pwc-archive/papers-with-abstracts huggingface dataset:
RDFNet: RGB-D Multi-Level Residual Feature Fusion for Indoor Semantic Segmentation
Authors: Seong-Jin Park, Ki-Sang Hong, Seungyong Lee
Date: 2017-10-01 00:00:00
Proceeding: ICCV 2017 10
Tasks: Segmentation, Semantic Segmentation

In multi-class indoor semantic segmentation using RGB-D data, it has been shown that incorporating depth feature into RGB feature is helpful to improve segmentation accuracy. However, previous studies have not fully exploited the potentials of multi-modal feature fusion, e.g., simply concatenating RGB and depth features or averaging RGB and depth score maps. To learn the optimal fusion of multi-modal features, this paper presents a novel network that extends the core idea of residual learning to RGB-D semantic segmentation. Our network effectively captures multi-level RGB-D CNN features by including multi-modal feature fusion blocks and multi-level feature refinement blocks. Feature fusion blocks learn residual RGB and depth features and their combinations to fully exploit the complementary characteristics of RGB and depth data. Feature refinement blocks learn the combination of fused features from multiple levels to enable high-resolution prediction. Our network can efficiently train discriminative multi-level features from each modality end-to-end by taking full advantage of skip-connections. Our comprehensive experiments demonstrate that the proposed architecture achieves the state-of-the-art accuracy on two challenging RGB-D indoor datasets, NYUDv2 and SUN RGB-D.
The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes
Authors: Gerhard Neuhold, Tobias Ollmann, Samuel Rota Bulo, Peter Kontschieder
Date: 2017-10-01 00:00:00
Proceeding: ICCV 2017 10
Tasks: Diversity, Image Segmentation, Instance Segmentation, road scene understanding, Scene Understanding, Segmentation, Semantic Segmentation

The Mapillary Vistas Dataset is a novel, large-scale street-level image dataset containing 25,000 high-resolution images annotated into 66 object categories with additional, instance-specific labels for 37 classes. Annotation is performed in a dense and fine-grained style by using polygons for delineating individual objects. Our dataset is 5x larger than the total amount of fine annotations for Cityscapes and contains images from all around the world, captured at various conditions regarding weather, season and daytime. Images come from different imaging devices (mobile phones, tablets, action cameras, professional capturing rigs) and differently experienced photographers. In such a way, our dataset has been designed and compiled to cover diversity, richness of detail and geographic extent. As default benchmark tasks, we define semantic image segmentation and instance-specific image segmentation, aiming to significantly further the development of state-of-the-art methods for visual road-scene understanding.
Self-Organized Text Detection With Minimal Post-Processing via Border Learning
Authors: Yue Wu, Prem Natarajan
Date: 2017-10-01 00:00:00
Proceeding: ICCV 2017 10
Tasks: Text Detection

In this paper we propose a new solution to the text detection problem via border learning. Specifically, we make four major contributions: 1) We analyze the insufficiencies of the classic non-text and text settings for text detection. 2) We introduce the border class to the text detection problem for the first time, and validate that the decoding process is largely simplified with the help of text border. 3) We collect and release a new text detection ppt dataset containing 10,692 images with non-text, border, and text annotations. 4) We develop a lightweight (only 0.28M parameters), fully convolutional network (FCN) to effectively learn borders in text images. The results of our extensive experiments show that the proposed solution achieves comparable performance, and often outperforms state-of-the-art approaches on standard benchmarks--even though our solution only requires minimal post-processing to parse a bounding box from a detected text map, while others often require heavy post-processing.
Sparse Exact PGA on Riemannian Manifolds
Authors: Monami Banerjee, Rudrasis Chakraborty, Baba C. Vemuri
Date: 2017-10-01 00:00:00
Proceeding: ICCV 2017 10
Tasks: Computational Efficiency, Dimensionality Reduction

