mtybilly/Instruct2DS huggingface dataset:
Do It Yourself Hyperspectral Imaging With Everyday Digital Cameras
Authors: Seoung Wug Oh, Michael S. Brown, Marc Pollefeys, Seon Joo Kim
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: Capturing hyperspectral images requires expensive and specialized hardware that is not readily accessible to most users. Digital cameras, on the other hand, are significantly cheaper in comparison and can be easily purchased and used. In this paper, we present a framework for reconstructing hyperspectral images by using multiple consumer-level digital cameras. Our approach works by exploiting the different spectral sensitivities of different camera sensors. In particular, due to the differences in spectral sensitivities of the cameras, different cameras yield different RGB measurements for the same spectral signal. We  introduce an algorithm that is able to combine and convert these different RGB measurements into a single hyperspectral image for both indoor and outdoor scenes. This camera-based approach allows hyperspectral imaging at a fraction of the cost of most existing hyperspectral hardware. We validate the accuracy of our reconstruction against ground truth hyperspectral images (using both synthetic and real cases) and show its usage on relighting applications.
Automatic Content-Aware Color and Tone Stylization
Authors: Joon-Young Lee, Kalyan Sunkavalli, Zhe Lin, Xiaohui Shen, In So Kweon
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: We introduce a new technique that automatically generates diverse, visually compelling stylizations for a photograph in an unsupervised manner. We achieve this by learning style ranking for a given input using a large photo collection and selecting a diverse subset of matching styles for final style transfer. We also propose an improved technique that transfers the global color and tone of the chosen exemplars to the input photograph while avoiding the common visual artifacts produced by the existing style transfer methods. Together, our style selection and transfer techniques produce compelling, artifact-free results on a wide range of input photographs, and a user study shows that our results are preferred over other techniques.
DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation
Authors: Hao Chen, Xiaojuan Qi, Lequan Yu, Pheng-Ann Heng
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: The morphology of glands has been used routinely by pathologists to assess the malignancy degree of adenocarcinomas. Accurate segmentation of glands from histology images is a crucial step to obtain reliable morphological statistics for quantitative diagnosis. In this paper, we proposed an efficient deep contour-aware network (DCAN) to solve this challenging problem under a unified multi-task learning framework. In the proposed network, multi-level contextual features from the hierarchical architecture are explored with auxiliary supervision for accurate gland segmentation. When incorporated with multi-task regularization during the training, the discriminative capability of intermediate features can be further improved. Moreover, our network can not only output accurate probability maps of glands, but also depict clear contours simultaneously for separating clustered objects, which further boosts the gland segmentation performance. This unified framework can be efficient when applied to large-scale histopathological data without resorting to additional steps to generate contours based on low-level cues for post-separating. Our method won the 2015 MICCAI Gland Segmentation Challenge out of 13 competitive teams, surpassing all the other methods by a significant margin.
Learning to Read Chest X-Rays: Recurrent Neural Cascade Model for Automated Image Annotation
Authors: Hoo-Chang Shin, Kirk Roberts, Le Lu, Dina Demner-Fushman, Jianhua Yao, Ronald M. Summers
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: Despite the recent advances in automatically describing image contents, their applications have been mostly limited to image caption datasets containing natural images (e.g., Flickr 30k, MSCOCO). In this paper, we present a deep learning model to efficiently detect a disease from an image and annotate its contexts (e.g., location, severity and the affected organs). We employ a publicly available radiology dataset of chest x-rays and their reports, and use its image annotations to mine disease names to train convolutional neural networks (CNNs). In doing so, we adopt various regularization techniques to circumvent the large normal-vs-diseased cases bias. Recurrent neural networks (RNNs) are then trained to describe the contexts of a detected disease, based on the deep CNN features. Moreover, we introduce a novel approach to use the weights of the already trained pair of CNN/RNN on the domain-specific image/text dataset, to infer the joint image/text contexts for composite image labeling. Significantly improved image annotation results are demonstrated using the recurrent neural cascade model by taking the joint image/text contexts into account.
