pwc-archive/papers-with-abstracts huggingface dataset:
Optimal Transformation Estimation With Semantic Cues
Authors: Danda Pani Paudel, Adlane Habed, Luc van Gool
Date: 2017-10-01 00:00:00
Proceeding: ICCV 2017 10

This paper addresses the problem of estimating the geometric transformation relating two distinct visual modalities (e.g. an image and a map, or a projective structure and a Euclidean 3D model) while relying only on semantic cues, such as semantically segmented regions or object bounding boxes. The proposed approach differs from the traditional feature-to-feature correspondence reasoning: starting from semantic regions on one side, we seek their possible corresponding regions on the other, thus constraining the sought geometric transformation. This entails a simultaneous search for the transformation and for the region-to-region correspondences.This paper is the first to derive the conditions that must be satisfied for a convex region, defined by control points, to be transformed inside an ellipsoid. These conditions are formulated as Linear Matrix Inequalities and used within a Branch-and-Prune search to obtain the globally optimal transformation. We tested our approach, under mild initial bound conditions, on two challenging registration problems for aligning: (i) a semantically segmented image and a map via a 2D homography; (ii) a projective 3D structure and its Euclidean counterpart.
Dynamics Enhanced Multi-Camera Motion Segmentation From Unsynchronized Videos
Authors: Xikang Zhang, Bengisu Ozbay, Mario Sznaier, Octavia Camps
Date: 2017-10-01 00:00:00
Proceeding: ICCV 2017 10
Tasks: Motion Segmentation, Segmentation

This paper considers the multi-camera motion segmentation problem using unsynchronized videos. Specifically, given two video clips containing several moving objects, captured by unregistered, unsynchronized cameras with different viewpoints, our goal is to assign features to moving objects in the scene. This problem challenges existing methods, due to the lack of registration information and correspondences across cameras. To solve it, we propose a new method that exploits both shape and dynamical information and does not require spatio-temporal registration or shared features. As shown in the paper, the combination of shape and dynamical information results in improved performance even in the single camera case, and allows for solving the multi-camera segmentation problem with a computational cost similar to that of existing single-view techniques. These results are illustrated using both the existing Hopkins 155 data set and a new multi-camera data set, the RSL-12.
Taking the Scenic Route to 3D: Optimising Reconstruction From Moving Cameras
Authors: Oscar Mendez, Simon Hadfield, Nicolas Pugeault, Richard Bowden
Date: 2017-10-01 00:00:00
Proceeding: ICCV 2017 10

Reconstruction of 3D environments is a problem that has been widely addressed in the literature. While many approaches exist to perform reconstruction, few of them take an active role in deciding where the next observations should come from. Furthermore, the problem of travelling from the camera's current position to the next, known as pathplanning, usually focuses on minimising path length. This approach is ill-suited for reconstruction applications, where learning about the environment is more valuable than speed of traversal. We present a novel Scenic Route Planner that selects paths which maximise information gain, both in terms of total map coverage and reconstruction accuracy. We also introduce a new type of collaborative behaviour into the planning stage called opportunistic collaboration, which allows sensors to switch between acting as independent Structure from Motion (SfM) agents or as a variable baseline stereo pair. We show that Scenic Planning enables similar performance to state-of-the-art batch approaches using less than 0.00027% of the possible stereo pairs (3% of the views). Comparison against length-based pathplanning approaches show that our approach produces more complete and more accurate maps with fewer frames. Finally, we demonstrate the Scenic Pathplanner's ability to generalise to live scenarios by mounting cameras on autonomous ground-based sensor platforms and exploring an environment.
FLaME: Fast Lightweight Mesh Estimation Using Variational Smoothing on Delaunay Graphs
Authors: W. Nicholas Greene, Nicholas Roy
Date: 2017-10-01 00:00:00
Proceeding: ICCV 2017 10
Tasks: CPU, Depth Estimation, Monocular Depth Estimation

