mtybilly/Instruct2DS huggingface dataset:
On the Effectiveness of Visible Watermarks
Authors: Michael Rubinstein, Ce Liu, William T. Freeman Tali Dekel
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (Main Conference Track), 2017

Abstract: Visible watermarking is a widely-used technique for marking and protecting copyrights of many millions of images on the web, yet it suffers from an inherent security flaw---watermarks are typically added in a consistent manner to many images. We show that this consistency allows to automatically estimate the watermark and recover the original images with high accuracy. Specifically, we present a generalized multi-image matting algorithm that  takes a watermarked image collection as input and automatically estimates the "foreground" (watermark), its alpha matte, and  the "background" (original) images.  Since such an attack relies on the consistency of watermarks across image collection, we explore and evaluate how it is affected by various types of inconsistencies in the watermark embedding that could potentially be used to make watermarking more secured.  We demonstrate the algorithm on stock imagery available on the web, and provide extensive quantitative analysis on synthetic watermarked data. A key takeaway message of this paper is that visible watermarks should be designed to not only be robust against removal from a single image, but to be more resistant to mass-scale removal from image collections as well.
Deep View Morphing
Authors: Junghyun Kwon, Max McFarland, Silvio Savarese Dinghuang Ji
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (Main Conference Track), 2017

Abstract: Recently, convolutional neural networks (CNN) have been successfully applied to view synthesis problems. However, such CNN-based methods can suffer from lack of texture details, shape distortions, or high computational complexity. In this paper, we propose a novel CNN architecture for view synthesis called "Deep View Morphing" that does not suffer from these issues. To synthesize a middle view of two input images, a rectification network first rectifies the two input images. An encoder-decoder network then generates dense correspondences between the rectified images and blending masks to predict the visibility of pixels of the rectified images in the middle view. A view morphing network finally synthesizes the middle view using the dense correspondences and blending masks. We experimentally show the proposed method significantly outperforms the state-of-the-art CNN-based view synthesis method.
Designing Illuminant Spectral Power Distributions for Surface Classification
Authors: Joyce Farrell, Brian Wandell Henryk Blasinski
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (Main Conference Track), 2017

Abstract: There are many scientific, medical and industrial imaging applications where users have full control of the scene illumination and color reproduction is not the primary objective For example, it is possible to co-design sensors and spectral illumination in order to classify and detect changes in biological tissues, organic and inorganic materials, and object surface properties.  In this paper, we propose two different approaches to illuminant spectrum selection for surface classification. In the supervised framework we formulate a biconvex optimization problem where we alternate between optimizing support vector classifier weights and optimal illuminants. We also describe a sparse Principal Component Analysis (PCA) dimensionality reduction approach that can be used with unlabeled data. We efficiently solve the non-convex PCA problem using a convex relaxation and Alternating Direction Method of Multipliers (ADMM). We compare the classification accuracy of a monochrome imaging sensor with optimized illuminants to the classification accuracy of conventional RGB cameras with natural broadband illumination.
End-To-End Learning of Driving Models From Large-Scale Video Datasets
Authors: Yang Gao, Fisher Yu, Trevor Darrell Huazhe Xu
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (Main Conference Track), 2017

Abstract: Robust perception-action models should be learned from training data with diverse visual appearances and realistic behaviors, yet current approaches to deep visuomotor policy learning have been generally limited to in-situ models learned from a single vehicle or simulation environment. We advocate learning a generic vehicle motion model from large scale crowd-sourced video data, and develop an end-to-end trainable architecture for learning to predict a distribution over future vehicle egomotion from instantaneous monocular camera observations and previous vehicle state. Our model incorporates a novel FCN-LSTM architecture, which can be learned from large-scale crowd-sourced vehicle action data, and leverages available scene segmentation side tasks to improve performance under a privileged learning paradigm. We provide a novel large-scale dataset of crowd-sourced driving behavior suitable for training our model, and report results predicting the driver action on held out sequences across diverse conditions.
Unsupervised Visual-Linguistic Reference Resolution in Instructional Videos
Authors: Joseph J. Lim, Li Fei-Fei, Juan Carlos Niebles De-An Huang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (Main Conference Track), 2017

Abstract: We propose an unsupervised method for reference resolution in instructional videos, where the goal is to temporally link an entity (e.g., "dressing") to the action (e.g., "mix yogurt") that produced it. The key challenge is the inevitable visual-linguistic ambiguities arising from the changes in both visual appearance and referring expression of an entity in the video. This challenge is amplified by the fact that we aim to resolve references with no supervision. We address these challenges by learning a joint visual-linguistic model, where linguistic cues can help resolve visual ambiguities and vice versa. We verify our approach by learning our model unsupervisedly using more than two thousand unstructured cooking videos from YouTube, and show that our visual-linguistic model can substantially improve upon state-of-the-art linguistic only model on reference resolution in instructional videos.
Dense Captioning With Joint Inference and Visual Context
Authors: Kevin Tang, Jianchao Yang, Li-Jia Li Linjie Yang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (Main Conference Track), 2017

