Domain generalization (DG) aims to generalize a model trained on multiple source (i.e., training) domains to a distributionally different target (i.e., test) domain. In contrast to the DG setup that strictly requires the availability of multiple source domains, this paper considers a more realistic yet challenging scenario, namely Single Domain Generalization (SDG). In this new setting, there is only one source domain available for training, from which the limited diversity may jeopardize the model generalization on unseen target domains. To tackle this problem, we propose a style-complement module to enhance the generalization power of the model by synthesizing images from diverse distributions that are complementary to the source ones. More specifically, we adopt tractable upper and lower bounds of mutual information (MI) between the generated and source samples and perform the two-step optimization iteratively: (1) by minimizing MI upper bound approximation for each pair, the generated images are forced to diversify from the source samples; (2) subsequently, we maximize the lower bound of MI between the samples from the same semantic category, which assists the network to learn discriminative features from diverse-styled images. Extensive experiments on three benchmark datasets demonstrate the superiority of our approach, which surpasses the state-of-the-art single DG methods by up to 25.14%.
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Motion is an important cue for video prediction and often utilized by separating video content into static and dynamic components. Most of the previous work utilizing motion is deterministic but there are stochastic methods that can model the inherent uncertainty of the future. Existing stochastic models either do not reason about motion explicitly or make limiting assumptions about the static part. In this paper, we reason about appearance and motion in the video stochastically by predicting the future based on the motion history. Explicit reasoning about motion without history already reaches the performance of current stochastic models. The motion history further improves the results by allowing to predict consistent dynamics several frames into the future. Our model performs comparably to the state-of-the-art models on the generic video prediction datasets, however, significantly outperforms them on two challenging real-world autonomous driving datasets with complex motion and dynamic background.
Computer vision applications such as visual relationship detection and human object interaction can be formulated as a composite (structured) set detection problem in which both the parts (subject, object, and predicate) and the sum (triplet as a whole) are to be detected in a hierarchical fashion. In this paper, we present a new approach, denoted Part-and-Sum detection Transformer (PST), to perform end-to-end visual composite set detection. Different from existing Transformers in which queries are at a single level, we simultaneously model the joint part and sum hypotheses/interactions with composite queries and attention modules. We explicitly incorporate sum queries to enable better modeling of the part-and-sum relations that are absent in the standard Transformers. Our approach also uses novel tensor-based part queries and vector-based sum queries, and models their joint interaction. We report experiments on two vision tasks, visual relationship detection and human object interaction and demonstrate that PST achieves state of the art results among single-stage models, while nearly matching the results of custom designed two-stage models.
Existing single image high dynamic range (HDR) reconstruction attempt to expand the range of luminance. They are not effective to generate plausible textures and colors in the reconstructed results, especially for high-density pixels in ultra-high-definition (UHD) images.To address these problems, we propose a new HDR reconstruction network for UHD images by collaboratively learning color and texture details. First, we propose a dual-path network to extract content and chromatic features at a reduced resolution of the low dynamic range (LDR) input. These two types features are used to fit bilatera-space affine models for real-time HDR reconstruction. To extract the main data structure of the LDR input, we propose to use 3D Tucker decomposition and reconstruction to prevents false edges and noise amplification in the learned bilateral grid. As a result, the high-quality content and chromatic features can be reconstructed capitalized on guided bilateral upsampling. Finally, we fuse these two full-resolution feature maps into the HDR reconstructed results.Our proposed method can achieve real-time processing for UHD image (about 160 fps).Experimental results demonstrate that the proposed algorithm performs favorably against the state-of-the-art HDR reconstruction approaches on public benchmarks and real-world UHD images.
In this paper, we tackle the problem of dense light field (LF) reconstruction from sparsely-sampled ones with wide baselines and propose a learnable model, namely dynamic interpolation, to replace the commonly-used geometry warping operation. Specifically, with the estimated geometric relation between input views, we first construct a lightweight neural network to dynamically learn weights for interpolating neighbouring pixels from input views to synthesize each pixel of novel views independently. In contrast to the fixed and content-independent weights employed in the geometry warping operation, the learned interpolation weights implicitly incorporate the correspondences between the source and novel views and adapt to different image content information. Then, we recover the spatial correlation between the independently synthesized pixels of each novel view by referring to that of input views using a geometry-based spatial refinement module. We also constrain the angular correlation between the novel views through a disparity-oriented LF structure loss. Experimental results on LF datasets with wide baselines show that the reconstructed LFs achieve much higher PSNR/SSIM and preserve the LF parallax structure better than state-of-the-art methods. The source code is publicly available at https://github.com/MantangGuo/DI4SLF.