We propose a lightweight method for dense online monocular depth estimation capable of reconstructing 3D meshes on computationally constrained platforms. Our main contribution is to pose the reconstruction problem as a non-local variational optimization over a time-varying Delaunay graph of the scene geometry, which allows for an efficient, keyframeless approach to depth estimation. The graph can be tuned to favor reconstruction quality or speed and is continuously smoothed and augmented as the camera explores the scene. Unlike keyframe-based approaches, the optimized surface is always available at the current pose, which is necessary for low-latency obstacle avoidance. FLaME (Fast Lightweight Mesh Estimation) can generate mesh reconstructions at upwards of 230 Hz using less than one Intel i7 CPU core, which enables operation on size, weight, and power-constrained platforms. We present results from both benchmark datasets and experiments running FLaME in-the-loop onboard a small flying quadrotor.
From RGB to Spectrum for Natural Scenes via Manifold-Based Mapping
Authors: Yan Jia, Yinqiang Zheng, Lin Gu, Art Subpa-Asa, Antony Lam, Yoichi Sato, Imari Sato
Date: 2017-10-01 00:00:00
Proceeding: ICCV 2017 10
Tasks: Dimensionality Reduction

Spectral analysis of natural scenes can provide much more detailed information about the scene than an ordinary RGB camera. The richer information provided by hyperspectral images has been beneficial to numerous applications, such as understanding natural environmental changes and classifying plants and soils in agriculture based on their spectral properties. In this paper, we present an efficient manifold learning based method for accurately reconstructing a hyperspectral image from a single RGB image captured by a commercial camera with known spectral response. By applying a nonlinear dimensionality reduction technique to a large set of natural spectra, we show that the spectra of natural scenes lie on an intrinsically low dimensional manifold. This allows us to map an RGB vector to its corresponding hyperspectral vector accurately via our proposed novel manifold-based reconstruction pipeline. Experiments using both synthesized RGB images using hyperspectral datasets and real world data demonstrate our method outperforms the state-of-the-art.
Automatic Content-Aware Projection for 360deg Videos
Authors: Yeong Won Kim, Chang-Ryeol Lee, Dae-Yong Cho, Yong Hoon Kwon, Hyeok-Jae Choi, Kuk-Jin Yoon
Date: 2017-10-01 00:00:00
Proceeding: ICCV 2017 10

To watch 360 videos on normal 2D displays, we need to project the selected part of the 360 image onto the 2D display plane. In this paper, we propose a fully-automated framework for generating content-aware 2D normal-view perspective videos from 360 videos. Especially, we focus on the projection step preserving important image contents and reducing image distortion. Basically, our projection method is based on Pannini projection model. At first, the salient contents such as linear structures and salient regions in the image are preserved by optimizing the single Panini projection model. Then, the multiple Panini projection models at salient regions are interpolated to suppress image distortion globally. Finally, the temporal consistency for image projection is enforced for producing temporally stable normal-view videos. Our proposed projection method does not require any user-interaction and is much faster than previous content-preserving methods. It can be applied to not only images but also videos taking the temporal consistency of projection into account. Experiments on various 360 videos show the superiority of the proposed projection method quantitatively and qualitatively.
Blur-Invariant Deep Learning for Blind-Deblurring
Authors: T. M. Nimisha, Akash Kumar Singh, A. N. Rajagopalan
Date: 2017-10-01 00:00:00
Proceeding: ICCV 2017 10
Tasks: Deblurring, Decoder, Deep Learning

In this paper, we investigate deep neural networks for blind motion deblurring. Instead of regressing for the motion blur kernel and performing non-blind deblurring out- side of the network (as most methods do), we propose a compact and elegant end-to-end deblurring network. Inspired by the data-driven sparse-coding approaches that are capable of capturing linear dependencies in data, we generalize this notion by embedding non-linearities into the learning process. We propose a new architecture for blind motion deblurring that consists of an autoencoder that learns the data prior, and an adversarial network that attempts to generate and discriminate between clean and blurred features. Once the network is trained, the generator learns a blur-invariant data representation which when fed through the decoder results in the final deblurred output.
AOD-Net: All-In-One Dehazing Network
Authors: Boyi Li, Xiulian Peng, Zhangyang Wang, Jizheng Xu, Dan Feng
Date: 2017-10-01 00:00:00
Proceeding: ICCV 2017 10
Tasks: All, Image Dehazing, object-detection, Object Detection, SSIM