Abstract: Dense captioning is a newly emerging computer vision topic for understanding images with dense language descriptions. The goal is to densely detect visual concepts (e.g., objects, object parts, and interactions between them) from images, labeling each with a short descriptive phrase. We identify two key challenges of dense captioning that need to be properly addressed when tackling the problem. First, dense visual concept annotations in each image are associated with highly overlapping target regions, making accurate localization of each visual concept challenging. Second, the large amount of visual concepts makes it hard to recognize each of them by appearance alone. We propose a new model pipeline based on two novel ideas, joint inference and context fusion, to alleviate these two challenges. We design our model architecture in a methodical manner and thoroughly evaluate the variations in architecture. Our final model,  compact and efficient, achieves state-of-the-art accuracy on Visual Genome for dense captioning with a relative gain of 73% compared to the previous best algorithm. Qualitative experiments also reveal the semantic capabilities of our model in dense captioning.
Unsupervised Learning of Long-Term Motion Dynamics for Videos
Authors: Boya Peng, De-An Huang, Alexandre Alahi, Li Fei-Fei Zelun Luo
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (Main Conference Track), 2017

Abstract: We present an unsupervised representation learning approach that compactly encodes the motion dependencies in videos. Given a pair of images from a video clip, our framework learns to predict the long-term 3D motions. To reduce the complexity of the learning framework, we propose to describe the motion as a sequence of atomic 3D flows computed with RGB-D modality. We  use a Recurrent Neural Network based Encoder-Decoder framework to predict these sequences of flows. We argue that in order for the decoder to reconstruct these sequences, the encoder must learn a robust video representation that captures long-term motion dependencies and spatial-temporal relations. We demonstrate the effectiveness of our learned temporal representations on  activity classification across multiple modalities and datasets such as NTU RGB+D and  MSR Daily Activity 3D. Our framework is generic to any input modality, i.e., RGB, depth, and RGB-D videos.
CLKN: Cascaded Lucas-Kanade Networks for Image Alignment
Authors: Chun-Nan Chou, Edward Y. Chang Che-Han Chang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (Main Conference Track), 2017

Abstract: This paper proposes a data-driven approach for image alignment. Our main contribution is a novel network architecture that combines the strengths of convolutional neural networks (CNNs) and the Lucas-Kanade algorithm. The main component of this architecture is a Lucas-Kanade layer that performs the inverse compositional algorithm on convolutional feature maps. To train our network, we develop a cascaded feature learning method that incorporates the coarse-to-fine strategy into the training process. This method learns a pyramid representation of convolutional features in a cascaded manner and yields a cascaded network that performs coarse-to-fine alignment on the feature pyramids. We apply our model to the task of homography estimation, and perform training and evaluation on a large labeled dataset generated from the MS-COCO dataset. Experimental results show that the proposed approach significantly outperforms the other methods.
Agent-Centric Risk Assessment: Accident Anticipation and Risky Region Localization
Authors: Shih-Han Chou, Fu-Hsiang Chan, Juan Carlos Niebles, Min Sun Kuo-Hao Zeng
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (Main Conference Track), 2017

Abstract: For survival, a living agent (e.g., human in Fig. 1(a)) must have the ability to assess risk (1) by temporally anticipating accidents before they occur (Fig. 1(b)), and (2) by spatially localizing risky regions (Fig. 1(c)) in the environment to move away from threats. In this paper, we take an agent-centric approach to study the accident anticipation and risky region localization tasks. We propose a novel soft-attention Recurrent Neural Network (RNN) which explicitly models both spatial and appearance-wise non-linear interaction between the agent triggering the event and another agent or static-region involved. In order to test our proposed method, we introduce the Epic Fail (EF) dataset consisting of 3000 viral videos capturing various accidents. In the experiments, we evaluate the risk assessment accuracy both in the temporal domain (accident anticipation) and spatial domain (risky region localization) on our EF dataset and the Street Accident (SA) dataset. Our method consistently outperforms other baselines on both datasets.
ShapeOdds: Variational Bayesian Learning of Generative Shape Models
Authors: Ross Whitaker Shireen Elhabian
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (Main Conference Track), 2017