Principal Component Analysis (PCA) is a widely popular dimensionality reduction technique for vector-valued inputs. In the past decade, a nonlinear generalization of PCA, called the Principal Geodesic Analysis (PGA) was developed to tackle data that lie on a smooth manifold. PGA suffers from the same problem as PCA in that, in both the methods, each Principal Component (PC) is a linear combination of the original variables. This makes it very difficult to interpret the PCs especially in high dimensions. This lead to the introduction of sparse PCA (SPCA) in the vector space input case. In this paper, we present a novel generalization of SPCA, called sparse exact PGA (SEPGA) that can cope with manifold-valued input data and respect the intrinsic geometry of the underlying manifold. Sparsity has the advantage of not only easy interpretability but also computational efficiency. We achieve this by formulating the PGA problem as a minimization of the projection error in conjunction with sparsity constraints enforced on the principal vectors post isomorphic mapping to Rm, where m is the dimension of the manifold on which the data reside. Further, for constant curvature smooth manifolds, we use analytic formulae for the projection error leading to an efficient solution to the SEPGA problem. We present extensive experimental results demonstrating the performance of SEPGA to achieve very good sparse principal components without sacrificing the accuracy of reconstruction. This makes the representation of manifold-valued data using SEPGA accurate and efficient.
Tensor RPCA by Bayesian CP Factorization With Complex Noise
Authors: Qiong Luo, Zhi Han, Xi'ai Chen, Yao Wang, Deyu Meng, Dong Liang, Yandong Tang
Date: 2017-10-01 00:00:00
Proceeding: ICCV 2017 10
Tasks: valid

The RPCA model has achieved good performances in various applications. However, two defects limit its effectiveness. Firstly, it is designed for dealing with data in matrix form, which fails to exploit the structure information of higher order tensor data in some pratical situations. Secondly, it adopts L1-norm to tackle noise part which makes it only valid for sparse noise. In this paper, we propose a tensor RPCA model based on CP decomposition and model data noise by Mixture of Gaussians (MoG). The use of tensor structure to raw data allows us to make full use of the inherent structure priors, and MoG is a general approximator to any blends of consecutive distributions, which makes our approach capable of regaining the low dimensional linear subspace from a wide range of noises or their mixture. The model is solved by a new proposed algorithm inferred under a variational Bayesian framework. The superiority of our approach over the existing state-of-the-art approaches is demonstrated by extensive experiments on both of synthetic and real data.
Multimodal Gaussian Process Latent Variable Models With Harmonization
Authors: Guoli Song, Shuhui Wang, Qingming Huang, Qi Tian
Date: 2017-10-01 00:00:00
Proceeding: ICCV 2017 10
Tasks: Cross-Modal Retrieval, Retrieval

In this work, we address multimodal learning problem with Gaussian process latent variable models (GPLVMs) and their application to cross-modal retrieval. Existing GPLVM based studies generally impose individual priors over the model parameters and ignore the intrinsic relations among these parameters. Considering the strong complementarity between modalities, we propose a novel joint prior over the parameters for multimodal GPLVMs to propagate multimodal information in both kernel hyperparameter spaces and latent space. The joint prior is formulated as a harmonization constraint on the model parameters, which enforces the agreement among the modality-specific GP kernels and the similarity in the latent space. We incorporate the harmonization mechanism into the learning process of multimodal GPLVMs. The proposed methods are evaluated on three widely used multimodal datasets for cross-modal retrieval. Experimental results show that the harmonization mechanism is beneficial to the GPLVM algorithms for learning non-linear correlation among heterogeneous modalities.
Dense and Low-Rank Gaussian CRFs Using Deep Embeddings
Authors: Siddhartha Chandra, Nicolas Usunier, Iasonas Kokkinos
Date: 2017-10-01 00:00:00
Proceeding: ICCV 2017 10
Tasks: GPU, Human Part Segmentation, Saliency Prediction, Segmentation, Semantic Segmentation, Structured Prediction

In this work we introduce a structured prediction model that endows the Deep Gaussian Conditional Random Field (G-CRF) with a densely connected graph structure. We keep memory and computational complexity under control by expressing the pairwise interactions as inner products of low-dimensional, learnable embeddings. The G-CRF system matrix is therefore low-rank, allowing us to solve the resulting system in a few milliseconds on the GPU by using conjugate gradients. As in G-CRF, inference is exact, the unary and pairwise terms are jointly trained end-to-end by using analytic expressions for the gradients, while we also develop even faster, Potts-type variants of our embeddings. We show that the learned embeddings capture pixel-to-pixel affinities in a task-specific manner, while our approach achieves state of the art results on three challenging benchmarks, namely semantic segmentation, human part segmentation, and saliency estimation. Our implementation is fully GPU based, built on top of the Caffe library, and is available at https://github.com/siddharthachandra/gcrf-v2.0
A Multimodal Deep Regression Bayesian Network for Affective Video Content Analyses
Authors: Quan Gan, Shangfei Wang, Longfei Hao, Qiang Ji
Date: 2017-10-01 00:00:00
Proceeding: ICCV 2017 10
Tasks: regression