Conformal Surface Alignment With Optimal Mobius Search
Authors: Huu Le, Tat-Jun Chin, David Suter
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: Deformations of surfaces with the same intrinsic shape can often be described accurately by a conformal model. A major focus of computational conformal geometry is the estimation of the conformal mapping that aligns a given pair of object surfaces. The uniformization theorem en- ables this task to be acccomplished in a canonical 2D do- main, wherein the surfaces can be aligned using a M obius transformation. Current algorithms for estimating M obius transformations, however, often cannot provide satisfactory alignment or are computationally too costly. This paper in- troduces a novel globally optimal algorithm for estimating Mobius transformations to align surfaces that are topologi- cal discs. Unlike previous methods, the proposed algorithm deterministically calculates the best transformation, with- out requiring good initializations. Further, our algorithm is also much faster than previous techniques in practice. We demonstrate the efficacy of our algorithm on data commonly used in computational conformal geometry.
Coupled Harmonic Bases for Longitudinal Characterization of Brain Networks
Authors: Seong Jae Hwang, Nagesh Adluru, Maxwell D. Collins, Sathya N. Ravi, Barbara B. Bendlin, Sterling C. Johnson, Vikas Singh
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: There is a great deal of interest in using large scale brain imaging studies to understand how brain connectivity evolves over time for an individual and how it varies over different levels/quantiles of cognitive function. To do so, one typically performs so-called tractography procedures on diffusion MR brain images and derives measures of brain connectivity expressed as graphs. The nodes correspond to distinct brain regions and the edges encode the strength of the connection. The scientific interest is in characterizing the evolution of these graphs over time or from healthy individuals to diseased. We pose this important question in terms of the Laplacian of the connectivity graphs derived from various longitudinal or disease time points - quantifying its progression is then expressed in terms of coupling the harmonic bases of a full set of Laplacians. We derive a coupled system of generalized eigenvalue problems (and corresponding numerical optimization schemes) whose solution helps characterize the full life cycle of brain connectivity evolution in a given dataset. Finally, we show a set of results on a diffusion MR imaging dataset of middle aged people at risk for Alzheimer's disease (AD), who are cognitively healthy. In such asymptomatic adults, we find that a framework for characterizing brain connectivity evolution provides the ability to predict cognitive scores for individual subjects, and for estimating the progression of participant's brain connectivity into the future.
Automating Carotid Intima-Media Thickness Video Interpretation With Convolutional Neural Networks
Authors: Jae Shin, Nima Tajbakhsh, R. Todd Hurst, Christopher B. Kendall, Jianming Liang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: Cardiovascular disease (CVD) is the leading cause of mortality yet largely preventable, but the key to prevention is to identify at risk individuals before adverse events. For predicting individual CVD risk, carotid intima-media thickness (CIMT), a noninvasive ultrasound method, has proven to be valuable, offering several advantages over CT coronary artery calcium score. However, each CIMT examination includes several ultrasound videos, and interpreting each of these CIMT videos involves three operations: (1) select three end-diastolic ultrasound frames (EUF) in the video, (2) localize a region of interest (ROI) in each selected frame, and (3) trace the lumen-intima interface and the media-adventitia interface in each ROI to measure CIMT. These operations are tedious, laborious, and time consuming, a serious limitation that hinders the widespread utilization of CIMT in clinical practice. To overcome this limitation, this paper presents a new system to automate CIMT video interpretation. Our extensive experiments demonstrate that the suggested system significantly outperforms the state-of-the-art methods. The superior performance is attributable to our unified framework based on convolutional neural networks (CNNs) coupled with our informative image representation and effective post-processing of the CNN outputs, which are uniquely designed for each of the above three operations.
Context Encoders: Feature Learning by Inpainting
Authors: Deepak Pathak, Philipp Krahenbuhl, Jeff Donahue, Trevor Darrell, Alexei A. Efros
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: We present an unsupervised visual feature learning algorithm driven by context-based pixel prediction. By analogy with auto-encoders, we propose Context Encoders -- a convolutional neural network trained to generate the contents of an arbitrary image region conditioned on its surroundings. In order to succeed at this task, context encoders need to both understand the content of the entire image, as well as produce a plausible hypothesis for the missing part(s). When training context encoders, we have experimented with both a standard pixel-wise reconstruction loss, as well as a reconstruction plus an adversarial loss.  The latter produces much sharper results because it can better handle multiple modes in the output. We found that a context encoder learns a representation that captures not just appearance but also the semantics of visual structures. We quantitatively demonstrate the effectiveness of our learned features for CNN pre-training on classification, detection, and segmentation tasks. Furthermore, context encoders can be used for semantic inpainting tasks, either stand-alone or as initialization for non-parametric methods.