This paper proposes an image dehazing model built with a convolutional neural network (CNN), called All-in-One Dehazing Network (AOD-Net). It is designed based on a re-formulated atmospheric scattering model. Instead of estimating the transmission matrix and the atmospheric light separately as most previous models did, AOD-Net directly generates the clean image through a light-weight CNN. Such a novel end-to-end design makes it easy to embed AOD-Net into other deep models, e.g., Faster R-CNN, for improving high-level tasks on hazy images. Experimental results on both synthesized and natural hazy image datasets demonstrate our superior performance than the state-of-the-art in terms of PSNR, SSIM and the subjective visual quality. Furthermore, when concatenating AOD-Net with Faster R-CNN, we witness a large improvement of the object detection performance on hazy images.
Simultaneous Detection and Removal of High Altitude Clouds From an Image
Authors: Tushar Sandhan, Jin Young Choi
Date: 2017-10-01 00:00:00
Proceeding: ICCV 2017 10
Tasks: Vocal Bursts Intensity Prediction, Weather Forecasting

Interestingly, shape of the high-altitude clouds serves as a beacon for weather forecasting, so its detection is of vital importance. Besides these clouds often cause hindrance in an endeavor of satellites to inspect our world. Even thin clouds produce the undesired superposition of visual information, whose decomposition into the clear background and cloudy layer using a single satellite image is a highly ill-posed problem. In this work, we derive sophisticated image priors by thoroughly analyzing the properties of high-altitude clouds and geological images; and formulate a non-convex optimization scheme, which simultaneously detects and removes the clouds within a few seconds. Experimental results on real world RGB images demonstrate that the proposed method outperforms the other competitive methods by retaining the comprehensive background details and producing the precise shape of the cloudy layer.
Understanding Low- and High-Level Contributions to Fixation Prediction
Authors: Matthias Kummerer, Thomas S. A. Wallis, Leon A. Gatys, Matthias Bethge
Date: 2017-10-01 00:00:00
Proceeding: ICCV 2017 10
Tasks: Object Recognition, Prediction, Saliency Prediction, Vocal Bursts Intensity Prediction

Understanding where people look in images is an important problem in computer vision. Despite significant research, it remains unclear to what extent human fixations can be predicted by low-level (contrast) compared to high-level (presence of objects) image features. Here we address this problem by introducing two novel models that use different feature spaces but the same readout architecture. The first model predicts human fixations based on deep neural network features trained on object recognition. This model sets a new state-of-the art in fixation prediction by achieving top performance in area under the curve metrics on the MIT300 hold-out benchmark (AUC = 88%, sAUC = 77%, NSS = 2.34). The second model uses purely low-level (isotropic contrast) features. This model achieves better performance than all models not using features pre-trained on object recognition, making it a strong baseline to assess the utility of high-level features. We then evaluate and visualize which fixations are better explained by low-level compared to high-level image features. Surprisingly we find that a substantial proportion of fixations are better explained by the simple low-level model than the state-of-the-art model. Comparing different features within the same powerful readout architecture allows us to better understand the relevance of low- versus high-level features in predicting fixation locations, while simultaneously achieving state-of-the-art saliency prediction.
Image Super-Resolution Using Dense Skip Connections
Authors: Tong Tong, Gen Li, Xiejie Liu, Qinquan Gao
Date: 2017-10-01 00:00:00
Proceeding: ICCV 2017 10
Tasks: Computational Efficiency, Image Super-Resolution, Super-Resolution

