mtybilly/Instruct2DS huggingface dataset:
Jointly Attentive Spatial-Temporal Pooling Networks for Video-Based Person Re-Identification
Authors: Yu Cheng, Kang Gu, Yang Yang, Shiyu Chang, Pan Zhou Shuangjie Xu
Published in: IEEE International Conference on Computer Vision (ICCV), 2017 (Main Conference Track), 2017

Abstract: Person Re-Identification (person re-id) is a crucial task as its applications in visual surveillance and human-computer interaction. In this work, we present a novel joint Spatial and Temporal Attention Pooling Network (ASTPN) for video-based person re-identification, which enables the feature extractor to be aware of the current input video sequences, in a way that interdependency from the matching items can directly influence the computation of each other's representation. Specifically, the spatial pooling layer is able to select regions from each frame, while the attention temporal pooling performed can select informative frames over the sequence, both pooling guided by the information from distance matching. Experiments are conduced on the iLIDS-VID, PRID-2011 and MARS datasets and the results demonstrate that this approach outperforms existing state-of-art methods. We also analyze how the joint pooling in both dimensions can boost the person re-id performance more effectively than using either of them separately.
Automatic Content-Aware Projection for 360deg Videos
Authors: Chang-Ryeol Lee, Dae-Yong Cho, Yong Hoon Kwon, Hyeok-Jae Choi, Kuk-Jin Yoon Yeong Won Kim
Published in: IEEE International Conference on Computer Vision (ICCV), 2017 (Main Conference Track), 2017

Abstract: 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: Akash Kumar Singh, A. N. Rajagopalan T. M. Nimisha
Published in: IEEE International Conference on Computer Vision (ICCV), 2017 (Main Conference Track), 2017

Abstract: 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.
Non-Linear Convolution Filters for CNN-Based Learning
Authors: Alexandros Doumanoglou, Nicholas Vretos, Petros Daras Georgios Zoumpourlis
Published in: IEEE International Conference on Computer Vision (ICCV), 2017 (Main Conference Track), 2017

Abstract: During the last years, Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance in image classification. Their architectures have largely drawn inspiration by models of the primate visual system. However, while recent research results of neuroscience prove the existence of non-linear operations in the response of complex visual cells, little effort has been devoted to extend the convolution technique to non-linear forms. Typical convolutional layers are linear systems, hence their expressiveness is limited. To overcome this, various non-linearities have been used as activation functions inside CNNs, while also many pooling strategies have been applied. We address the issue of developing a convolution method in the context of a computational model of the visual cortex, exploring quadratic forms through the Volterra kernels. Such forms, constituting a more rich function space, are used as approximations of the response profile of visual cells. Our proposed second-order convolution is tested on CIFAR-10 and CIFAR-100. We show that a network which combines linear and non-linear filters in its convolutional layers, can outperform networks that use standard linear filters with the same architecture, yielding results competitive with the state-of-the-art on these datasets.
AOD-Net: All-In-One Dehazing Network
Authors: Xiulian Peng, Zhangyang Wang, Jizheng Xu, Dan Feng Boyi Li
Published in: IEEE International Conference on Computer Vision (ICCV), 2017 (Main Conference Track), 2017

Abstract: 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: Jin Young Choi Tushar Sandhan
Published in: IEEE International Conference on Computer Vision (ICCV), 2017 (Main Conference Track), 2017

Abstract: 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: Thomas S. A. Wallis, Leon A. Gatys, Matthias Bethge Matthias Kummerer
Published in: IEEE International Conference on Computer Vision (ICCV), 2017 (Main Conference Track), 2017

Abstract: 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: Gen Li, Xiejie Liu, Qinquan Gao Tong Tong
Published in: IEEE International Conference on Computer Vision (ICCV), 2017 (Main Conference Track), 2017

Abstract: 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.
Convergence Analysis of MAP Based Blur Kernel Estimation
Authors: Seungyong Lee Sunghyun Cho
Published in: IEEE International Conference on Computer Vision (ICCV), 2017 (Main Conference Track), 2017