Comparative Deep Learning of Hybrid Representations for Image Recommendations
Authors: Chenyi Lei, Dong Liu, Weiping Li, Zheng-Jun Zha, Houqiang Li
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: In many image-related tasks, learning expressive and discriminative representations of images is essential, and deep learning has been studied for automating the learning of such representations. Some user-centric tasks, such as image recommendations, call for effective representations of not only images but also preferences and intents of users over images. Such representations are termed hybrid and addressed via a deep learning approach in this paper. We design a dual-net deep network, in which the two sub-networks map input images and preferences of users into a same latent semantic space, and then the distances between images and users in the latent space are calculated to make decisions. We further propose a comparative deep learning (CDL) method to train the deep network, using a pair of images compared against one user to learn the pattern of their relative distances. The CDL embraces much more training data than naive deep learning, and thus achieves superior performance than the latter, with no cost of increasing network complexity. Experimental results with real-world data sets for image recommendations have shown the proposed dual-net network and CDL greatly outperform other state-of-the-art image recommendation solutions.
Fast ConvNets Using Group-Wise Brain Damage
Authors: Vadim Lebedev, Victor Lempitsky
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: We revisit the idea of brain damage, i.e. the pruning of the coefficients of a neural network, and suggest how brain damage can be modified and used to speedup convolutional layers in ConvNets. The approach uses the fact that many efficient implementations reduce generalized convolutions to matrix multiplications. The suggested brain damage process prunes the convolutional kernel tensor in a group-wise fashion. After such pruning, convolutions can be reduced to multiplications of thinned dense matrices, which leads to speedup. We investigate different ways to add group-wise prunning to the learning process, and show that several-fold speedups of convolutional layers can be attained using group-sparsity regularizers. Our approach can adjust the shapes of the receptive fields in the convolutional layers, and even prune excessive feature maps from ConvNets, all in a data-driven way.
Learning to Co-Generate Object Proposals With a Deep Structured Network
Authors: Zeeshan Hayder, Xuming He, Mathieu Salzmann
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: Generating object proposals has become a key component of modern object detection pipelines. However, most existing methods generate the object candidates independently of each other. In this paper, we present an approach to co-generating object proposals in multiple images, thus leveraging the collective power of multiple object candidates. In particular, we introduce a deep structured network that jointly predicts the objectness scores and the bounding box locations of multiple object candidates. Our deep structured network consists of a fully-connected Conditional Random Field built on top of a set of deep Convolutional Neural Networks, which learn features to model both the individual object candidate and the similarity between multiple candidates. To train our deep structured network, we develop an end-to-end learning algorithm that, by unrolling the CRF inference procedure, lets us backpropagate the loss gradient throughout the entire structured network. We demonstrate the effectiveness of our approach on two benchmark datasets, showing significant improvement over state-of-the-art object proposal algorithms.
DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks
Authors: Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Pascal Frossard
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: State-of-the-art deep neural networks have achieved impressive results on many image classification tasks. However, these same architectures have been shown to be unstable to small, well sought, perturbations of the images. Despite the importance of this phenomenon, no effective methods have been proposed to accurately compute the robustness of state-of-the-art deep classifiers to such perturbations on large-scale datasets. In this paper, we fill this gap and propose the DeepFool algorithm to efficiently compute perturbations that fool deep networks, and thus reliably quantify the robustness of these classifiers. Extensive experimental results show that our approach outperforms recent methods in the task of computing adversarial perturbations and making classifiers more robust.
Blockout: Dynamic Model Selection for Hierarchical Deep Networks
Authors: Calvin Murdock, Zhen Li, Howard Zhou, Tom Duerig
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: Most deep architectures for image classification--even those that are trained to classify a large number of diverse categories--learn shared image representations with a single model. Intuitively, however, categories that are more similar should share more information than those that are very different. While hierarchical deep networks address this problem by learning separate features for subsets of related categories, current implementations require simplified models using fixed architectures specified via heuristic clustering methods. Instead, we propose Blockout, a method for regularization and model selection that simultaneously learns both the model architecture and parameters. A generalization of Dropout, our approach gives a novel parametrization of hierarchical architectures that allows for structure learning via back-propagation. To demonstrate its utility, we evaluate Blockout on the CIFAR and ImageNet datasets, demonstrating improved classification accuracy, better regularization performance, faster training, and the clear emergence of hierarchical network structures.