Abstract: Shape models provide a compact parameterization of a class of shapes, and have been shown to be important to a variety of vision problems, including object detection, tracking, and image segmentation. Learning generative shape models from grid-structured representations, aka silhouettes, is usually hindered by (1) data likelihoods with intractable marginals and posteriors, (2) high-dimensional shape spaces with limited training samples (and the associated risk of overfitting), and (3) estimation of hyperparameters relating to model complexity that often entails computationally expensive grid searches. In this paper, we propose a Bayesian treatment that relies on direct probabilistic formulation for learning generative shape models in the silhouettes space. We propose a variational approach for learning a latent variable model in which we make use of, and extend, recent works on variational bounds of logistic-Gaussian integrals to circumvent intractable marginals and posteriors. Spatial coherency and sparsity priors are also incorporated to lend stability to the optimization problem by regularizing the solution space while avoiding overfitting in this high-dimensional, low-sample-size scenario. We deploy a type-II maximum likelihood estimate of the model hyperparameters to avoid grid searches. We demonstrate that the proposed model generates realistic samples, generalizes to unseen examples, and is able to handle missing regions and/or background clutter, while comparing favorably with recent, neural-network-based approaches.
Expecting the Unexpected: Training Detectors for Unusual Pedestrians With Adversarial Imposters
Authors: Deva Ramanan Shiyu Huang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (Main Conference Track), 2017

Abstract: As autonomous vehicles become an every-day reality, high-accuracy pedestrian detection is of paramount practical importance.  Pedestrian detection is a highly researched topic with mature methods,  but most datasets (for both training and evaluation) focus on common scenes of people engaged in typical walking poses on sidewalks. But performance is most crucial for dangerous scenarios that are rarely observed, such as children playing in the street and people using bicycles/skateboards in unexpected ways. Such "in-the-tail" data is notoriously hard to observe, making both training and testing difficult. To analyze this problem, we have collected a novel annotated dataset of dangerous scenarios called the Precarious Pedestrian dataset. Even given a  dedicated collection effort, it is relatively small by contemporary standards (~ 1000 images). To explore large-scale data-driven learning, we explore the use of synthetic data generated by a game engine. A significant challenge is selected the right "priors" or parameters for synthesis: we would like realistic data with realistic poses and object configurations. Inspired by Generative Adversarial Networks, we generate a massive amount of synthetic data and train a discriminative classifier to select a realistic subset (that fools the classifier), which we deem Synthetic Imposters. We demonstrate that this pipeline allows one to generate realistic (or adverserial) training data by making use of rendering/animation engines. Interestingly, we also demonstrate that such data can be used to rank algorithms, suggesting that Synthetic Imposters can also be used for "in-the-tail" validation at test-time, a notoriously difficult challenge for real-world deployment.
ER3: A Unified Framework for Event Retrieval, Recognition and Recounting
Authors: Gang Hua, Dongqing Zhang, Nebojsa Jojic, Le Wang, Jianru Xue, Nanning Zheng Zhanning Gao
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (Main Conference Track), 2017

Abstract: We develop a unified framework for complex event retrieval, recognition and recounting. The framework is based on a compact video representation that exploits the temporal correlations in image features. Our feature alignment procedure identifies and removes the feature redundancies across frames and outputs an intermediate tensor representation we call video imprint. The video imprint is then fed into a reasoning network, whose attention mechanism parallels that of memory networks used in language modeling. The reasoning network simultaneously recognizes the event category and locates the key pieces of evidence for event recounting. In event retrieval tasks, we show that the compact video representation aggregated from the video imprint achieves significantly better retrieval accuracy compared with existing methods. We also set new state of the art results in event recognition tasks with an additional benefit: The latent structure in our reasoning network highlights the areas of the video imprint and can be directly used for event recounting. As video imprint maps back to locations in the video frames, the network allows not only the identification of key frames but also specific areas inside each frame which are most influential to the decision process.
Outlier-Robust Tensor PCA
Authors: Jiashi Feng Pan Zhou
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (Main Conference Track), 2017

Abstract: Low-rank tensor analysis is important for various real applications in computer vision. However, existing methods focus on recovering a low-rank tensor contaminated by Gaussian or gross sparse noise and hence cannot effectively handle outliers that are common in practical tensor data. To solve this issue, we propose an outlier-robust tensor principle component analysis (OR-TPCA) method for simultaneous low-rank tensor recovery and outlier detection. For intrinsically low-rank tensor observations with arbitrary outlier corruption, OR-TPCA is the first method that has provable performance guarantee for exactly recovering the tensor subspace and detecting outliers  under mild conditions.  Since tensor data are naturally high-dimensional and multi-way, we further develop a fast randomized algorithm that requires small sampling size yet can substantially accelerate OR-TPCA without performance drop. Experimental results on four tasks: outlier detection, clustering, semi-supervised and supervised learning, clearly demonstrate the advantages of our method.
Mind the Class Weight Bias: Weighted Maximum Mean Discrepancy for Unsupervised Domain Adaptation
Authors: Yukang Ding, Peihua Li, Qilong Wang, Yong Xu, Wangmeng Zuo Hongliang Yan
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (Main Conference Track), 2017