Abstract: One popular approach for blind deconvolution is to formulate a maximum a posteriori (MAP) problem with sparsity priors on the gradients of the latent image, and then alternatingly estimate the blur kernel and the latent image. While several successful MAP based methods have been proposed, there has been much controversy and confusion about their convergence, because sparsity priors have been shown to prefer blurry images to sharp natural images. In this paper, we revisit this problem and provide an analysis on the convergence of MAP based approaches. We first introduce a slight modification to a conventional joint energy function for blind deconvolution. The reformulated energy function yields the same alternating estimation process, but more clearly reveals how blind deconvolution works. We then show the energy function can actually favor the right solution instead of the no-blur solution under certain conditions, which explains the success of previous MAP based approaches. The reformulated energy function and our conditions for the convergence also provide a way to compare the qualities of different blur kernels, and we demonstrate its applicability to automatic blur kernel size selection, blur kernel estimation using light streaks, and defocus estimation.
Blob Reconstruction Using Unilateral Second Order Gaussian Kernels With Application to High-ISO Long-Exposure Image Denoising
Authors: Carlos Lopez-Molina, Bernard De Baets Gang Wang
Published in: IEEE International Conference on Computer Vision (ICCV), 2017 (Main Conference Track), 2017

Abstract: 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.
Deep Generative Adversarial Compression Artifact Removal
Authors: Lorenzo Seidenari, Marco Bertini, Alberto Del Bimbo Leonardo Galteri
Published in: IEEE International Conference on Computer Vision (ICCV), 2017 (Main Conference Track), 2017

Abstract: Compression artifacts arise in images whenever a lossy compression algorithm is applied. These artifacts eliminate details present in the original image, or add noise and small structures; because of these effects they make images less pleasant for the human eye, and may also lead to decreased performance of computer vision algorithms such as object detectors. To eliminate such artifacts, when decompressing an image, it is required to recover the original image from a disturbed version. To this end, we present a feed-forward fully convolutional residual network model trained using a generative adversarial framework. To provide a baseline, we show that our model can be also trained optimizing the Structural Similarity (SSIM), which is a better loss with respect to the simpler Mean Squared Error (MSE). Our GAN is able to produce images with more photorealistic details than MSE or SSIM based networks. Moreover we show that our approach can be used as a pre-processing step for object detection in case images are degraded by compression to a point that state-of-the art detectors fail. In this task, our GAN method obtains better performance than MSE or SSIM trained networks.
Online Multi-Object Tracking Using CNN-Based Single Object Tracker With Spatial-Temporal Attention Mechanism
Authors: Wanli Ouyang, Hongsheng Li, Xiaogang Wang, Bin Liu, Nenghai Yu Qi Chu
Published in: IEEE International Conference on Computer Vision (ICCV), 2017 (Main Conference Track), 2017

Abstract: In this paper, we propose a CNN-based framework for online MOT. This framework utilizes the merits of single object trackers in adapting appearance models and searching for target in the next frame. Simply applying single object tracker for MOT will encounter the problem in computational efficiency and drifted results caused by occlusion. Our framework achieves computational efficiency by sharing features and using ROI-Pooling to obtain individual features for each target. Some online learned target-specific CNN layers are used for adapting the appearance model for each target. In the framework, we introduce spatial-temporal attention mechanism (STAM) to handle the drift caused by occlusion and interaction among targets. The visibility map of the target is learned and used for inferring the spatial attention map. The spatial attention map is then applied to weight the features. Besides, the occlusion status can be estimated from the visibility map, which controls the online updating process via weighted loss on training samples with different occlusion statuses in different frames. It can be considered as temporal attention mechanism. The proposed algorithm achieves 34.3% and 46.0% in MOTA on challenging MOT15 and MOT16 benchmark dataset respectively.
Mutual Enhancement for Detection of Multiple Logos in Sports Videos
Authors: Xiaoqing Lu, Chengcui Zhang, Yongtao Wang, Zhi Tang Yuan Liao
Published in: IEEE International Conference on Computer Vision (ICCV), 2017 (Main Conference Track), 2017