FireCaffe: Near-Linear Acceleration of Deep Neural Network Training on Compute Clusters
Authors: Forrest N. Iandola, Matthew W. Moskewicz, Khalid Ashraf, Kurt Keutzer
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: Long training times for high-accuracy deep neural networks (DNNs) impede research into new DNN architectures and slow the development of high-accuracy DNNs. In this paper we present FireCaffe, which successfully scales deep neural network training across a cluster of GPUs. We also present a number of best practices to aid in comparing advancements in methods for scaling and accelerating the training of deep neural networks. The speed and scalability of distributed algorithms is almost always limited by the overhead of communicating between servers; DNN training is not an exception to this rule. Therefore, the key consideration here is to reduce communication overhead wherever possible, while not degrading the accuracy of the DNN models that we train. Our approach has three key pillars. First, we select network hardware that achieves high bandwidth between GPU servers -- Infiniband or Cray interconnects are ideal for this. Second, we consider a number of communication algorithms, and we find that reduction trees are more efficient and scalable than the traditional parameter server approach. Third, we optionally increase the batch size to reduce the total quantity of communication during DNN training, and we identify hyperparameters that allow us to reproduce the small-batch accuracy while training with large batch sizes. When training GoogLeNet and Network-in-Network on ImageNet, we achieve a 47x and 39x speedup, respectively, when training on a cluster of 128 GPUs.
MDL-CW: A Multimodal Deep Learning Framework With Cross Weights
Authors: Sarah Rastegar, Mahdieh Soleymani, Hamid R. Rabiee, Seyed Mohsen Shojaee
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: Deep learning has received much attention as of the most powerful approaches for multimodal representation learning in recent years. An ideal model for multimodal data can reason about missing modalities using the available ones, and usually provides more information when multiple modalities are being considered. All the previous deep models contain separate modality-specific networks and find a shared representation on top of those networks. Therefore, they only consider high level interactions between modalities to find a joint representation for them. In this paper, we propose a multimodal deep learning framework (MDL-CW) that exploits the cross weights between representation of modalities, and try to gradually learn interactions of the modalities in a deep network manner (from low to high level interactions). Moreover, we theoretically show that considering these interactions provide more intra-modality information, and introduce a multi-stage pre-training method that is based on the properties of multi-modal data. In the proposed framework, as opposed to the existing deep methods for multi-modal data, we try to reconstruct the representation of each modality at a given level, with representation of other modalities in the previous layer. Extensive experimental results show that the proposed model outperforms state-of-the-art information retrieval methods for both image and text queries on the PASCAL-sentence and SUN-Attribute databases.
Structured Receptive Fields in CNNs
Authors: Jorn-Henrik Jacobsen, Jan van Gemert, Zhongyu Lou, Arnold W. M. Smeulders
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: Learning powerful feature representations with CNNs is hard when training data are limited. Pre-training is one way to overcome this, but it requires large datasets sufficiently similar to the target domain. Another option is to design priors into the model, which can range from tuned hyperparameters to fully engineered representations like Scattering Networks. We combine these ideas into structured receptive field networks, a model which has a fixed filter basis and yet retains the flexibility of CNNs. This flexibility is achieved by expressing receptive fields in CNNs as a weighted sum over a fixed basis which is similar in spirit to Scattering Networks. The key difference is that we learn arbitrary effective filter sets from the basis rather than modeling the filters. This approach explicitly connects classical multiscale image analysis with general CNNs. With structured receptive field networks, we improve considerably over unstructured CNNs for small and medium dataset scenarios as well as over Scattering for large datasets. We validate our findings on ILSVRC2012, Cifar-10, Cifar-100 and MNIST. As a realistic small dataset example, we show state-of-the-art classification results on popular 3D MRI brain-disease datasets where pre-training is difficult due to a lack of large public datasets in a similar domain.
First Person Action Recognition Using Deep Learned Descriptors
Authors: Suriya Singh, Chetan Arora, C. V. Jawahar
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: We focus on the problem of wearer's action recognition in first person a.k.a. egocentric videos. This problem is more challenging than third person activity recognition due to unavailability of wearer's pose and sharp movements in the videos caused by the natural head motion of the wearer. Carefully crafted features based on hands and objects cues for the problem have been shown to be successful for limited targeted datasets. We propose convolutional neural networks (CNNs) for end to end learning and classification of wearer's actions. The proposed network makes use of egocentric cues by capturing hand pose, head motion and saliency map. It is compact. It can also be trained from relatively small number of labeled egocentric videos that are available. We show that the proposed network can generalize and give state of the art performance on various disparate egocentric action datasets.