Abstract: In domain adaptation, maximum mean discrepancy (MMD) has been widely adopted as a discrepancy metric between the distributions of source and target domains. However, existing MMD-based domain adaptation methods generally ignore the changes of class prior distributions, i.e., class weight bias across domains. This remains an open problem but ubiquitous for domain adaptation, which can be caused by changes in sample selection criteria and application scenarios. We show that MMD cannot account for class weight bias and results in degraded domain adaptation performance. To address this issue, a weighted MMD model is proposed in this paper. Specifically, we introduce class-specific auxiliary weights into the original MMD for exploiting the class prior probability on source and target domains, whose challenge lies in the fact that the class label in target domain is unavailable. To account for it, our proposed weighted MMD model is defined by introducing an auxiliary weight for each class in the source domain, and a classification EM algorithm is suggested by alternating between assigning the pseudo-labels, estimating auxiliary weights and updating model parameters. Extensive experiments demonstrate the superiority of our weighted MMD over conventional MMD for domain adaptation.
SyncSpecCNN: Synchronized Spectral CNN for 3D Shape Segmentation
Authors: Hao Su, Xingwen Guo, Leonidas J. Guibas Li Yi
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (Main Conference Track), 2017

Abstract: In this paper, we study the problem of semantic annotation on 3D models that are represented as shape graphs.  A functional view is taken to represent localized information on graphs, so that annotations such as part segment or keypoint are nothing but 0-1 indicator vertex functions. Compared with images that are 2D grids, shape graphs are irregular and non-isomorphic data structures. To enable the prediction of vertex functions on them by convolutional neural networks, we resort to spectral CNN method that enables weight sharing by parametrizing kernels in the spectral domain spanned by graph Laplacian eigenbases. Under this setting, our network, named SyncSpecCNN, strives to overcome two key challenges: how to share coefficients and conduct multi-scale analysis in different parts of the graph for a single shape, and how to share information across related but different shapes that may be represented by very different graphs. Towards these goals, we introduce a spectral parametrization of dilated convolutional kernels and a spectral transformer network. Experimentally we tested SyncSpecCNN on various tasks, including 3D shape part segmentation and keypoint prediction.  State-of-the-art performance has been achieved on all benchmark datasets.
Unrolling the Shutter: CNN to Correct Motion Distortions
Authors: Yogesh Balaji, A. N. Rajagopalan Vijay Rengarajan
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (Main Conference Track), 2017

Abstract: Row-wise exposure delay present in CMOS cameras is responsible for skew and curvature distortions known as the rolling shutter (RS) effect while imaging under camera motion. Existing RS correction methods resort to using multiple images or tailor scene-specific correction schemes. We propose a convolutional neural network (CNN) architecture that automatically learns essential scene features from a single RS image to estimate the row-wise camera motion and undo RS distortions back to the time of first-row exposure. We employ long rectangular kernels to specifically learn the effects produced by the row-wise exposure. Experiments reveal that our proposed architecture performs better than the conventional CNN employing square kernels. Our single-image correction method fares well even operating in a frame-by-frame manner against video-based methods and performs better than scene-specific correction schemes even under challenging situations.
Deep Level Sets for Salient Object Detection
Authors: Bing Shuai, Jun Liu, Gang Wang Ping Hu
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (Main Conference Track), 2017

Abstract: Deep learning has been applied to saliency detection in recent years. The superior performance has proved that deep networks can model the semantic properties of salient objects. Yet it is difficult for a deep network to discriminate pixels belonging to similar receptive fields around the object boundaries, thus deep networks may output maps with blurred saliency and inaccurate boundaries. To tackle such an issue, in this work, we propose a deep Level Set network to produce compact and uniform saliency maps. Our method drives the network to learn a Level Set function for salient objects so it can output more accurate boundaries and compact saliency. Besides, to propagate saliency information among pixels and recover full resolution saliency map, we extend a superpixel-based guided filter to be a layer in the network. The proposed network has a simple structure and is trained end-to-end. During testing, the network can produce saliency maps by efficiently feedforwarding testing images at a speed over 12FPS on GPUs. Evaluations on benchmark datasets show that the proposed method achieves state-of-the-art performance.
Instance-Aware Image and Sentence Matching With Selective Multimodal LSTM
Authors: Wei Wang, Liang Wang Yan Huang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (Main Conference Track), 2017

