CVPR 2019 论文列表

Example-guided image synthesis aims to synthesize an image from a semantic label map and an exemplary image indicating style. We use the term "style" in this problem to refer to implicit characteristics of images, for example: in portraits "style" includes gender, racial identity, age, hairstyle; in full body pictures it includes clothing; in street scenes it refers to weather and time of day and such like. A semantic label map in these cases indicates facial expression, full body pose, or scene segmentation. We propose a solution to the example-guided image synthesis problem using conditional generative adversarial networks with style consistency. Our key contributions are (i) a novel style consistency discriminator to determine whether a pair of images are consistent in style; (ii) an adaptive semantic consistency loss; and (iii) a training data sampling strategy, for synthesizing style-consistent results to the exemplar. We demonstrate the efficiency of our method on face, dance and street view synthesis tasks.

In this work, we study the robustness of a CNN+RNN based image captioning system being subjected to adversarial noises. We propose to fool an image captioning system to generate some targeted partial captions for an image polluted by adversarial noises, even the targeted captions are totally irrelevant to the image content. A partial caption indicates that the words at some locations in this caption are observed, while words at other locations are not restricted. It is the first work to study exact adversarial attacks of targeted partial captions. Due to the sequential dependencies among words in a caption, we formulate the generation of adversarial noises for targeted partial captions as a structured output learning problem with latent variables. Both the generalized expectation maximization algorithm and structural SVMs with latent variables are then adopted to optimize the problem. The proposed methods generate very successful attacks to three popular CNN+RNN based image captioning models. Furthermore, the proposed attack methods are used to understand the inner mechanism of image captioning systems, providing the guidance to further improve automatic image captioning systems towards human captioning.

Event cameras are novel sensors that report brightness changes in the form of asynchronous "events" instead of intensity frames. They have significant advantages over conventional cameras: high temporal resolution, high dynamic range, and no motion blur. Since the output of event cameras is fundamentally different from conventional cameras, it is commonly accepted that they require the development of specialized algorithms to accommodate the particular nature of events. In this work, we take a different view and propose to apply existing, mature computer vision techniques to videos reconstructed from event data. We propose a novel, recurrent neural network to reconstruct videos from a stream of events and train it on a large amount of simulated event data. Our experiments show that our approach surpasses state-of-the-art reconstruction methods by a large margin (> 20%) in terms of image quality. We further apply off-the-shelf computer vision algorithms to videos reconstructed from event data on tasks such as object classification and visual-inertial odometry, and show that this strategy consistently outperforms algorithms that were specifically designed for event data. We believe that our approach opens the door to bringing the outstanding properties of event cameras to an entirely new range of tasks.

Event cameras are bio-inspired vision sensors that mimic retinas to asynchronously report per-pixel intensity changes rather than outputting an actual intensity image at regular intervals. This new paradigm of image sensor offers significant potential advantages; namely, sparse and non-redundant data representation. Unfortunately, however, most of the existing artificial neural network architectures, such as a CNN, require dense synchronous input data, and therefore, cannot make use of the sparseness of the data. We propose EventNet, a neural network designed for real-time processing of asynchronous event streams in a recursive and event-wise manner. EventNet models dependence of the output on tens of thousands of causal events recursively using a novel temporal coding scheme. As a result, at inference time, our network operates in an event-wise manner that is realized with very few sum-of-the-product operations---look-up table and temporal feature aggregation---which enables processing of 1 mega or more events per second on standard CPU. In experiments using real data, we demonstrated the real-time performance and robustness of our framework.

Event cameras have a lot of advantages over traditional cameras, such as low latency, high temporal resolution, and high dynamic range. However, since the outputs of event cameras are the sequences of asynchronous events over time rather than actual intensity images, existing algorithms could not be directly applied. Therefore, it is demanding to generate intensity images from events for other tasks. In this paper, we unlock the potential of event camera-based conditional generative adversarial networks to create images/videos from an adjustable portion of the event data stream. The stacks of space-time coordinates of events are used as inputs and the network is trained to reproduce images based on the spatio-temporal intensity changes. The usefulness of event cameras to generate high dynamic range (HDR) images even in extreme illumination conditions and also non blurred images under rapid motion is also shown. In addition, the possibility of generating very high frame rate videos is demonstrated, theoretically up to 1 million frames per second(FPS) since the temporal resolution of event cameras is about 1 microsecond. Proposed methods are evaluated by comparing the results with the intensity images captured on the same pixel grid-line of events using online available real datasets and synthetic datasets produced by the event camera simulator.