GeomNet: A Neural Network Based on Riemannian Geometries of SPD Matrix Space and Cholesky Space for 3D Skeleton-Based Interaction Recognition
PDF ↗In this paper, we propose a novel method for representation and classification of two-person interactions from 3D skeleton sequences. The key idea of our approach is to use Gaussian distributions to capture statistics on Rn and those on the space of symmetric positive definite (SPD) matrices. The main challenge is how to parametrize those distributions. Towards this end, we develop methods for embedding Gaussian distributions in matrix groups based on the theory of Lie groups and Riemannian symmetric spaces. Our method relies on the Riemannian geometry of the underlying manifolds and has the advantage of encoding high-order statistics from 3D joint positions. We show that the proposed method achieves competitive results in two-person interaction recognition on two large-scale benchmarks for 3D human activity understanding.
DepthInSpace: Exploitation and Fusion of Multiple Video Frames for Structured-Light Depth Estimation
PDF ↗We present DepthInSpace, a self-supervised deep-learning method for depth estimation using a structured-light camera. The design of this method is motivated by the commercial use case of embedded depth sensors in nowadays smartphones. We first propose to use estimated optical flow from ambient information of multiple video frames as a complementary guide for training a single-frame depth estimation network, helping to preserve edges and reduce over-smoothing issues. Utilizing optical flow, we also propose to fuse the data of multiple video frames to get a more accurate depth map. In particular, fused depth maps are more robust in occluded areas and incur less in flying pixels artifacts. We finally demonstrate that these more precise fused depth maps can be used as self-supervision for fine-tuning a single-frame depth estimation network to improve its performance. Our models' effectiveness is evaluated and compared with state-of-the-art models on both synthetic and our newly introduced real datasets. The implementation code, training procedure, and both synthetic and captured real datasets are available at https://www.idiap.ch/paper/depthinspace.
Barrel distortion rectification aims at removing the radial distortion in a distorted image captured by a wide-angle lens. Previous deep learning methods mainly solve this problem by learning the implicit distortion parameters or the nonlinear rectified mapping function in a direct manner. However, this type of manner results in an indistinct learning process of rectification and thus limits the deep perception of distortion. In this paper, inspired by the curriculum learning, we analyze the barrel distortion rectification task in a progressive and meaningful manner. By considering the relationship among different construction levels in an image, we design a multi-level curriculum that disassembles the rectification task into three levels, structure recovery, semantics embedding, and texture rendering. With the guidance of the curriculum that corresponds to the construction of images, the proposed hierarchical architecture enables a progressive rectification and achieves more accurate results. Moreover, we present a novel distortion-aware pre-training strategy to facilitate the initial learning of neural networks, promoting the model to converge faster and better. Experimental results on the synthesized and real-world distorted image datasets show that the proposed approach significantly outperforms other learning methods, both qualitatively and quantitatively.
Every time you sit in front of a TV or monitor, your face is actively illuminated by time-varying patterns of light. This paper proposes to use this time-varying illumination for synthetic relighting of your face with any new illumination condition. In doing so, we take inspiration from the light stage work of Debevec et al. [4], who first demonstrated the ability to relight people captured in a controlled lighting environment. Whereas existing light stages require expensive, room-scale spherical capture gantries and exist in only a few labs in the world, we demonstrate how to acquire useful data from a normal TV or desktop monitor. Instead of subjecting the user to uncomfortable rapidly flashing light patterns, we operate on images of the user watching a YouTube video or other standard content. We train a deep network on images plus monitor patterns of a given user and learn to predict images of that user under any target illumination (monitor pattern). Experimental evaluation shows that our method produces realistic relighting results.
We address the task of Re-Identification (Re-ID) in multi-target multi-camera (MTMC) tracking where we track multiple pedestrians using multiple overlapping uncalibrated (unknown pose) cameras. Since the videos are temporally synchronized and spatially overlapping, we can see a person from multiple views and associate their trajectory across cameras. In order to find the correct association between pedestrians visible from multiple views during the same time window, we extract a visual feature from a tracklet (sequence of pedestrian images) that encodes its similarity and dissimilarity to all other candidate tracklets. We propose a inter-tracklet (person to person) attention mechanism that learns a representation for a target tracklet while taking into account other tracklets across multiple views. Furthermore, to encode the gait and motion of a person, we introduce second intra-tracklet (person-specific) attention module with position embeddings. This second module employs a transformer encoder to learn a feature from a sequence of features over one tracklet. Experimental results on WILDTRACK and our new dataset `ConstructSite' confirm the superiority of our model over state-of-the-art ReID methods (5% and 10% performance gain respectively) in the context of uncalibrated MTMC tracking. While our model is designed for overlapping cameras, we also obtain state-of-the-art results on two other benchmark datasets (MARS and DukeMTMC) with non-overlapping cameras.