Abstract: Effective image and sentence matching depends on how to well measure their global visual-semantic similarity. Based on the observation that such a global similarity arises from a complex aggregation of multiple local similarities between pairwise instances of image (objects) and sentence (words), we propose a selective multimodal Long Short-Term Memory network (sm-LSTM) for instance-aware image and sentence matching. The sm-LSTM includes a multimodal context-modulated attention scheme at each timestep that can selectively attend to a pair of instances of image and sentence, by predicting pairwise instance-aware saliency maps for image and sentence. For selected pairwise instances, their representations are obtained based on the predicted saliency maps, and then compared to measure their local similarity. By similarly measuring multiple local similarities within a few timesteps, the sm-LSTM sequentially aggregates them with hidden states to obtain a final matching score as the desired global similarity. Extensive experiments show that our model can well match image and sentence with complex content, and achieve the state-of-the-art results on two public benchmark datasets.
From Motion Blur to Motion Flow: A Deep Learning Solution for Removing Heterogeneous Motion Blur
Authors: Jie Yang, Lingqiao Liu, Yanning Zhang, Ian Reid, Chunhua Shen, Anton van den Hengel, Qinfeng Shi Dong Gong
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (Main Conference Track), 2017

Abstract: Removing pixel-wise heterogeneous motion blur is challenging due to the ill-posed nature of the problem. The predominant solution is to estimate the blur kernel by adding a prior, but extensive literature on the subject indicates the difficulty in identifying a prior which is suitably informative, and general. Rather than imposing a prior based on theory, we propose instead to learn one from the data. Learning a prior over the latent image would require modeling all possible image content. The critical observation underpinning our approach, however, is that learning the motion flow instead allows the model to focus on the cause of the blur, irrespective of the image content. This is a much easier learning task, but it also avoids the iterative process through which latent image priors are typically applied. Our approach directly estimates the motion flow from the blurred image through a fully-convolutional deep neural network (FCN) and recovers the unblurred image from the estimated motion flow. Our FCN is the first universal end-to-end mapping from the blurred image to the dense motion flow. To train the FCN, we simulate motion flows to generate synthetic blurred-image-motion-flow pairs thus avoiding the need for human labeling. Extensive experiments on challenging realistic blurred images demonstrate that the proposed method outperforms the state-of-the-art.
Deep Temporal Linear Encoding Networks
Authors: Vivek Sharma, Luc Van Gool Ali Diba
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (Main Conference Track), 2017

Abstract: The CNN-encoding of features from entire videos for the representation of human actions has rarely been addressed. Instead, CNN work has focused on approaches to fuse spatial and temporal networks, but these were typically limited to processing shorter sequences. We present a new video representation, called temporal linear encoding (TLE) and embedded inside of CNNs as a new layer, which captures the appearance and motion throughout entire videos. It encodes this aggregated information into a robust video feature representation, via end-to-end learning. Advantages of TLEs are: (a) they encode the entire video into a compact feature representation, learning the semantics and a discriminative feature space; (b) they are applicable to all kinds of networks like 2D and 3D CNNs for video classification; and (c) they model feature interactions in a more expressive way and without loss of information. We conduct experiments on two challenging human action datasets: HMDB51 and UCF101. The experiments show that TLE outperforms current state-of-the-art methods on both datasets.
End-To-End Training of Hybrid CNN-CRF Models for Stereo
Authors: Christian Reinbacher, Alexander Shekhovtsov, Thomas Pock Patrick Knobelreiter
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (Main Conference Track), 2017

Abstract: We propose a novel and principled hybrid CNN+CRF model for stereo estimation. Our model allows to exploit the advantages of both, convolutional neural networks (CNNs) and conditional random fields (CRFs) in an unified approach. The CNNs compute expressive features for matching and distinctive color edges, which in turn are used to compute the unary and binary costs of the CRF. For inference, we apply a recently proposed highly parallel dual block descent algorithm which only needs a small fixed number of iterations to compute a high-quality approximate minimizer. As the main contribution of the paper, we propose a theoretically sound method based on the structured output support vector machine (SSVM) to train the hybrid CNN+CRF model on large-scale data end-to-end. Our trained models perform very well despite the fact that we are using shallow CNNs and do not apply any kind of post-processing to the final output of the CRF. We evaluate our combined models on challenging stereo benchmarks such as Middlebury 2014 and Kitti 2015 and also investigate the performance of each individual component.
Deep Feature Flow for Video Recognition
Authors: Yuwen Xiong, Jifeng Dai, Lu Yuan, Yichen Wei Xizhou Zhu
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (Main Conference Track), 2017

Abstract: Deep convolutional neutral networks have achieved great success on image recognition tasks. Yet, it is non-trivial to transfer the state-of-the-art image recognition networks to videos as per-frame evaluation is too slow and unaffordable. We present deep feature flow, a fast and accurate framework for video recognition. It runs the expensive convolutional sub-network only on sparse key frames and propagates their deep feature maps to other frames via a flow field. It achieves significant speedup as flow computation is relatively fast. The end-to-end training of the whole architecture significantly boosts the recognition accuracy. Deep feature flow is flexible and general. It is validated on two recent large scale video datasets. It makes a large step towards practical video recognition. Code would be released.
Fully Convolutional Instance-Aware Semantic Segmentation
Authors: Haozhi Qi, Jifeng Dai, Xiangyang Ji, Yichen Wei Yi Li
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (Main Conference Track), 2017