Event cameras asynchronously report timestamped changes in pixel intensity and offer advantages over conventional raster scan cameras in terms of low-latency, low redundancy sensing and high dynamic range. In recent years, much of research in event based vision has been focused on performing tasks such as optic flow estimation, moving object segmentation, feature tracking, camera rotation estimation and more, through contrast maximization. In contrast maximization, events are warped along motion trajectories whose parameters depend on the quantity being estimated, to some time t_ref. The parameters are then scored by some reward function of the accumulated events at t_ref. The versatility of this approach has lead to a flurry of research in recent years, but no in-depth study of the reward chosen during optimization has yet been made. In this work we examine the choice of reward used in contrast maximization, propose a classification of different rewards and show how a reward can be constructed that is more robust to noise and aperture uncertainty. We validate our work experimentally by predicting optical flow and comparing to ground-truth data.

Deep neural networks are vulnerable to adversarial examples, which can mislead classifiers by adding imperceptible perturbations. An intriguing property of adversarial examples is their good transferability, making black-box attacks feasible in real-world applications. Due to the threat of adversarial attacks, many methods have been proposed to improve the robustness. Several state-of-the-art defenses are shown to be robust against transferable adversarial examples. In this paper, we propose a translation-invariant attack method to generate more transferable adversarial examples against the defense models. By optimizing a perturbation over an ensemble of translated images, the generated adversarial example is less sensitive to the white-box model being attacked and has better transferability. To improve the efficiency of attacks, we further show that our method can be implemented by convolving the gradient at the untranslated image with a pre-defined kernel. Our method is generally applicable to any gradient-based attack method. Extensive experiments on the ImageNet dataset validate the effectiveness of the proposed method. Our best attack fools eight state-of-the-art defenses at an 82% success rate on average based only on the transferability, demonstrating the insecurity of the current defense techniques.

In this paper, we introduce a method to automatically reconstruct the 3D motion of a person interacting with an object from a single RGB video. Our method estimates the 3D poses of the person and the object, contact positions, and forces and torques actuated by the human limbs. The main contributions of this work are three-fold. First, we introduce an approach to jointly estimate the motion and the actuation forces of the person on the manipulated object by modeling contacts and the dynamics of their interactions. This is cast as a large-scale trajectory optimization problem. Second, we develop a method to automatically recognize from the input video the position and timing of contacts between the person and the object or the ground, thereby significantly simplifying the complexity of the optimization. Third, we validate our approach on a recent MoCap dataset with ground truth contact forces and demonstrate its performance on a new dataset of Internet videos showing people manipulating a variety of tools in unconstrained environments.

This paper is about learning deep representations of images such that images belonging to the same class have more similar representations than those belonging to different classes. For this goal, prior work typically uses the triplet or N-pair loss, specified in terms of either l2-distances or dot-products between deep features. However, such formulations seem poorly suited to the highly non-Euclidean deep feature space. Our first contribution is in specifying the N-pair loss in terms of manifold similarities between deep features. We introduce a new time- and memory-efficient method for estimating the manifold similarities by using a closed-form convergence solution of the Random Walk algorithm. Our efficiency comes, in part, from following the recent work that randomly partitions the deep feature space, and expresses image distances via representatives of the resulting subspaces, a.k.a. proxies. Our second contribution is aimed at reducing overfitting by estimating hard proxies that are as close to one another as possible, but remain in their respective subspaces. Our evaluation demonstrates that we outperform the state of the art in both image retrieval and clustering on the benchmark CUB-200-2011, Cars196, and Stanford Online Products datasets.