Abstract: 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: Liang Wang, Ming-Hsuan Yang Jingyu Liu
Published in: IEEE International Conference on Computer Vision (ICCV), 2017 (Main Conference Track), 2017

Abstract: 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.
RoomNet: End-To-End Room Layout Estimation
Authors: Vijay Badrinarayanan, Tomasz Malisiewicz, Andrew Rabinovich Chen-Yu Lee
Published in: IEEE International Conference on Computer Vision (ICCV), 2017 (Main Conference Track), 2017

Abstract: This paper focuses on the task of room layout estimation from a monocular RGB image. Prior works break the problem into two sub-tasks: semantic segmentation of floor, walls, ceiling to produce layout hypotheses, followed by an iterative optimization step to rank these hypotheses. In contrast, we adopt a more direct formulation of this problem as one of estimating an ordered set of room layout keypoints. The room layout and the corresponding segmentation is completely specified given the locations of these ordered keypoints. We predict the locations of the room layout keypoints using RoomNet, an end-to-end trainable encoder-decoder network. On the challenging benchmark datasets Hedau and LSUN, we achieve state-of-the-art performance along with 200x to 600x speedup compared to the most recent work. Additionally, we present optional extensions to the RoomNet architecture such as including recurrent computations and memory units to refine the keypoint locations under the same parametric capacity.
SSH: Single Stage Headless Face Detector
Authors: Pouya Samangouei, Rama Chellappa, Larry S. Davis Mahyar Najibi
Published in: IEEE International Conference on Computer Vision (ICCV), 2017 (Main Conference Track), 2017

Abstract: We introduce the Single Stage Headless (SSH) face detector. Unlike two stage proposal-classification detectors, SSH detects faces in a single stage directly from the early convolutional layers in a classification network. SSH is headless. That is, it is able to achieve state-of-the-art results while removing the "head" of its underlying classification network -- i.e. all fully connected layers in the VGG-16 which contains a large number of parameters. Additionally, instead of relying on an image pyramid to detect faces with various scales, SSH is scale-invariant by design. We simultaneously detect faces with different scales in a single forward pass of the network, but from different layers. These properties make SSH fast and light-weight. Surprisingly, with a headless VGG-16, SSH beats the ResNet-101-based state-of-the-art on the WIDER dataset. Even though, unlike the current state-of-the-art, SSH does not use an image pyramid and is 5X faster. Moreover, if an image pyramid is deployed, our light-weight network achieves state-of-the-art on all subsets of the WIDER dataset, improving the AP by 2.5%. SSH also reaches state-of-the-art results on the FDDB and Pascal-Faces datasets while using a small input size, leading to a speed of 50 frames/second on a GPU.
AnnArbor: Approximate Nearest Neighbors Using Arborescence Coding
Authors: Victor Lempitsky Artem Babenko
Published in: IEEE International Conference on Computer Vision (ICCV), 2017 (Main Conference Track), 2017

Abstract: 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.
Boosting Image Captioning With Attributes
Authors: Yingwei Pan, Yehao Li, Zhaofan Qiu, Tao Mei Ting Yao
Published in: IEEE International Conference on Computer Vision (ICCV), 2017 (Main Conference Track), 2017

Abstract: Automatically describing an image with a natural language has been an emerging challenge in both fields of computer vision and natural language processing. In this paper, we present Long Short-Term Memory with Attributes (LSTM-A) - a novel architecture that integrates attributes into the successful Convolutional Neural Networks (CNNs) plus Recurrent Neural Networks (RNNs) image captioning framework, by training them in an end-to-end manner. Particularly, the learning of attributes is strengthened by integrating inter-attribute correlations into Multiple Instance Learning (MIL). To incorporate attributes into captioning, we construct variants of architectures by feeding image representations and attributes into RNNs in different ways to explore the mutual but also fuzzy relationship between them. Extensive experiments are conducted on COCO image captioning dataset and our framework shows clear improvements when compared to state-of-the-art deep models. More remarkably, we obtain METEOR/CIDEr-D of 25.5%/100.2% on testing data of widely used and publicly available splits in [10] when extracting image representations by GoogleNet and achieve superior performance on COCO captioning Leaderboard.
Learning to Estimate 3D Hand Pose From Single RGB Images
Authors: Thomas Brox Christian Zimmermann
Published in: IEEE International Conference on Computer Vision (ICCV), 2017 (Main Conference Track), 2017