Recognizing Micro-Actions and Reactions From Paired Egocentric Videos
Authors: Ryo Yonetani, Kris M. Kitani, Yoichi Sato
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: We aim to understand the dynamics of social interactions between two people by recognizing their actions and reactions using a head-mounted camera. Our work will impact several first-person vision tasks that need the detailed understanding of social interactions, such as automatic video summarization of group events and assistive systems. To recognize micro-level actions and reactions, such as slight shifts in attention, subtle nodding, or small hand actions, where only subtle body motion is apparent, we propose to use paired egocentric videos recorded by two interacting people. We show that the first-person and second-person points-of-view features of two people, enabled by paired egocentric videos, are complementary and essential for reliably recognizing micro-actions and reactions. We also build a new dataset of dyadic (two-persons) interactions that comprises more than 1000 pairs of egocentric videos to enable systematic evaluations on the task of micro-action and reaction recognition.
Mining 3D Key-Pose-Motifs for Action Recognition
Authors: Chunyu Wang, Yizhou Wang, Alan L. Yuille
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: Recognizing an action from a sequence of 3D skeletal poses is a challenging task. First, different actors may perform the same action in various performing styles. Second, the estimated poses are sometimes inaccurate due to sensory noises. These challenges can cause large variations between instances of the same class. Third, the datasets are usually small, with only a few actors performing few repetitions of each action. Hence training complex classifiers risks over-fitting the data. We address this task by mining a set of key-pose-motifs for each action class. A key-pose-motif contains a set of ordered  poses or  action units(a short sequence of poses), which are required to be close but not necessarily adjacent in the action sequences. The representation is robust to style variations and outlier poses. The key-pose-motifs are represented in terms of a dictionary using soft-quantization (probabilities) to deal with inaccuracies caused by quantization. We propose an efficient algorithm to mine key-pose-motifs taking into account these probabilities. We classify a sequence by matching it to the motifs of each class and select the class that maximizes the matching score. This simple classifier obtains state-of-the-art performance on two benchmark datasets and outperforms a deep network approach.
Predicting the Where and What of Actors and Actions Through Online Action Localization
Authors: Khurram Soomro, Haroon Idrees, Mubarak Shah
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: This paper proposes a novel approach to tackle the challenging problem of 'online action localization' which entails predicting actions and their locations as they happen in a video. Typically, action localization or recognition is performed in an offline manner where all the frames in the video are processed together and action labels are not predicted for the future. This dis-allows timely localization of actions - an important consideration for surveillance tasks. In our approach, given a batch of frames from the immediate past in a video, we estimate pose and over- segment the current frame into superpixels. Next, we discriminatively train an actor foreground model on the superpixels using the pose bounding boxes. A Conditional Random Field with superpixels as nodes, and edges connecting spatio-temporal neighbors is used to obtain action segments. The action confidence is predicted using dynamic programming on SVM scores obtained on short segments of the video, thereby capturing sequential information of the actions. The issue of visual drift is handled by updating the appearance model and pose refinement in an online manner. Lastly, we introduce a new measure to quantify the performance of action prediction (i.e. online action localization), which analyzes how the prediction accuracy varies as a function of observed portion of the video. Our experiments suggest that despite using only a few frames to localize actions at each time instant, we are able to predict the action and obtain competitive results to state-of-the-art offline methods.
Actions ~ Transformations
Authors: Xiaolong Wang, Ali Farhadi, Abhinav Gupta
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: What defines an action like "kicking ball"? We argue that the true meaning of an action lies in the change or transformation an action brings to the environment. In this paper, we propose a novel representation for actions by modeling an action as a transformation which changes the state of the environment before the action happens (precondition) to the state after the action (effect). Motivated by recent advancements of video representation using deep learning, we design a Siamese network which models the action as a transformation on a high-level feature space. We show that our model gives improvements on standard action recognition datasets including UCF101 and HMDB51. More importantly, our approach is able to  generalize beyond learned action categories and shows significant performance improvement on cross-category generalization on our new ACT dataset.