Learning class-conditional data distributions is crucial for Generative Adversarial Networks (GAN) in semi-supervised learning. To improve both instance synthesis and classification in this setting, we propose an enhanced TripleGAN (EnhancedTGAN) model in this work. We follow the adversarial training scheme of the original TripleGAN, but completely re-design the training targets of the generator and classifier. Specifically, we adopt feature-semantics matching to enhance the generator in learning class-conditional distributions from both the aspects of statistics in the latent space and semantics consistency with respect to the generator and classifier. Since a limited amount of labeled data is not sufficient to determine satisfactory decision boundaries, we include two classifiers, and incorporate collaborative learning into our model to provide better guidance for generator training. The synthesized high-fidelity data can in turn be used for improving classifier training. In the experiments, the superior performance of our approach on multiple benchmark datasets demonstrates the effectiveness of the mutual reinforcement between the generator and classifiers in facilitating semi-supervised instance synthesis and classification.

Defocus blur detection (DBD) is a fundamental yet challenging topic, since the homogeneous region is obscure and the transition from the focused area to the unfocused region is gradual. Recent DBD methods make progress through exploring deeper or wider networks with the expense of high memory and computation. In this paper, we propose a novel learning strategy by breaking DBD problem into multiple smaller defocus blur detectors and thus estimate errors can cancel out each other. Our focus is the diversity enhancement via cross-ensemble network. Specifically, we design an end-to-end network composed of two logical parts: feature extractor network (FENet) and defocus blur detector cross-ensemble network (DBD-CENet). FENet is constructed to extract low-level features. Then the features are fed into DBD-CENet containing two parallel-branches for learning two groups of defocus blur detectors. For each individual, we design cross-negative and self-negative correlations and an error function to enhance ensemble diversity and balance individual accuracy. Finally, the multiple defocus blur detectors are combined with a uniformly weighted average to obtain the final DBD map. Experimental results indicate the superiority of our method in terms of accuracy and speed when compared with several state-of-the-art methods.

In this paper, we reduce the image dehazing problem to an image-to-image translation problem, and propose Enhanced Pix2pix Dehazing Network (EPDN), which generates a haze-free image without relying on the physical scattering model. EPDN is embedded by a generative adversarial network, which is followed by a well-designed enhancer. Inspired by visual perception global-first theory, the discriminator guides the generator to create a pseudo realistic image on a coarse scale, while the enhancer following the generator is required to produce a realistic dehazing image on the fine scale. The enhancer contains two enhancing blocks based on the receptive field model, which reinforces the dehazing effect in both color and details. The embedded GAN is jointly trained with the enhancer. Extensive experiment results on synthetic datasets and real-world datasets show that the proposed EPDN is superior to the state-of-the-art methods in terms of PSNR, SSIM, PI, and subjective visual effect.

In this paper, we propose an Enhanced Bayesian Compression method to flexibly compress the deep networks via reinforcement learning. Unlike the existing Bayesian compression method which cannot explicitly enforce quantization weights during training, our method learns flexible codebooks in each layer for an optimal network quantization. To dynamically adjust the state of codebooks, we employ an Actor-Critic network to collaborate with the original deep network. Different from most existing network quantization methods, our EBC does not require re-training procedures after the quantization. Experimental results show that our method obtains low-bit precision with acceptable accuracy drop on MNIST, CIFAR and ImageNet.

Standard image captioning tasks such as COCO and Flickr30k are factual, neutral in tone and (to a human) state the obvious (e.g., "a man playing a guitar"). While such tasks are useful to verify that a machine understands the content of an image, they are not engaging to humans as captions. With this in mind we define a new task, PERSONALITY-CAPTIONS, where the goal is to be as engaging to humans as possible by incorporating controllable style and personality traits. We collect and release a large dataset of 241,858 of such captions conditioned over 215 possible traits. We build models that combine existing work from (i) sentence representations [36] with Transformers trained on 1.7 billion dialogue examples; and (ii) image representations [32] with ResNets trained on 3.5 billion social media images. We obtain state-of-the-art performance on Flickr30k and COCO, and strong performance on our new task. Finally, online evaluations validate that our task and models are engaging to humans, with our best model close to human performance.