The inherent dependencies between visual elements and aural elements are crucial for affective video content analyses, yet have not been successfully exploited. Therefore, we propose a multimodal deep regression Bayesian network (MMDRBN) to capture the dependencies between visual elements and aural elements for affective video content analyses. The regression Bayesian network (RBN) is a directed graphical model consisting of one latent layer and one visible layer. Due to the explaining away effect in Bayesian networks (BN), RBN is able to capture both the dependencies among the latent variables given the observation and the dependencies among visible variables. We propose a fast learning algorithm to learn the RBN. For the MMDRBN, first, we learn several RBNs layer-wisely from visual modality and audio modality respectively. Then we stack these RBNs and obtain two deep networks. After that, a joint representation is extracted from the top layers of the two deep networks, and thus captures the high order dependencies between visual modality and audio modality. In order to predict the valence or arousal score of video contents, we initialize a feed-forward inference network from the MMDRBN whose inference is intractable by minimizing the KullbackLeibler (KL)divergence between the two networks. The back propagation algorithm is adopted for finetuning the inference network. Experimental results on the LIRIS-ACCEDE database demonstrate that the proposed MMDRBN successfully captures the dependencies between visual and audio elements, and thus achieves better performance compared with state of the art work.
Moving Object Detection in Time-Lapse or Motion Trigger Image Sequences Using Low-Rank and Invariant Sparse Decomposition
Authors: Moein Shakeri, Hong Zhang
Date: 2017-10-01 00:00:00
Proceeding: ICCV 2017 10
Tasks: Moving Object Detection, object-detection, Object Detection, valid

Low-rank and sparse representation based methods have attracted wide attention in background subtraction and moving object detection, where moving objects in the scene are modeled as pixel-wise sparse outliers. Since in real scenarios moving objects are also structurally sparse, recently researchers have attempted to extract moving objects using structured sparse outliers. Although existing methods with structured sparsity-inducing norms produce promising results, they are still vulnerable to various illumination changes that frequently occur in real environments, specifically for time-lapse image sequences where assumptions about sparsity between images such as group sparsity are not valid. In this paper, we first introduce a prior map obtained by illumination invariant representation of images. Next, we propose a low-rank and invariant sparse decomposition using the prior map to detect moving objects under significant illumination changes. Experiments on challenging benchmark datasets demonstrate the superior performance of our proposed method under complex illumination changes.
BIER - Boosting Independent Embeddings Robustly
Authors: Michael Opitz, Georg Waltner, Horst Possegger, Horst Bischof
Date: 2017-10-01 00:00:00
Proceeding: ICCV 2017 10
Tasks: Image Retrieval, Metric Learning, Retrieval

Learning similarity functions between image pairs with deep neural networks yields highly correlated activations of large embeddings. In this work, we show how to improve the robustness of embeddings by exploiting independence in ensembles. We divide the last embedding layer of a deep network into an embedding ensemble and formulate training this ensemble as an online gradient boosting problem. Each learner receives a reweighted training sample from the previous learners. This leverages large embedding sizes more effectively by significantly reducing correlation of the embedding and consequently increases retrieval accuracy of the embedding. Our method does not introduce any additional parameters and works with any differentiable loss function. We evaluate our metric learning method on image retrieval tasks and show that it improves over state-of-the-art methods on the CUB-200-2011, Cars-196, Stanford Online Products, In-Shop Clothes Retrieval and VehicleID datasets by a significant margin.
3D Graph Neural Networks for RGBD Semantic Segmentation
Authors: Xiaojuan Qi, Renjie Liao, Jiaya Jia, Sanja Fidler, Raquel Urtasun
Date: 2017-10-01 00:00:00
Proceeding: ICCV 2017 10
Tasks: Graph Neural Network, RGBD Semantic Segmentation, Semantic Segmentation