Abstract: We present the first fully convolutional end-to-end solution for instance-aware semantic segmentation task. It inherits all the merits of FCNs for semantic segmentation and instance mask proposal. It performs instance mask prediction and classification jointly. The underlying convolutional representation is fully shared between the two sub-tasks, as well as between all regions of interest. The network architecture is highly integrated and efficient. It achieves state-of-the-art performance in both accuracy and efficiency. It wins the COCO 2016 segmentation competition by a large margin. Code would be released.
Truncated Max-Of-Convex Models
Authors: M. Pawan Kumar Pankaj Pansari
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (Main Conference Track), 2017

Abstract: Truncated convex models (TCM) are a special case of pair- wise random fields that have been widely used in computer vision. However, by restricting the order of the potentials to be at most two, they fail to capture useful image statistics. We propose a natural generalization of TCM to high-order random fields, which we call truncated max-of-convex models (TMCM). The energy function of TMCM consists of two types of potentials: (i) unary potential, which has no restriction on its form; and (ii) high-order potential, which is the sum of the truncation of the m largest convex distances over disjoint pairs of random variables in an arbitrary size clique. The use of a convex distance function encourages smoothness, while truncation allows for discontinuities in the labeling. By using m > 1, TMCM provides robustness towards errors in the definition of the cliques. In order to minimize the energy function of a TMCM over all possible labelings, we design an efficient st-mincut based range expansion algorithm. We prove the accuracy of our algorithm by establishing strong multiplicative bounds for several special cases of interest. Using synthetic and standard real datasets, we demonstrate the benefit of our high-order TMCM over pairwise TCM, as well as the benefit of our range expansion algorithm over other st-mincut based approaches.
Asymmetric Feature Maps With Application to Sketch Based Retrieval
Authors: Ondrej Chum Giorgos Tolias
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (Main Conference Track), 2017

Abstract: We propose a novel concept of asymmetric feature maps (AFM), which allows to evaluate multiple kernels between a query and database entries without increasing the memory requirements. To demonstrate the advantages of the AFM method, we derive a short vector image representation that, due to asymmetric feature maps, supports efficient scale and translation invariant sketch-based image retrieval. Unlike most of the short-code based retrieval systems, the proposed method provides the query localization in the retrieved image. The efficiency of the search is boosted by approximating a 2D translation search via trigonometric polynomial of scores by 1D projections. The projections are a special case of AFM. An order of magnitude speed-up is achieved compared to traditional trigonometric polynomials. The results are boosted by an image-based average query expansion, exceeding significantly the state of the art on standard benchmarks.
Instance-Level Salient Object Segmentation
Authors: Yuan Xie, Liang Lin, Yizhou Yu Guanbin Li
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (Main Conference Track), 2017

Abstract: Image saliency detection has recently witnessed rapid progress due to deep convolutional neural networks. However, none of the existing methods is able to identify object instances in the detected salient regions. In this paper, we present a salient instance segmentation method that produces a saliency mask with distinct object instance labels for an input image. Our method consists of three steps, estimating saliency map, detecting salient object contours and identifying salient object instances. For the first two steps, we propose a multiscale saliency refinement network, which generates high-quality salient region masks and salient object contours. Once integrated with multiscale combinatorial grouping and a MAP-based subset optimization framework, our method can generate very promising salient object instance segmentation results. To promote further research and evaluation of salient instance segmentation, we also construct a new database of 1000 images and their pixelwise salient instance annotations. Experimental results demonstrate that our proposed method is capable of achieving state-of-the-art performance on all public benchmarks for salient region detection as well as on our new dataset for salient instance segmentation.
Kernel Square-Loss Exemplar Machines for Image Retrieval
Authors: Joaquin Zepeda, Jean Ponce, Francis Bach, Patrick Perez Rafael S. Rezende
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (Main Conference Track), 2017

Abstract: Zepeda and Perez have recently demonstrated the promise of the exemplar SVM (ESVM) as a feature encoder for image retrieval. This paper extends this approach in several directions: We first show that replacing the hinge loss by the square loss in the ESVM cost function significantly reduces encoding time with negligible effect on accuracy. We call this model square-loss exemplar machine, or SLEM.  We then introduce a kernelized SLEM which can be implemented efficiently through low-rank matrix decomposition, and displays improved performance.  Both SLEM variants exploit the fact that the negative examples are fixed, so most of the SLEM computational complexity is relegated to an offline process independent of the positive examples.  Our experiments establish the performance and computational advantages of our approach using a large array of base features and standard image retrieval datasets.
Direct Photometric Alignment by Mesh Deformation
Authors: Nianjuan Jiang, Shuaicheng Liu, Loong-Fah Cheong, Minh Do, Jiangbo Lu Kaimo Lin
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (Main Conference Track), 2017