Recent studies have shown that the performance of single-image super-resolution methods can be significantly boosted by using deep convolutional neural networks. In this study, we present a novel single-image super-resolution method by introducing dense skip connections in a very deep network. In the proposed network, the feature maps of each layer are propagated into all subsequent layers, providing an effective way to combine the low-level features and high-level features to boost the reconstruction performance. In addition, the dense skip connections in the network enable short paths to be built directly from the output to each layer, alleviating the vanishing-gradient problem of very deep networks. Moreover, deconvolution layers are integrated into the network to learn the upsampling filters and to speedup the reconstruction process. Further, the proposed method substantially reduces the number of parameters, enhancing the computational efficiency. We evaluate the proposed method using images from four benchmark datasets and set a new state of the art.
Blob Reconstruction Using Unilateral Second Order Gaussian Kernels With Application to High-ISO Long-Exposure Image Denoising
Authors: Gang Wang, Carlos Lopez-Molina, Bernard De Baets
Date: 2017-10-01 00:00:00
Proceeding: ICCV 2017 10
Tasks: Denoising, Image Denoising, Position

Blob detection and image denoising are fundamental, and sometimes related, tasks in computer vision. In this paper, we propose a blob reconstruction method using scale-invariant normalized unilateral second order Gaussian kernels. Unlike other blob detection methods, our method suppresses non-blob structures while also identifying blob parameters, i.e., position, prominence and scale, thereby facilitating blob reconstruction. We present an algorithm for high-ISO long-exposure noise removal that results from the combination of our blob reconstruction method and state-of-the-art denoising methods, i.e., the non-local means algorithm (NLM) and the color version of block-matching and 3-D filtering (CBM3D). Experiments on standard images corrupted by real high-ISO long-exposure noise and real-world noisy images demonstrate that our schemes incorporating the blob reduction procedure outperform both the original NLM and CBM3D.
Mutual Enhancement for Detection of Multiple Logos in Sports Videos
Authors: Yuan Liao, Xiaoqing Lu, Chengcui Zhang, Yongtao Wang, Zhi Tang
Date: 2017-10-01 00:00:00
Proceeding: ICCV 2017 10
Tasks: object-detection, Object Detection, Re-Ranking

Detecting logo frequency and duration in sports videos provides sponsors an effective way to evaluate their advertising efforts. However, general-purposed object detection methods cannot address all the challenges in sports videos. In this paper, we propose a mutual-enhanced approach that can improve the detection of a logo through the information obtained from other simultaneously occurred logos. In a Fast-RCNN-based framework, we first introduce a homogeneity-enhanced re-ranking method by analyzing the characteristics of homogeneous logos in each frame, including type repetition, color consistency, and mutual exclusion. Different from conventional enhance mechanism that improves the weak proposals with the dominant proposals, our mutual method can also enhance the relatively significant proposals with weak proposals. Mutual enhancement is also included in our frame propagation mechanism that improves logo detection by utilizing the continuity of logos across frames. We use a tennis video dataset and an associated logo collection for detection evaluation. Experiments show that the proposed method outperforms existing methods with a higher accuracy.
Referring Expression Generation and Comprehension via Attributes
Authors: Jingyu Liu, Liang Wang, Ming-Hsuan Yang
Date: 2017-10-01 00:00:00
Proceeding: ICCV 2017 10
Tasks: Attribute, Referring Expression, Referring expression generation

Referring expression is a kind of language expression that used for referring to particular objects.To make the expression without ambiguation, people often use attributes to describe the particular object. In this paper, we explore the role of attributes by incorporating them into both referring expression generation and comprehension. We first train an attribute learning model from visual objects and their paired descriptions. Then in the generation task, we take the learned attributes as the input into the generation model, thus the expressions are generated driven by both attributes and the previous words. For comprehension, we embed the learned attributes with visual features and semantics into the common space model, then the target object is retrieved based on its ranking distance in the common space. Experimental results on the three standard datasets, RefCOCO, RefCOCO+, and RefCOCOg show significant improvements over the baseline model, demonstrating that our methods are effective for both tasks.
AnnArbor: Approximate Nearest Neighbors Using Arborescence Coding
Authors: Artem Babenko, Victor Lempitsky
Date: 2017-10-01 00:00:00
Proceeding: ICCV 2017 10
Tasks: Quantization