RGBD semantic segmentation requires joint reasoning about 2D appearance and 3D geometric information. In this paper we propose a 3D graph neural network (3DGNN) that builds a k-nearest neighbor graph on top of 3D point cloud. Each node in the graph corresponds to a set of points and is associated with a hidden representation vector initialized with an appearance feature extracted by a unary CNN from 2D images. Relying on recurrent functions, every node dynamically updates its hidden representation based on the current status and incoming messages from its neighbors. This propagation model is unrolled for a certain number of time steps and the final per-node representation is used for predicting the semantic class of each pixel. We use back-propagation through time to train the model. Extensive experiments on NYUD2 and SUN-RGBD datasets demonstrate the effectiveness of our approach.
Learning Multi-Attention Convolutional Neural Network for Fine-Grained Image Recognition
Authors: Heliang Zheng, Jianlong Fu, Tao Mei, Jiebo Luo
Date: 2017-10-01 00:00:00
Proceeding: ICCV 2017 10
Tasks: Clustering, Fine-Grained Image Classification, Fine-Grained Image Recognition, General Classification, Multi-Task Learning

Recognizing fine-grained categories (e.g., bird species) highly relies on discriminative part localization and part-based fine-grained feature learning. Existing approaches predominantly solve these challenges independently, while neglecting the fact that part localization (e.g., head of a bird) and fine-grained feature learning (e.g., head shape) are mutually correlated. In this paper, we propose a novel part learning approach by a multi-attention convolutional neural network (MA-CNN), where part generation and feature learning can reinforce each other. MA-CNN consists of convolution, channel grouping and part classification sub-networks. The channel grouping network takes as input feature channels from convolutional layers, and generates multiple parts by clustering, weighting and pooling from spatially-correlated channels. The part classification network further classifies an image by each individual part, through which more discriminative fine-grained features can be learned. Two losses are proposed to guide the multi-task learning of channel grouping and part classification, which encourages MA-CNN to generate more discriminative parts from feature channels and learn better fine-grained features from parts in a mutual reinforced way. MA-CNN does not need bounding box/part annotation and can be trained end-to-end. We incorporate the learned parts from MA-CNN with part-CNN for recognition, and show the best performances on three challenging published fine-grained datasets, e.g., CUB-Birds, FGVC-Aircraft and Stanford-Cars.
Quantitative Evaluation of Confidence Measures in a Machine Learning World
Authors: Matteo Poggi, Fabio Tosi, Stefano Mattoccia
Date: 2017-10-01 00:00:00
Proceeding: ICCV 2017 10
Tasks: BIG-bench Machine Learning

Confidence measures aim at detecting unreliable depth measurements and play an important role for many purposes and in particular, as recently shown, to improve stereo accuracy. This topic has been thoroughly investigated by Hu and Mordohai in 2010 (and 2012) considering 17 confidence measures and two local algorithms on the two datasets available at that time. However, since then major breakthroughs happened in this field: the availability of much larger and challenging datasets, novel and more effective stereo algorithms including ones based on deep-learning and confidence measures leveraging on machine learning techniques. Therefore, this paper aims at providing an exhaustive and updated review and quantitative evaluation of 52 (actually, 76 considering variants) state-of-the-art confidence measures - focusing on recent ones mostly based on random-forests and deep-learning - with three algorithms on the challenging datasets available today. Moreover we deal with problems inherently induced by learning-based confidence measures. How are these methods able to generalize to new data? How a specific training improves their effectiveness? How more effective confidence measures can actually improve the overall stereo accuracy?
End-To-End Face Detection and Cast Grouping in Movies Using Erdos-Renyi Clustering
Authors: SouYoung Jin, Hang Su, Chris Stauffer, Erik Learned-Miller
Date: 2017-10-01 00:00:00
Proceeding: ICCV 2017 10
Tasks: Clustering, Face Detection