Abstract: The choice of motion models is vital in applications like image/video stitching and video stabilization. Conventional methods explored different approaches ranging from simple global parametric models to complex per-pixel optical flow. Mesh-based warping methods achieve a good balance between computational complexity and model flexibility. However, they typically require high quality feature correspondences and suffer from mismatches and low-textured image content. In this paper, we propose a mesh-based photometric alignment method that minimizes pixel intensity difference instead of Euclidean distance of known feature correspondences. The proposed method combines the superior performance of dense photometric alignment with the efficiency of mesh-based image warping. It achieves better global alignment quality than the feature-based counterpart in textured images, and more importantly, it is also robust to low-textured image content. Abundant experiments show that our method can handle a variety of images and videos, and outperforms representative state-of-the-art methods in both image stitching and video stabilization tasks.
Semantic Multi-View Stereo: Jointly Estimating Objects and Voxels
Authors: Michael J. Black, Andreas Geiger Ali Osman Ulusoy
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (Main Conference Track), 2017

Abstract: Dense 3D reconstruction from RGB images is a highly ill-posed problem due to occlusions, textureless or reflective surfaces, as well as other challenges. We propose object-level shape priors to address these ambiguities. Towards this goal, we formulate a probabilistic model that integrates multi-view image evidence with 3D shape information from multiple objects. Inference in this model yields a dense 3D reconstruction of the scene as well as the existence and precise 3D pose of the objects in it. Our approach is able to recover fine details not captured in the input shapes while defaulting to the input models in occluded regions where image evidence is weak. Due to its probabilistic nature, the approach is able to cope with the approximate geometry of the 3D models as well as input shapes that are not present in the scene. We evaluate the approach quantitatively on several challenging indoor and outdoor datasets.
HOPE: Hierarchical Object Prototype Encoding for Efficient Object Instance Search in Videos
Authors: Yuwei Wu, Junsong Yuan Tan Yu
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (Main Conference Track), 2017

Abstract: This paper tackles the problem of efficient and effective object instance search in videos. To effectively capture the relevance between a query and video frames and precisely localize the particular object, we leverage the object proposals to improve the quality of object instance search in videos. However, hundreds of object proposals obtained from each frame could result in unaffordable memory and computational cost. To this end, we present a simple yet effective hierarchical object prototype encoding (HOPE) model to accelerate the object instance search without sacrificing accuracy, which exploits both the spatial and temporal self-similarity property existing in object proposals generated from video frames. We design two types of sphere k-means methods, i.e., spatially-constrained sphere k-means and temporally-constrained sphere k-means to learn frame-level object prototypes and dataset-level object prototypes, respectively.  In this way, the object instance search problem is cast to the sparse matrix-vector multiplication problem. Thanks to the sparsity of the codes, both the memory and computational cost are significantly reduced. Experimental results on two video datasets demonstrate that our approach significantly improves the performance of video object instance search over other state-of-the-art fast search schemes.
Learning Adaptive Receptive Fields for Deep Image Parsing Network
Authors: Yao Sun, Jinqiao Wang, Hanjiang Lai, Si Liu Zhen Wei
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (Main Conference Track), 2017

Abstract: In this paper, we introduce a novel approach to regulate receptive field in deep image parsing network automatically. Unlike previous works which have stressed much importance on obtaining better receptive fields using manually selected dilated convolutional kernels, our approach uses two affine transformation layers in the network's backbone and operates on feature maps. Feature maps will be inflated/shrinked by the new layer and therefore receptive fields in following layers are changed accordingly. By end-to-end training, the whole framework is data-driven without laborious manual intervention. The proposed method is generic across dataset and different tasks. We conduct extensive experiments on both general parsing task and face parsing task as concrete examples to demonstrate the method's superior regulation ability over manual designs.
Contour-Constrained Superpixels for Image and Video Processing
Authors: Won-Dong Jang, Chang-Su Kim Se-Ho Lee
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (Main Conference Track), 2017