To compress large datasets of high-dimensional descriptors, modern quantization schemes learn multiple codebooks and then represent individual descriptors as combinations of codewords. Once the codebooks are learned, these schemes encode descriptors independently. In contrast to that, we present a new coding scheme that arranges dataset descriptors into a set of arborescence graphs, and then encodes non-root descriptors by quantizing their displacements with respect to their parent nodes. By optimizing the structure of arborescences, our coding scheme can decrease the quantization error considerably, while incurring only minimal overhead on the memory footprint and the speed of nearest neighbor search in the compressed dataset compared to the independent quantization. The advantage of the proposed scheme is demonstrated in a series of experiments with datasets of SIFT and deep descriptors.
Locally-Transferred Fisher Vectors for Texture Classification
Authors: Yang Song, Fan Zhang, Qing Li, Heng Huang, Lauren J. O'Donnell, Weidong Cai
Date: 2017-10-01 00:00:00
Proceeding: ICCV 2017 10
Tasks: Classification, General Classification, image-classification, Image Classification, Texture Classification

Texture classification has been extensively studied in computer vision. Recent research shows that the combination of Fisher vector (FV) encoding and convolutional neural network (CNN) provides significant improvement in texture classification over the previous feature representation methods. However, by truncating the CNN model at the last convolutional layer, the CNN-based FV descriptors would not incorporate the full capability of neural networks in feature learning. In this study, we propose that we can further transform the CNN-based FV descriptors in a neural network model to obtain more discriminative feature representations. In particular, we design a locally-transferred Fisher vector (LFV) method, which involves a multi-layer neural network model containing locally connected layers to transform the input FV descriptors with filters of locally shared weights. The network is optimized based on the hinge loss of classification, and transferred FV descriptors are then used for image classification. Our results on three challenging texture image datasets show improved performance over the state of the art.
Object-Level Proposals
Authors: Jianxiang Ma, Anlong Ming, Zilong Huang, Xinggang Wang, Yu Zhou
Date: 2017-10-01 00:00:00
Proceeding: ICCV 2017 10
Tasks: Object, object-detection, Object Detection

Edge and surface are two fundamental visual elements of an object. The majority of existing object proposal approaches utilize edge or edge-like cues to rank candidates, while we consider that the surface cue containing the 3D characteristic of objects should be captured effectively for proposals, which has been rarely discussed before. In this paper, an object-level proposal model is presented, which constructs an occlusion-based objectness taking the surface cue into account. Specifically, the better detection of occlusion edges is focused on to enrich the surface cue into proposals, namely, the occlusion-dominated fusion and normalization criterion are designed to obtain the approximately overall contour information, to enhance the occlusion edge map at utmost and thus boost proposals. Experimental results on the PASCAL VOC 2007 and MS COCO 2014 dataset demonstrate the effectiveness of our approach, which achieves around 6% improvement on the average recall than Edge Boxes at 1000 proposals and also leads to a modest gain on the performance of object detection.
Exploiting Spatial Structure for Localizing Manipulated Image Regions
Authors: Jawadul H. Bappy, Amit K. Roy-Chowdhury, Jason Bunk, Lakshmanan Nataraj, B. S. Manjunath
Date: 2017-10-01 00:00:00
Proceeding: ICCV 2017 10
Tasks: Image Manipulation, Semantic Segmentation

The advent of high-tech journaling tools facilitates an image to be manipulated in a way that can easily evade state-of-the-art image tampering detection approaches. The recent success of the deep learning approaches in different recognition tasks inspires us to develop a high confidence detection framework which can localize manipulated regions in an image. Unlike semantic object segmentation where all meaningful regions (objects) are segmented, the localization of image manipulation focuses only the possible tampered region which makes the problem even more challenging. In order to formulate the framework, we employ a hybrid CNN-LSTM model to capture discriminative features between manipulated and non-manipulated regions. One of the key properties of manipulated regions is that they exhibit discriminative features in boundaries shared with neighboring non-manipulated pixels. Our motivation is to learn the boundary discrepancy, i.e., the spatial structure, between manipulated and non-manipulated regions with the combination of LSTM and convolution layers. We perform end-to-end training of the network to learn the parameters through back-propagation given groundtruth mask information. The overall framework is capable of detecting different types of image manipulations, including copy-move, removal and splicing. Our model shows promising results in localizing manipulated regions, which is demonstrated through rigorous experimentation on three diverse datasets.