Abstract: Low-cost consumer depth cameras and deep learning have enabled reasonable 3D hand pose estimation from single depth images. In this paper, we present an approach that estimates 3D hand pose from regular RGB images. This task has far more ambiguities due to the missing depth information. To this end, we propose a deep network that learns a network-implicit 3D articulation prior. Together with detected keypoints in the images, this network yields good estimates of the 3D pose. We introduce a large scale 3D hand pose dataset based on synthetic hand models for training the involved networks. Experiments on a variety of test sets, including one on sign language recognition, demonstrate the feasibility of 3D hand pose estimation on single color images.
Locally-Transferred Fisher Vectors for Texture Classification
Authors: Fan Zhang, Qing Li, Heng Huang, Lauren J. O'Donnell, Weidong Cai Yang Song
Published in: IEEE International Conference on Computer Vision (ICCV), 2017 (Main Conference Track), 2017

Abstract: 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: Anlong Ming, Zilong Huang, Xinggang Wang, Yu Zhou Jianxiang Ma
Published in: IEEE International Conference on Computer Vision (ICCV), 2017 (Main Conference Track), 2017

Abstract: 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.
Extreme Clicking for Efficient Object Annotation
Authors: Jasper R. R. Uijlings, Frank Keller, Vittorio Ferrari Dim P. Papadopoulos
Published in: IEEE International Conference on Computer Vision (ICCV), 2017 (Main Conference Track), 2017

Abstract: Manually annotating object bounding boxes is central to building computer vision datasets, and it is very time consuming (annotating ILSVRC [53] took 35s for one high-quality box [62]). It involves clicking on imaginary corners of a tight box around the object. This is difficult as these corners are often outside the actual object and several adjustments are required to obtain a tight box. We propose extreme clicking instead: we ask the annotator to click on four physical points on the object: the top, bottom, left- and right-most points. This task is more natural and these points are easy to find. We crowd-source extreme point annotations for PASCAL VOC 2007 and 2012 and show that (1) annotation time is only 7s per box, 5x faster than the traditional way of drawing boxes [62]; (2) the quality of the boxes is as good as the original ground-truth drawn the traditional way; (3) detectors trained on our annotations are as accurate as those trained on the original ground-truth. Moreover, our extreme clicking strategy not only yields box coordinates, but also four accurate boundary points. We show (4) how to incorporate them into GrabCut to obtain more accurate segmentations than those delivered when initializing it from bounding boxes; (5) semantic segmentations models trained on these segmentations outperform those trained on segmentations derived from bounding boxes.
WordSup: Exploiting Word Annotations for Character Based Text Detection
Authors: Chengquan Zhang, Yuxuan Luo, Yuzhuo Wang, Junyu Han, Errui Ding Han Hu
Published in: IEEE International Conference on Computer Vision (ICCV), 2017 (Main Conference Track), 2017

Abstract: Imagery texts are usually organized as a hierarchy of several visual elements, i.e. characters, words, text lines and text blocks. Among these elements, character is the most basic one for various languages such as Western, Chinese, Japanese, mathematical expression and etc. It is natural and convenient to construct a common text detection engine based on character detectors. However, training character detectors requires a vast of location annotated characters, which are expensive to obtain. Actually, the existing real text datasets are mostly annotated in word or line level. To remedy this dilemma, we propose a weakly supervised framework that can utilize word annotations, either in tight quadrangles or the more loose bounding boxes, for character detector training. When applied in scene text detection, we are thus able to train a robust character detector by exploiting word annotations in the rich large-scale real scene text datasets, e.g. ICDAR15 [??] and COCO-text [??]. The character detector acts as a key role in the pipeline of our text detection engine. It achieves the state-of-the-art performance on several challenging scene text detection benchmarks. We also demonstrate the flexibility of our pipeline by various scenarios, including deformed text detection and math expression recognition.
Illuminating Pedestrians via Simultaneous Detection & Segmentation
Authors: Xi Yin, Xiaoming Liu Garrick Brazil
Published in: IEEE International Conference on Computer Vision (ICCV), 2017 (Main Conference Track), 2017