Visual Path Prediction in Complex Scenes With Crowded Moving Objects
Authors: YoungJoon Yoo, Kimin Yun, Sangdoo Yun, JongHee Hong, Hawook Jeong, Jin Young Choi
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: This paper proposes a novel path prediction algorithm for progressing one step further than the existing works focusing on single target path prediction. In this paper, we consider moving dynamics of co-occurring objects for path prediction in a scene that includes crowded moving objects. To solve this problem, we first suggest a two-layered probabilistic model to find major movement patterns and their co-occurrence tendency. By utilizing the unsupervised learning results from the model, we present an algorithm to find the future location of any target object. Through extensive qualitative/quantitative experiments, we show that our algorithm can find a plausible future path in complex scenes with a large number of moving objects.
End-To-End Learning of Action Detection From Frame Glimpses in Videos
Authors: Serena Yeung, Olga Russakovsky, Greg Mori, Li Fei-Fei
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: In this work we introduce a fully end-to-end approach for action detection in videos that learns to directly predict the temporal bounds of actions. Our intuition is that the process of detecting actions is naturally one of observation and refinement: observing moments in video, and refining hypotheses about when an action is occurring. Based on this insight, we formulate our model as a recurrent neural network-based agent that interacts with a video over time. The agent observes video frames and decides both where to look next and whether to emit a prediction. Since backpropagation is not adequate in this non-differentiable setting, we use REINFORCE to learn the agent's task-specific decision policy. Our model achieves state-of-the-art results on the THUMOS'14 and ActivityNet datasets while observing only a fraction (2% or less) of the video frames.
Action Recognition in Video Using Sparse Coding and Relative Features
Authors: Anali Alfaro, Domingo Mery, Alvaro Soto
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: This work presents an approach to category-based action recognition in video using sparse coding techniques. The proposed approach includes two main contributions: i) A new method to handle intra-class variations by decomposing each video into a reduced set of representative atomic action acts or key-sequences, and ii) A new video descriptor, ITRA: Inter-Temporal Relational Act Descriptor, that exploits the power of comparative reasoning to capture relative similarity relations among key-sequences. In terms of the method to obtain key-sequences, we introduce a loss function that, for each video, leads to the identification of a sparse set of representative key-frames capturing both, relevant particularities arising in the input video, as well as relevant generalities arising in the complete class collection. In terms of the method to obtain the ITRA descriptor, we introduce a novel scheme to quantify relative intra and inter-class similarities among local temporal patterns arising in the videos. The resulting ITRA descriptor demonstrates to be highly effective to discriminate among action categories. As a result, the proposed approach reaches remarkable action recognition performance on several popular benchmark datasets, outperforming alternative state-of-the-art techniques by a  large margin.
Improving Human Action Recognition by Non-Action Classification
Authors: Yang Wang, Minh Hoai
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: In this paper we consider the task of recognizing human actions in realistic video where human actions are dominated by irrelevant factors. We first study the benefits of removing non-action video segments, which are the ones that do not portray any human action. We then learn a non-action classifier and use it to down-weight irrelevant video segments. The non-action classifier is trained using ActionThread, a dataset with shot-level annotation for the occurrence or absence of a human action. The non-action classifier can be used to identify non-action shots with high precision and subsequently used to improve the performance of action recognition systems.
Actionness Estimation Using Hybrid Fully Convolutional Networks
Authors: Limin Wang, Yu Qiao, Xiaoou Tang, Luc Van Gool
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: Actionness was introduced to quantify the likelihood of containing a generic action instance at a specific location. Accurate and efficient estimation of actionness is important in video analysis and may benefit other relevant tasks such as action recognition and action detection. This paper presents a new deep architecture for actionness estimation, called hybrid fully convolutional network (H-FCN), which is composed of appearance FCN (A-FCN) and motion FCN (M-FCN). These two FCNs leverage the strong capacity of deep models to estimate actionness maps from the perspectives of static appearance and dynamic motion, respectively. In addition, the fully convolutional nature of H-FCN allows it to efficiently process videos with arbitrary sizes. Experiments are conducted on the challenging datasets of Stanford40, UCF Sports, and JHMDB to verify the effectiveness of H-FCN on actionness estimation, which demonstrate that our method achieves superior performance to previous ones. Moreover, we apply the estimated actionness maps on action proposal generation and action detection. Our actionness maps advance the current state-of-the-art performance of these tasks substantially.