We present ALADIN (All Layer AdaIN); a novel architecture for searching images based on the similarity of their artistic style. Representation learning is critical to visual search, where distance in the learned search embedding reflects image similarity. Learning an embedding that discriminates fine-grained variations in style is hard, due to the difficulty of defining and labelling style. ALADIN takes a weakly supervised approach to learning a representation for fine-grained style similarity of digital artworks, leveraging BAM-FG, a novel large-scale dataset of user generated content groupings gathered from the web. ALADIN sets a new state of the art accuracy for style-based visual search over both coarse labelled style data (BAM) and BAM-FG; a new 2.62 million image dataset of 310,000 fine-grained style groupings also contributed by this work.
Object detection has been widely used in many safety-critical tasks, such as autonomous driving. However, its vulnerability to adversarial examples has not been sufficiently studied, especially under the practical scenario of black-box attacks, where the attacker can only access the query feedback of predicted bounding-boxes and top-1 scores returned by the attacked model. Compared with black-box attack to image classification, there are two main challenges in black-box attack to detection. Firstly, even if one bounding-box is successfully attacked, another sub-optimal bounding-box may be detected near the attacked bounding-box. Secondly, there are multiple bounding-boxes, leading to very high attack cost. To address these challenges, we propose a Parallel Rectangle Flip Attack (PRFA) via random search. Specifically, we generate perturbations in each rectangle patch to avoid sub-optimal detection near the attacked region. Besides, utilizing the observation that adversarial perturbations mainly locate around objects' contours and critical points under white-box attacks, the search space of attacked rectangles is reduced to improve the attack efficiency. Moreover, we develop a parallel mechanism of attacking multiple rectangles simultaneously to further accelerate the attack process. Extensive experiments demonstrate that our method can effectively and efficiently attack various popular object detectors, including anchor-based and anchor-free, and generate transferable adversarial examples.
Neural volumetric representations such as Neural Radiance Fields (NeRF) have emerged as a compelling technique for learning to represent 3D scenes from images with the goal of rendering photorealistic images of the scene from unobserved viewpoints. However, NeRF's computational requirements are prohibitive for real-time applications: rendering views from a trained NeRF requires querying a multilayer perceptron (MLP) hundreds of times per ray. We present a method to train a NeRF, then precompute and store (i.e. ""bake"") it as a novel representation called a Sparse Neural Radiance Grid (SNeRG) that enables real-time rendering on commodity hardware. To achieve this, we introduce 1) a reformulation of NeRF's architecture, and 2) a sparse voxel grid representation with learned feature vectors. The resulting scene representation retains NeRF's ability to render fine geometric details and view-dependent appearance, is compact (averaging less than 90 MB per scene), and can be rendered in real-time (higher than 30 frames per second on a laptop GPU). Actual screen captures are shown in our video.
Although recent research has witnessed a significant progress on the video deblurring task, these methods struggle to reconcile inference efficiency and visual quality simultaneously, especially on ultra-high-definition (UHD) videos (e.g., 4K resolution). To address the problem, we propose a novel deep model for fast and accurate UHD Video Deblurring (UHDVD). The proposed UHDVD is achieved by a separable-patch architecture, which collaborates with a multi-scale integration scheme to achieve a large receptive field without adding the number of generic convolutional layers and kernels. Additionally, we design a residual channel-spatial attention (RCSA) module to improve accuracy and reduce the depth of the network appropriately. The proposed UHDVD is the first real-time deblurring model for 4K videos at 35 fps. To train the proposed model, we build a new dataset comprised of 4K blurry videos and corresponding sharp frames using three different smartphones. Comprehensive experimental results show that our network performs favorably against the state-ofthe-art methods on both the 4K dataset and public benchmarks in terms of accuracy, speed, and model size.
