3D hand reconstruction from images is a widely-studied problem in computer vision and graphics, and has a particularly high relevance for virtual and augmented reality. Although several 3D hand reconstruction approaches leverage hand models as a strong prior to resolve ambiguities and achieve more robust results, most existing models account only for the hand shape and poses and do not model the texture. To fill this gap, in this work we present HTML, the first parametric texture model of human hands. Our model spans several dimensions of hand appearance variability (e.g. related to gender, ethnicity, or age) and only requires a commodity camera for data acquisition. Experimentally, we demonstrate that our appearance model can be used to tackle a range of challenging problems such as 3D hand reconstruction from a single monocular image. Furthermore, our appearance model can be used to define a neural rendering layer that enables training with a self-supervised photometric loss. We make our model publicly available.
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This paper proposes new ways of sample mixing by thinking of the process as generation of barycenter in a metric space for data augmentation. First, we present an optimal-transport-based mixup technique to generate Wasserstein barycenter which works well on images with clean background and is empirically shown complementary to existing mixup methods. Then we generalize mixup to an AutoMix technique by using a learnable network to fit barycenter in a cooperative way between the classifier (a.k.a. discriminator) and generator networks. Experimental results on both multi-class and multi-label prediction tasks show the efficacy of our approach, which is also verified in the presence of unseen categories (open set) and noise.
Defocus blur detection (DBD) is a classical low level vision task. It has recently attracted attention focusing on designing complex convolutional neural networks (CNN) which make full use of both low level features and high level semantic information. The heavy networks used in these methods lead to low processing speed, resulting difficulty in applying to real-time applications. In this work, we propose novel perspectives on the DBD problem and design convenient approach to build a real-time cost-effective DBD model. First, we observe that the semantic information does not always relate to and sometimes mislead the blur detection. We start from the essential characteristics of the DBD problem and propose a data augmentation method accordingly to destroy the semantic information and enforce the model to learn image blur related features rather than the semantic features. A novel self-supervision training objective is proposed to enhance the model training consistency and stability. Second, by rethinking the relationship between defocus blur detection and salience detection, we identify two previously ignored but common scenarios, based on which we design a hard mining strategy to enhance the DBD model. By using the proposed techniques, our model that uses a slightly modified U-Net as backbone, improves the processing speed by more than 3 times and performs competitively against state of the art methods. Ablation study is also conducted to verify the effectiveness of each part of our proposed methods.
For safety-critical applications such as autonomous driving, CNNs have to be robust with respect to unavoidable image corruptions, such as image noise. While previous works addressed the task of robust prediction in the context of full-image classification, we consider it for dense semantic segmentation. We build upon an insight from image classification that output robustness can be improved by increasing the network-bias towards object shapes. We present a new training schema that increases this shape bias. Our basic idea is to alpha-blend a portion of the RGB training images with faked images, where each class-label is given a fixed, randomly chosen color that is not likely to appear in real imagery. This forces the network to rely more strongly on shape cues. We call this data augmentation technique ""Painting-by-Numbers''. We demonstrate the effectiveness of our training schema for DeepLabv3$+$ with various network backbones, MobileNet-V2, ResNets, and Xception, and evaluate it on the Cityscapes dataset. With respect to our 16 different types of image corruptions and 5 different network backbones, we are in 74% better than training with clean data. For cases where we are worse than a model trained without our training schema, it is mostly only marginally worse. However, for some image corruptions such as images with noise, we see a considerable performance gain of up to 25%.
Data augmentation is one of the most important tools in training modern deep neural networks. Recently, great advances have been made in searching for optimal augmentation policies in the image classification domain. However, two key points related to data augmentation remain uncovered by the current methods. First is that most if not all modern augmentation search methods are extit{offline} where learning policies are isolated from their usage. The learned policies are mostly constant throughout the training process and are extit{not adapted} to the current training model state. Second, the policies rely on class-preserving image processing functions. Hence applying current offline methods to new tasks may require domain knowledge to specify such kind of operations. In this work, we offer an orthogonal extit{online} data augmentation scheme together with three new augmentation networks, co-trained with the target learning task. It is both more efficient, in the sense that it does not require expensive offline training when entering a new domain, and more adaptive as it adapts to the learner state. Our augmentation networks require less domain knowledge and are easily applicable to new tasks. Extensive experiments demonstrate that the proposed scheme alone performs on par with the state-of-the-art offline data augmentation methods, as well as improving upon the state-of-the-art in combination with those methods.