Real-Time Action Recognition With Enhanced Motion Vector CNNs
Authors: Bowen Zhang, Limin Wang, Zhe Wang, Yu Qiao, Hanli Wang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: The deep two-stream architecture exhibited excellent performance on video based action recognition. The most computationally expensive step in this approach comes from the calculation of optical flow which prevents it to be real-time. This paper accelerates this architecture by replacing optical flow with motion vector which can be obtained directly from compressed videos without extra calculation. However, motion vector lacks fine structures, and contains noisy and inaccurate motion patterns, leading to the evident degradation of recognition performance. Our key insight for relieving this problem is that optical flow and motion vector are inherent correlated. Transferring the knowledge learned with optical flow CNN to motion vector CNN can significantly boost the performance of the latter. Specifically, we introduce three strategies for this, initialization transfer, supervision transfer and their combination. Experimental results show that our method achieves comparable recognition performance to the state-of-the-art, while our method can process 390.7 frames per second, which is 27 times faster than the original two-stream method.
Laplacian Patch-Based Image Synthesis
Authors: Joo Ho Lee, Inchang Choi, Min H. Kim
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: Patch-based image synthesis has been enriched with global optimization on the image pyramid. Successively, the gradient-based synthesis has improved structural coherence and details. However, the gradient operator is directional and inconsistent and requires computing multiple operators. It also introduces a significantly heavy computational burden to solve the Poisson equation that often accompanies artifacts in non-integrable gradient fields. In this paper, we propose a patch-based synthesis using a Laplacian pyramid to improve searching correspondence with enhanced awareness of edge structures. Contrary to the gradient operators, the Laplacian pyramid has the advantage of being isotropic in detecting changes to provide more consistent performance in decomposing the base structure and the detailed localization. Furthermore, it does not require heavy computation as it employs approximation by the differences of Gaussians. We examine the potentials of the Laplacian pyramid for enhanced edge-aware correspondence search. We demonstrate the effectiveness of the Laplacian-based approach over the state-of-the-art patch-based image synthesis methods.
Rain Streak Removal Using Layer Priors
Authors: Yu Li, Robby T. Tan, Xiaojie Guo, Jiangbo Lu, Michael S. Brown
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: This paper addresses the problem of rain streak removal from a single image. Rain streaks impair visibility of an image and introduce undesirable interference that can severely affect the performance of computer vision algorithms.   Rain streak removal can be formulated as a layer decomposition problem, with a rain streak layer superimposed on a background layer containing the true scene content. Existing decomposition methods that address this problem employ either dictionary learning methods or impose a low rank structure on the appearance of the rain streaks.   While these methods can improve the overall visibility, they tend to leave too many rain streaks in the background image or over-smooth the background image.   In this paper, we propose an effective method that uses simple patch-based priors for both the background and rain layers.  These priors are based on Gaussian mixture models and can accommodate multiple orientations and scales of the rain streaks. This simple approach removes rain streaks better than the existing methods qualitatively and quantitatively.  We overview our method and demonstrate its effectiveness over prior work on a number of examples.
Gradient-Domain Image Reconstruction Framework With Intensity-Range and Base-Structure Constraints
Authors: Takashi Shibata, Masayuki Tanaka, Masatoshi Okutomi
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: This paper presents a novel unified gradient-domain image reconstruction framework with intensity-range constraint and base-structure constraint. The existing method for manipulating base structures and detailed textures are classifiable into two major approaches: i) gradient-domain and ii) layer-decomposition. To generate detail-preserving and artifact-free output images, we combine the benefits of the two approaches into the proposed framework by introducing the intensity-range constraint and the base-structure constraint. To preserve details of the input image, the proposed method takes advantage of reconstructing the output image in the gradient domain, while the output intensity is guaranteed to lie within the specified intensity range, e.g. 0-to-255, by the intensity-range constraint. In addition, the reconstructed image lies close to the base structure by the base-structure constraint, which is effective for restraining artifacts. Experimental results show that the proposed framework is effective for various applications such as tone mapping, seamless image cloning, detail enhancement, and image restoration.
Removing Clouds and Recovering Ground Observations in Satellite Image Sequences via Temporally Contiguous Robust Matrix Completion
Authors: Jialei Wang, Peder A. Olsen, Andrew R. Conn, Aurelie C. Lozano
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: We consider the problem of removing and replacing clouds in satellite image sequences, which has a wide range of applications in remote sensing. Our approach first detects and removes the cloud-contaminated part of the image sequences, then recovers the missing scenes from the clean parts by the proposed "TECROMAC" (TEmporally Contiguous RObust MAtrix Completion) objective. The objective function balances temporal smoothness with a low rank solution while staying close to the original observations. The matrix where the rows are pixels and columns are the days of the image has low-rank because the pixels reflect land-types such as vegetation, roads and lakes and there are relatively few of these. We provide efficient optimization algorithms for TECROMAC, so we can run on images containing millions of pixels. Empirical results on real satellite image sequences as well as simulated data demonstrate that our approach is able to recover underlying images from heavily cloud-contaminated observations.
