our method improves the accuracy of head pose estimation for real-world images despite using only labels from synthetic images.
Flare in Interference-Based Hyperspectral Cameras
Authors: Eden Sassoon,  Yoav Y. Schechner,  Tali Treibitz
Date: 2019-10-01 00:00:00
Proceeding: ICCV 2019 10

Stray light (flare) is formed inside cameras by internal reflections between optical elements. We point out a flare effect of significant magnitude and implication to snapshot hyperspectral imagers. Recent technologies enable placing interference-based filters on individual pixels in imaging sensors. These filters have narrow transmission bands around custom wavelengths and high transmission efficiency. Cameras using arrays of such filters are compact, robust and fast. However, as opposed to traditional broad-band filters, which often absorb unwanted light, narrow band-pass interference filters reflect non-transmitted light. This is a source of very significant flare which biases hyperspectral measurements. The bias in any pixel depends on spectral content in other pixels. We present a theoretical image formation model for this effect, and quantify it through simulations and experiments. In addition, we test deflaring of signals affected by such flare.