Time-lapse videos usually contain visually appealing content but are often difficult and costly to create. In this paper, we present an end-to-end solution to synthesize a time-lapse video from a single outdoor image using deep neural networks. Our key idea is to train a conditional generative adversarial network based on existing datasets of time-lapse videos and image sequences. We propose a multi-frame joint conditional generation framework to effectively learn the correlation between the illumination change of an outdoor scene and the time of the day. We further present a multi-domain training scheme for robust training of our generative models from two datasets with different distributions and missing timestamp labels. Compared to alternative time-lapse video synthesis algorithms, our method uses the timestamp as the control variable and does not require a reference video to guide the synthesis of the final output. We conduct ablation studies to validate our algorithm and compare with state-of-the-art techniques both qualitatively and quantitatively.

Product Quantization, a dictionary based hashing method, is one of the leading unsupervised hashing techniques. While it ignores the labels, it harnesses the features to construct look up tables that can approximate the feature space. In recent years, several works have achieved state of the art results on hashing benchmarks by learning binary representations in a supervised manner. This work presents Deep Product Quantization (DPQ), a technique that leads to more accurate retrieval and classification than the latest state of the art methods, while having similar computational complexity and memory footprint as the Product Quantization method. To our knowledge, this is the first work to introduce a dictionary-based representation that is inspired by Product Quantization and which is learned end-to-end, and thus benefits from the supervised signal. DPQ explicitly learns soft and hard representations to enable an efficient and accurate asymmetric search, by using a straight-through estimator. Our method obtains state of the art results on an extensive array of retrieval and classification experiments.

Projector photometric compensation aims to modify a projector input image such that it can compensate for disturbance from the appearance of projection surface. In this paper, for the first time, we formulate the compensation problem as an end-to-end learning problem and propose a convolutional neural network, named CompenNet, to implicitly learn the complex compensation function. CompenNet consists of a UNet-like backbone network and an autoencoder subnet. Such architecture encourages rich multi-level interactions between the camera-captured projection surface image and the input image, and thus captures both photometric and environment information of the projection surface. In addition, the visual details and interaction information are carried to deeper layers along the multi-level skip convolution layers. The architecture is of particular importance for the projector compensation task, for which only a small training dataset is allowed in practice. Another contribution we make is a novel evaluation benchmark, which is independent of system setup and thus quantitatively verifiable. Such benchmark is not previously available, to our best knowledge, due to the fact that conventional evaluation requests the hardware system to actually project the final results. Our key idea, motivated from our end-to-end problem formulation, is to use a reasonable surrogate to avoid such projection process so as to be setup-independent. Our method is evaluated carefully on the benchmark, and the results show that our end-to-end learning solution outperforms state-of-the-arts both qualitatively and quantitatively by a significant margin.

We propose a novel multi-task learning architecture, which allows learning of task-specific feature-level attention. Our design, the Multi-Task Attention Network (MTAN), consists of a single shared network containing a global feature pool, together with a soft-attention module for each task. These modules allow for learning of task-specific features from the global features, whilst simultaneously allowing for features to be shared across different tasks. The architecture can be trained end-to-end and can be built upon any feed-forward neural network, is simple to implement, and is parameter efficient. We evaluate our approach on a variety of datasets, across both image-to-image predictions and image classification tasks. We show that our architecture is state-of-the-art in multi-task learning compared to existing methods, and is also less sensitive to various weighting schemes in the multi-task loss function. Code is available at https://github.com/lorenmt/mtan.

We present an end-to-end learned algorithm for seeded segmentation. Our method is based on the Random Walker algorithm, where we predict the edge weights of the un- derlying graph using a convolutional neural network. This can be interpreted as learning context-dependent diffusiv- ities for a linear diffusion process. After calculating the exact gradient for optimizing these diffusivities, we pro- pose simplifications that sparsely sample the gradient while still maintaining competitive results. The proposed method achieves the currently best results on the seeded CREMI neuron segmentation challenge.