We present an end-to-end system for detecting and clustering faces by identity in full-length movies. Unlike works that start with a predefined set of detected faces, we consider the end-to-end problem of detection and clustering together. We make three separate contributions. First, we combine a state-of-the-art face detector with a generic tracker to extract high quality face tracklets. We then introduce a novel clustering method, motivated by the classic graph theory results of Erdos and Renyi. It is based on the observations that large clusters can be fully connected by joining just a small fraction of their point pairs, while just a single connection between two different people can lead to poor clustering results. This suggests clustering using a verification system with very few false positives but perhaps moderate recall. We introduce a novel verification method, rank-1 counts verification, that has this property, and use it in a link-based clustering scheme. Finally, we define a novel end-to-end detection and clustering evaluation metric allowing us to assess the accuracy of the entire end-to-end system. We present state-of-the-art results on multiple video data sets and also on standard face databases.
Editable Parametric Dense Foliage From 3D Capture
Authors: Gaurav Chaurasia, Paul Beardsley
Date: 2017-10-01 00:00:00
Proceeding: ICCV 2017 10

We present an algorithm to compute parametric models of dense foliage. The guiding principles of our work are automatic reconstruction and compact artist friendly representation. We use Bezier patches to model leaf surface, which we compute from images and point clouds of dense foliage. We present an algorithm to segment individual leaves from colour and depth data. We then reconstruct the Bezier representation from segmented leaf points clouds using non-linear optimisation. Unlike previous work, we do not require laboratory scanned exemplars or user intervention. We also demonstrate intuitive manipulators to edit the reconstructed parametric models. We believe our work is a step towards making captured data more accessible to artists for foliage modelling.
Refractive Structure-From-Motion Through a Flat Refractive Interface
Authors: Francois Chadebecq, Francisco Vasconcelos, George Dwyer, Rene Lacher, Sebastien Ourselin, Tom Vercauteren, Danail Stoyanov
Date: 2017-10-01 00:00:00
Proceeding: ICCV 2017 10
Tasks: Camera Pose Estimation, Pose Estimation

Recovering 3D scene geometry from underwater images involves the Refractive Structure-from-Motion (RSfM) problem, where the image distortions caused by light refraction at the interface between different propagation media invalidates the single view point assumption. Direct use of the pinhole camera model in RSfM leads to inaccurate camera pose estimation and consequently drift. RSfM methods have been thoroughly studied for the case of a thick glass interface that assumes two refractive interfaces between the camera and the viewed scene. On the other hand, when the camera lens is in direct contact with the water, there is only one refractive interface. By explicitly considering a refractive interface, we develop a succinct derivation of the refractive fundamental matrix in the form of the generalised epipolar constraint for an axial camera. We use the refractive fundamental matrix to refine initial pose estimates obtained by assuming the pinhole model. This strategy allows us to robustly estimate underwater camera poses, where other methods suffer from poor noise-sensitivity. We also formulate a new four view constraint enforcing camera pose consistency along a video which leads us to a novel RSfM framework. For validation we use synthetic data to show the numerical properties of our method and we provide results on real data to demonstrate performance within laboratory settings and for applications in endoscopy.
Deltille Grids for Geometric Camera Calibration
Authors: Hyowon Ha, Michal Perdoch, Hatem Alismail, In So Kweon, Yaser Sheikh
Date: 2017-10-01 00:00:00
Proceeding: ICCV 2017 10
Tasks: 3D Reconstruction, Camera Calibration

The recent proliferation of high resolution cameras presents an opportunity to achieve unprecedented levels of precision in visual 3D reconstruction. Yet the camera calibration pipeline, developed decades ago using checkerboards, has remained the de facto standard. In this paper, we ask the question: are checkerboards the optimal pattern for high precision calibration? We empirically demonstrate that deltille grids (regular triangular tiling) produce the highest precision calibration of the possible tilings of Euclidean plane. We posit that they should be the new standard for high-precision calibration and present a complete ecosystem for calibration using deltille grids including: (1) a highly precise corner detection algorithm based on polynomial surface fitting; (2) an indexing scheme based on polarities extracted from the fitted surfaces; and (3) a 2D coding system for deltille grids, which we refer to as DelTags, in lieu of conventional matrix barcodes. We demonstrate state-of-the-art performance and apply the full calibration ecosystem through the use of 3D calibration objects for multiview camera calibration.