This paper uses a graphic engine to simulate a large amount of training data with free annotations. Between synthetic and real data, there is a two-level domain gap, i.e., content level and appearance level. While the latter has been widely studied, we focus on reducing the content gap in attributes like illumination and viewpoint. To reduce the problem complexity, we choose a smaller and more controllable application, vehicle re-identification (re-ID). We introduce a large-scale synthetic dataset VehicleX. Created in Unity, it contains 1,362 vehicles of various 3D models with fully editable attributes. We propose an attribute descent approach to let VehicleX approximate the attributes in real-world datasets. Specifically, we manipulate each attribute in VehicleX, aiming to minimize the discrepancy between VehicleX and real data in terms of the Fr ́echet Inception Distance (FID). This attribute descent algorithm allows content domain adaptation (DA) orthogonal to existing appearance DA methods. We mix the optimized VehicleX data with real-world vehicle re-ID datasets, and observe consistent improvement. With the augmented datasets, we report competitive accuracy. We make the dataset, engine and our codes available at https://github.com/yorkeyao/VehicleX/.
We propose a weakly-supervised approach for conditional image generation of complex scenes where a user has fine control over objects appearing in the scene. We exploit sparse semantic maps to control object shapes and classes, as well as textual descriptions or attributes to control both local and global style. In order to condition our model on textual descriptions, we introduce a semantic attention module whose computational cost is independent of the image resolution. To further augment the controllability of the scene, we propose a two-step generation scheme that decomposes background and foreground. The label maps used to train our model are produced by a large-vocabulary object detector, which enables access to unlabeled data and provides structured instance information. In such a setting, we report better FID scores compared to fully-supervised settings where the model is trained on ground-truth semantic maps. We also showcase the ability of our model to manipulate a scene on complex datasets such as COCO and Visual Genome.
Multi-Temporal Recurrent Neural Networks For Progressive Non-Uniform Single Image Deblurring With Incremental Temporal Training
PDF ↗Blind non-uniform image deblurring for severe blurs induced by large motions is still challenging. Multi-scale (MS) approach has been widely used for deblurring that sequentially recovers the downsampled original image in low spatial scale first and then further restores in high spatial scale using the result(s) from lower spatial scale(s). Here, we investigate a novel alternative approach to MS, called multi-temporal (MT), for non-uniform single image deblurring by exploiting time-resolved deblurring dataset from high-speed cameras. MT approach models severe blurs as a series of small blurs so that it deblurs small amount of blurs in the original spatial scale progressively instead of restoring the images in different spatial scales. To realize MT approach, we propose progressive deblurring over iterations and incremental temporal training with temporally augmented training data. Our MT approach, that can be seen as a form of curriculum learning in a wide sense, allows a number of state-of-the-art MS based deblurring methods to yield improved performances without using MS approach. We also proposed a MT recurrent neural network with recurrent feature maps that outperformed state-of-the-art deblurring methods with the smallest number of parameters.
Vision-and-Language Navigation (VLN) is a task where agents must decide how to move through a 3D environment to reach a goal by grounding natural language instructions to the visual surroundings. One of the problems of the VLN task is data scarcity since it is difficult to collect enough navigation paths with human-annotated instructions for interactive environments. In this paper, we explore the use of counterfactual thinking as a human-inspired data augmentation method that results in robust models. Counterfactual thinking is a concept that describes the human propensity to create possible alternatives to life events that have already occurred. We propose an adversarial-driven counterfactual reasoning model that can consider effective conditions instead of low-quality augmented data. In particular, we present a model-agnostic adversarial path sampler (APS) that learns to sample challenging paths that force the navigator to improve based on the navigation performance. APS also serves to do pre-exploration of unseen environments to strengthen the model's ability to generalize. We evaluate the influence of APS on the performance of different VLN baseline models using the room-to-room dataset (R2R). The results show that the adversarial training process with our proposed APS benefits VLN models under both seen and unseen environments. And the pre-exploration process can further gain additional improvements under unseen environments.
3D object detection has been widely studied due to its potential applicability to many promising areas such as robotics and augmented reality. Yet, the sparse nature of the 3D data poses unique challenges to this task. Most notably, the observable surface of the 3D point clouds is disjoint from the center of the instance to ground the bounding box prediction on. To this end, we propose Generative Sparse Detection Network (GSDN), a fully-convolutional single-shot sparse detection network that efficiently generates the support for object proposals. The key component of our model is a generative sparse tensor decoder, which uses a series of transposed convolutions and pruning layers to expand the support of sparse tensors while discarding unlikely object centers to maintain minimal runtime and memory footprint. GSDN can process unprecedentedly large-scale inputs with a single fully-convolutional feed-forward pass, thus does not require the heuristic post-processing stage that stitches results from sliding windows as other previous methods have. We validate our approach on three 3D indoor datasets including the large-scale 3D indoor reconstruction dataset where our method outperforms the state-of-the-art methods by a relative improvement of 7.14% while being 3.78 times faster than the best prior work.