Abstract: Pedestrian detection is a critical problem in computer vision with significant impact on safety in urban autonomous driving. In this work, we explore how semantic segmentation can be used to boost pedestrian detection accuracy while having little to no impact on network efficiency. We propose a segmentation infusion network to enable joint supervision on semantic segmentation and pedestrian detection. When placed properly, the additional supervision helps guide features in shared layers to become more sophisticated and helpful for the downstream pedestrian detector. Using this approach, we find weakly annotated boxes to be sufficient for considerable performance gains. We provide an in-depth analysis to demonstrate how shared layers are shaped by the segmentation supervision. In doing so, we show that the resulting feature maps become more semantically meaningful and robust to shape and occlusion. Overall, our simultaneous detection and segmentation framework achieves a considerable gain over the state-of-the-art on the Caltech pedestrian dataset, competitive performance on KITTI, and executes 2x faster than competitive methods.
Generalized Orderless Pooling Performs Implicit Salient Matching
Authors: Yang Gao, Trevor Darrell, Joachim Denzler, Erik Rodner Marcel Simon
Published in: IEEE International Conference on Computer Vision (ICCV), 2017 (Main Conference Track), 2017

Abstract: Most recent CNN architectures use average pooling as a final feature encoding step. In the field of fine-grained recognition, however, recent global representations like bilinear pooling offer improved performance. In this paper, we generalize average and bilinear pooling to "alpha-pooling", allowing for learning the pooling strategy during training. In addition, we present a novel way to visualize decisions made by these approaches. We identify parts of training images having the highest influence on the prediction of a given test image. This allows for justifying decisions to users and also for analyzing the influence of semantic parts. For example, we can show that the higher capacity VGG16 model focuses much more on the bird's head than, e.g., the lower-capacity VGG-M model when recognizing fine-grained bird categories. Both contributions allow us to analyze the difference when moving between average and bilinear pooling. In addition, experiments show that our generalized approach can outperform both across a variety of standard datasets.
Exploiting Spatial Structure for Localizing Manipulated Image Regions
Authors: Amit K. Roy-Chowdhury, Jason Bunk, Lakshmanan Nataraj, B. S. Manjunath Jawadul H. Bappy
Published in: IEEE International Conference on Computer Vision (ICCV), 2017 (Main Conference Track), 2017

Abstract: 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.
RDFNet: RGB-D Multi-Level Residual Feature Fusion for Indoor Semantic Segmentation
Authors: Ki-Sang Hong, Seungyong Lee Seong-Jin Park
Published in: IEEE International Conference on Computer Vision (ICCV), 2017 (Main Conference Track), 2017

Abstract: 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: Tobias Ollmann, Samuel Rota Bulo, Peter Kontschieder Gerhard Neuhold
Published in: IEEE International Conference on Computer Vision (ICCV), 2017 (Main Conference Track), 2017

Abstract: 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: Prem Natarajan Yue Wu
Published in: IEEE International Conference on Computer Vision (ICCV), 2017 (Main Conference Track), 2017

Abstract: 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: Rudrasis Chakraborty, Baba C. Vemuri Monami Banerjee
Published in: IEEE International Conference on Computer Vision (ICCV), 2017 (Main Conference Track), 2017

Abstract: 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: Zhi Han, Xi'ai Chen, Yao Wang, Deyu Meng, Dong Liang, Yandong Tang Qiong Luo
Published in: IEEE International Conference on Computer Vision (ICCV), 2017 (Main Conference Track), 2017