Abstract: A novel contour-constrained superpixel (CCS) algorithm is proposed in this work. We initialize superpixels and regions in a regular grid and then refine the superpixel label of each region hierarchically from block to pixel levels. To make superpixel boundaries compatible with object contours, we propose the notion of contour pattern matching and formulate an objective function including the contour constraint. Furthermore, we extend the CCS algorithm to generate temporal superpixels for video processing. We initialize superpixel labels in each frame by transferring those in the previous frame and refine the labels to make superpixels temporally consistent as well as compatible with object contours. Experimental results demonstrate that the proposed algorithm provides better performance than the state-of-the-art superpixel methods.
Learning to Predict Stereo Reliability Enforcing Local Consistency of Confidence Maps
Authors: Stefano Mattoccia Matteo Poggi
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (Main Conference Track), 2017

Abstract: Confidence measures estimate unreliable disparity assignments performed by a stereo matching algorithm and, as recently proved, can be used for several purposes. This paper aims at increasing, by means of a deep network, the effectiveness of state-of-the-art confidence measures exploiting the local consistency assumption. We exhaustively evaluated our proposal on 23 confidence measures, including 5 top-performing ones based on random-forests and CNNs, training our networks with two popular stereo algorithms and a small subset (25 out of 194 frames) of the KITTI 2012 dataset. Experimental results show that our approach dramatically increases the effectiveness of all the 23 confidence measures on the remaining frames. Moreover, without re-training, we report a further cross-evaluation on KITTI 2015 and Middlebury 2014 confirming that our proposal provides remarkable improvements for each confidence measure even when dealing with significantly different input data. To the best of our knowledge, this is the first method to move beyond conventional pixel-wise confidence estimation.
FlowNet 2.0: Evolution of Optical Flow Estimation With Deep Networks
Authors: Nikolaus Mayer, Tonmoy Saikia, Margret Keuper, Alexey Dosovitskiy, Thomas Brox Eddy Ilg
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (Main Conference Track), 2017

Abstract: The FlowNet demonstrated that optical flow estimation can be cast as a learning problem. However, the state of the art with regard to the quality of the flow has still been defined by traditional methods. Particularly on small displacements and real-world data, FlowNet cannot compete with variational methods. In this paper, we advance the concept of end-to-end learning of optical flow and make it work really well. The large improvements in quality and speed are caused by three major contributions: first, we focus on the training data and show that the schedule of presenting data during training is very important. Second, we develop a stacked architecture that includes warping of the second image with intermediate optical flow. Third, we elaborate on small displacements by introducing a subnetwork specializing on small motions. FlowNet 2.0 is only marginally slower than the original FlowNet but decreases the estimation error by more than 50%. It performs on par with state-of-the-art methods, while running at interactive frame rates. Moreover, we present faster variants that allow optical flow computation at up to 140fps with accuracy matching the original FlowNet.
Growing a Brain: Fine-Tuning by Increasing Model Capacity
Authors: Deva Ramanan, Martial Hebert Yu-Xiong Wang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (Main Conference Track), 2017

Abstract: CNNs have made an undeniable impact on computer vision through the ability to learn high-capacity models with large annotated training sets. One of their remarkable properties is the ability to transfer knowledge from a large source dataset to a (typically smaller) target dataset. This is usually accomplished through fine-tuning a fixed-size network on new target data. Indeed, virtually every contemporary visual recognition system makes use of fine-tuning to transfer knowledge from ImageNet. In this work, we analyze what components and parameters change during fine-tuning, and discover that increasing model capacity allows for more natural model adaptation through fine-tuning. By making an analogy to developmental learning, we demonstrate that growing a CNN with additional units, either by widening existing layers or deepening the overall network, significantly outperforms classic fine-tuning approaches. But in order to properly grow a network, we show that newly-added units must be appropriately normalized to allow for a pace of learning that is consistent with existing units. We empirically validate our approach on several benchmark datasets, producing state-of-the-art results.
Dynamic Attention-Controlled Cascaded Shape Regression Exploiting Training Data Augmentation and Fuzzy-Set Sample Weighting
Authors: Josef Kittler, William Christmas, Patrik Huber, Xiao-Jun Wu Zhen-Hua Feng
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (Main Conference Track), 2017

Abstract: We present a new Cascaded Shape Regression (CSR) architecture, namely Dynamic Attention-Controlled CSR (DAC-CSR), for robust facial landmark detection on unconstrained faces. Our DAC-CSR divides facial landmark detection into three cascaded sub-tasks: face bounding box refinement, general CSR and attention-controlled CSR. The first two stages refine initial face bounding boxes and output intermediate facial landmarks. Then, an online dynamic model selection method is used to choose appropriate domain-specific CSRs for further landmark refinement. The key innovation of our DAC-CSR is the fault-tolerant mechanism, using fuzzy set sample weighting, for attention-controlled domain-specific model training. Moreover, we advocate data augmentation with a simple but effective 2D profile face generator, and context-aware feature extraction for better facial feature representation. Experimental results obtained on challenging datasets demonstrate the merits of our DAC-CSR over the state-of-the-art methods.