D3: Deep Dual-Domain Based Fast Restoration of JPEG-Compressed Images
Authors: Zhangyang Wang, Ding Liu, Shiyu Chang, Qing Ling, Yingzhen Yang, Thomas S. Huang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: In this paper, we design a Deep Dual-Domain (D3) based fast restoration model to remove artifacts of JPEG compressed images. It leverages the large learning capacity of deep networks, as well as the problem-specific expertise that was hardly incorporated in the past design of deep architectures. For the latter, we take into consideration both the prior knowledge of the JPEG compression scheme, and the successful practice of the sparsity-based dual-domain approach. We further design the One-Step Sparse Inference (1-SI) module, as an efficient and light-weighted feed-forward approximation of sparse coding. Extensive experiments verify the superiority of the proposed D3 model over several state-of-the-art methods. Specifically, our best model is capable of outperforming the latest deep model for around 1 dB in PSNR, and is 30 times faster.
From Bows to Arrows: Rolling Shutter Rectification of Urban Scenes
Authors: Vijay Rengarajan, Ambasamudram N. Rajagopalan, Rangarajan Aravind
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: The rule of perspectivity that 'straight-lines-must-remain-straight' is easily inflected in CMOS cameras by distortions introduced by motion. Lines can be rendered as curves due to the row-wise exposure mechanism known as rolling shutter (RS). We solve the problem of correcting distortions arising from handheld cameras due to RS effect from a single image free from motion blur with special relevance to urban scenes. We develop a procedure to extract prominent curves from the RS image since this is essential for deciphering the varying row-wise motion. We pose an optimization problem with line desirability costs based on straightness, angle, and length, to resolve the geometric ambiguities while estimating the camera motion based on a rotation-only model assuming known camera intrinsic matrix. Finally, we rectify the RS image based on the estimated camera trajectory using inverse mapping. We show rectification results for RS images captured using mobile phone cameras. We also compare our single image method against existing video and nonblind RS rectification methods that typically require multiple images.
A Weighted Variational Model for Simultaneous Reflectance and Illumination Estimation
Authors: Xueyang Fu, Delu Zeng, Yue Huang, Xiao-Ping Zhang, Xinghao Ding
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: We propose a weighted variational model to estimate both the reflectance and the illumination from an observed image. We show that, though it is widely adopted for ease of modeling, the log-transformed image for this task is not ideal. Based on the previous investigation of the logarithmic transformation, a new weighted variational model is proposed for better prior representation, which is imposed in the regularization terms. Different from conventional variational models, the proposed model can preserve the estimated reflectance with more details. Moreover, the proposed model can suppress noise to some extent. An alternating minimization scheme is adopted to solve the proposed model. Experimental results demonstrate the effectiveness of the proposed model with its algorithm. Compared with other variational methods, the proposed method yields comparable or better results on both subjective and objective assessments.
Visualizing and Understanding Deep Texture Representations
Authors: Tsung-Yu Lin, Subhransu Maji
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: A number of recent approaches have used deep convolutional neural networks (CNNs) to build texture representations. Nevertheless, it is still unclear how these mod- els represent texture and invariances to categorical variations. This work conducts a systematic evaluation of recent CNN-based texture descriptors for recognition and attempts to understand the nature of invariances captured by these representations. First we show that the recently proposed bilinear CNN model [25] is an excellent generalpurpose texture descriptor and compares favorably to other CNN-based descriptors on various texture and scene recognition benchmarks. The model is translationally invariant and obtains better accuracy on the ImageNet dataset without requiring spatial jittering of data compared to corresponding models trained with spatial jittering. Based on recent work [13, 28] we propose a technique to visualize pre-images, providing a means for understanding categorical properties that are captured by these representations. Finally, we show preliminary results on how a unified parametric model of texture analysis and synthesis can be used for attribute-based image manipulation, e.g. to make an image more swirly, honeycombed, or knitted. The source code and additional visualizations are available at http://vis-www.cs.umass.edu/texture.