In this paper, we present a spatial rectifier to estimate surface normals of tilted images. Tilted images are of particular interest as more visual data are captured by arbitrarily oriented sensors such as body-/robot-mounted cameras. Existing approaches exhibit bounded performance on predicting surface normals because they were trained using gravity-aligned images. Our two main hypotheses are: (1) visual scene layout is indicative of the gravity direction; and (2) not all surfaces are equally represented by a learned estimator due to the structured distribution of the training data, i.e., there exists a transformation for each tilted image that is more responsive to the learned estimator than others. We design a spatial rectifier that is learned to transform the surface normal distribution of a tilted image to the rectified one that matches the gravity-aligned training data distribution. Along with the spatial rectifier, we propose a novel truncated angular loss that offers a stronger gradient at small angular errors and robustness to outliers. The resulting estimator outperforms the state-of-the-art methods including data augmentation baselines not only on ScanNet and NYUv2 but also on a new dataset called Tilt-RGBD that includes considerable roll and pitch camera motion.
AutoAugment has been a powerful algorithm that improves the accuracy of many vision tasks, yet it is sensitive to the operator space as well as hyper-parameters, and an improper setting may degenerate network optimization. This paper delves deep into the working mechanism, and reveals that AutoAugment may remove part of discriminative information from the training image and so insisting on the ground-truth label is no longer the best option. To relieve the inaccuracy of supervision, we make use of knowledge distillation that refers to the output of a teacher model to guide network training. Experiments are performed in standard image classification benchmarks, and demonstrate the effectiveness of our approach in suppressing noise of data augmentation and stabilizing training. Upon the cooperation of knowledge distillation and AutoAugment, we claim the new state-of-the-art on ImageNet classification with a top-1 accuracy of 85.7%.
This paper introduces data augmentation for point clouds by interpolation between examples. Data augmentation by interpolation has shown to be a simple and effective approach in the image domain. Such a mixup is however not directly transferable to point clouds, as we do not have a one-to-one correspondence between the points of two different objects. In this paper, we define data augmentation between point clouds as a shortest path linear interpolation. To that end, we introduce PointMixup, an interpolation method that generates new examples through an optimal assignment of the path function between two point clouds. We prove that our PointMixup finds the shortest path between two point clouds and that the interpolation is assignment invariant and linear. With the definition of interpolation, PointMixup allows to introduce strong interpolation-based regularizers such as mixup and manifold mixup to the point cloud domain. Experimentally, we show the potential of PointMixup for point cloud classification, especially when examples are scarce, as well as increased robustness to noise and geometric transformations to points. The code for PointMixup and the experimental details are publicly available.
The objective of this paper is self-supervised learning from video, in particular for representations for action recognition. We make the following contributions: (i) We propose a new architecture and learning framework Memory-augmented Dense Predictive Coding (MemDPC) for the task. It is trained with a predictive attention mechanism over the set of compressed memories, such that any future states can always be constructed by a convex combination of the condense representations, allowing to make multiple hypotheses efficiently.(ii) We investigate visual-only self-supervised video representation learning from RGB frames, or from unsupervised optical flow, or both. (iii) We thoroughly evaluate the quality of learnt representation on four different downstream tasks: action recognition, video retrieval, learning with scarce annotations, and unintentional action classification. In all cases, we demonstrate state-of-the-art or comparable performance over other approaches with orders of magnitude fewer training data.
Widespread application of computer vision systems in real world tasks is currently hindered by their unexpected behavior on unseen examples. This occurs due to limitations of empirical testing on finite test sets and lack of systematic methods to identify the breaking points of a trained model. In this work we propose semantic adversarial editing,a method to synthesize plausible but difficult data points on which our target model breaks down. We achieve this with a differentiable object synthesizer which can change an object’s appearance while retaining its pose. Constrained adversarial optimization of object appearance through this synthesizer produces rare/difficult versions of an object which fool the target object detector. Experiments show that our approach effectively synthesizes difficult test data, dropping the performance of YoloV3 detector by more than 20 mAP points by changing the appearance of a single object and discovering failure modes of the model. The generated semantic adversarial data can also be used to robustify the detector through data augmentation, consistently improving its performance in both standard and out-of-dataset-distribution test sets, across three different datasets.