Abstract: 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: Shuhui Wang, Qingming Huang, Qi Tian Guoli Song
Published in: IEEE International Conference on Computer Vision (ICCV), 2017 (Main Conference Track), 2017

Abstract: 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.
Segmentation-Aware Convolutional Networks Using Local Attention Masks
Authors: Konstantinos G. Derpanis, Iasonas Kokkinos Adam W. Harley
Published in: IEEE International Conference on Computer Vision (ICCV), 2017 (Main Conference Track), 2017

Abstract: We introduce an approach to integrate segmentation information within a convolutional neural network (CNN). This counter-acts the tendency of CNNs to smooth information across regions and increases their spatial precision. To obtain segmentation information, we set up a CNN to provide an embedding space where region co-membership can be estimated based on Euclidean distance. We use these embeddings to compute a local attention mask relative to every neuron position. We incorporate such masks in CNNs and replace the convolution operation with a "segmentation-aware" variant that allows a neuron to selectively attend to inputs coming from its own region. We call the resulting network a segmentation-aware CNN because it adapts its filters at each image point according to local segmentation cues, while at the same time remaining fully-convolutional. We demonstrate the merit of our method on two widely different dense prediction tasks, that involve classification (semantic segmentation) and regression (optical flow). Our results show that in semantic segmentation we can replace DenseCRF inference with a cascade of segmentation-aware filters, and in optical flow we obtain clearly sharper responses than the ones obtained with comparable networks that do not use segmentation. In both cases segmentation-aware convolution yields systematic improvements over strong baselines.
Rotation Equivariant Vector Field Networks
Authors: Michele Volpi, Nikos Komodakis, Devis Tuia Diego Marcos
Published in: IEEE International Conference on Computer Vision (ICCV), 2017 (Main Conference Track), 2017

Abstract: In many computer vision tasks, we expect a particular behavior of the output with respect to rotations of the input image. If this relationship is explicitly encoded, instead of treated as any other variation, the complexity of the problem is decreased, leading to a reduction in the size of the required model. We propose Rotation Equivariant vector field Networks (RotEqNet) to encode rotation equivariance and invariance into Convolutional Neural Networks (CNNs). Each convolutional filter is applied at multiple orientations and returns a vector field that represents the magnitude and angle of the highest scoring orientation at every spatial location. A modified convolution operator using vector fields as inputs and filters can then be applied to obtain deep architectures. We test RotEqNet on several problems requiring different responses with respect to the inputs' rotation: image classification, biomedical image segmentation, orientation estimation and patch matching. In all cases, we show that RotEqNet offers very compact models in terms of number of parameters and provides results in line to those of networks orders of magnitude larger.
ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression
Authors: Jianxin Wu, Weiyao Lin Jian-Hao Luo
Published in: IEEE International Conference on Computer Vision (ICCV), 2017 (Main Conference Track), 2017

Abstract: We propose an efficient and unified framework, namely ThiNet, to simultaneously accelerate and compress CNN models in both training and inference stages. We focus on the filter level pruning, i.e., the whole filter would be discarded if it is less important. Our method does not change the original network structure, thus it can be perfectly supported by any off-the-shelf deep learning libraries. We formally establish filter pruning as an optimization problem, and reveal that we need to prune filters based on statistics information computed from its next layer, not the current layer, which differentiates ThiNet from existing methods. Experimental results demonstrate the effectiveness of this strategy, which has advanced the state-of-the-art. We also show the performance of ThiNet on ILSVRC-12 benchmark. ThiNet achieves 3.31x FLOPs reduction and 16.63x compression on VGG-16, with only 0.52% top-5 accuracy drop. Similar experiments with ResNet-50 reveal that even for a compact network, ThiNet can also reduce more than half of the parameters and FLOPs, at the cost of roughly 1% top-5 accuracy drop. Moreover, the original VGG-16 model can be further pruned into a very small model with only 5.05MB model size, preserving AlexNet level accuracy but showing much stronger generalization ability.