3D building reconstruction from monocular remote sensing imagery is an important research problem and an economic solution to large-scale city modeling, compared with reconstruction from LiDAR data and multi-view imagery. However, several challenges such as the partial invisibility of building footprints and facades, the serious shadow effect, and the extreme variance of building height in large-scale areas, have restricted the existing monocular image based building reconstruction studies to certain application scenes, i.e., modeling simple low-rise buildings from near-nadir images. In this study, we propose a novel 3D building reconstruction method for monocular remote sensing images, which tackles the above difficulties, thus providing an appealing solution for more complicated scenarios. We design a multi-task building reconstruction network, named MTBR-Net, to learn the geometric property of oblique images, the key components of a 3D building model and their relations via four semantic-related and three offset-related tasks. The network outputs are further integrated by a prior knowledge based 3D model optimization method to produce the the final 3D building models. Results on a public 3D reconstruction dataset and a novel released dataset demonstrate that our method improves the height estimation performance by over 40% and the segmentation F1-score by 2% - 4% compared with current state-of-the-art.
The domain-adaptive semantic segmentation in aerial images by a deep-learning technique remains a challenge owing to the domain gaps caused by a resolution, image sensors, time-zone, the density of buildings, and even building styles of each city. Currently, convolutional neural network (CNN)-based domain adaptation methodologies have been developed to decrease the domain gaps, but, they have shown still poor performance to utilize multiple aerial images in different domains. In this paper, therefore, the CNN-based network denoted as Self-Mutating Network, which changes the values of parameters of convolutional filters itself according to the domain of input image, is proposed. By adopting Parameter Mutation to change the values of parameters and Parameter Fluctuation to randomly convulse the parameters, the network self-changes and fine-tunes the parameters, then achieves better predictions of a domain-adaptive segmentation. Through the ablation study of the Self-Mutating Network, we concluded that the Self-Mutating Network can be utilized in the domain-adaptive semantic segmentation of aerial images in different domains.
Disentangling data into interpretable and independent factors is critical for controllable generation tasks. With the availability of labeled data, supervision can help enforce the separation of specific factors as expected. However, it is often expensive or even impossible to label every single factor to achieve fully-supervised disentanglement. In this paper, we adopt a general setting where all factors that are hard to label or identify are encapsulated as a single unknown factor. Under this setting, we propose a flexible weakly-supervised multi-factor disentanglement framework DisUnknown, which Distills Unknown factors for enabling multi-conditional generation regarding both labeled and unknown factors. Specifically, a two-stage training approach is adopted to first disentangle the unknown factor with an effective and robust training method, and then train the final generator with the proper disentanglement of all labeled factors utilizing the unknown distillation. To demonstrate the generalization capacity and scalability of our method, we evaluate it on multiple benchmark datasets qualitatively and quantitatively and further apply it to various real-world applications on complicated datasets.
Domain adaptation (DA) aims to transfer the knowledge learned from source domain to an unlabeled target domain. Some recent works tackle source-free domain adaptation (SFDA) where only source pre-trained model is available for adaptation to target domain. However those methods does not consider keeping source performance which is of high practical value in real world application. In this paper, we propose a new domain adaptation paradigm denoted as Generalized Source-free Domain Adaptation (G-SFDA), where the learned model needs to perform well on both target and source domains, with only access to current unlabeled target data during adaptation. First, we propose local structure clustering (LSC), aiming to cluster the target features with its semantically similar neighbors, which successfully adapts the model to target domain in absence of source data. Second, we propose randomly generated domain attention (RGDA), it produces binary domain specific attention to activate different feature channels for different domains, meanwhile the domain attention will be utilized to regularize the gradient during adaptation to keep source information. In the experiments, for target performance our method is on par with or better than existing DA and SFDA methods, specifically achieves state-of-the-art performance (85.4%) on VisDA, and our method works well for all domains after adapting to single or multiple target domains.
Predicting human motion from a historical pose sequence is at the core of many applications in computer vision. Current state-of-the-art methods concentrate on learning motion contexts in the pose space, however, the high dimensionality and complex nature of human pose invoke inherent difficulties in extracting such contexts. In this paper, we instead advocate to model motion contexts in the joint trajectory space, as the trajectory of a joint is smooth, vectorial, and gives sufficient information to the model. Moreover, most existing methods consider only the dependencies between skeletal connected joints, disregarding prior knowledge and the hidden connections between geometrically separated joints. Motivated by this, we present a semi-constrained graph to explicitly encode skeletal connections and prior knowledge, while adaptively learn implicit dependencies between joints. We also explore the applications of our approach to a range of objects including human, fish, and mouse. Surprisingly, our method sets the new state-of-the-art performance on 4 different benchmark datasets, a remarkable highlight is that it achieves a 19.1% accuracy improvement over current state-of-the-art in average. To facilitate future research, we have released our code at https://github.com/Pose-Group/MPT.
Helmholtz stereopsis (HS) exploits the reciprocity principle of light propagation (i.e., the Helmholtz reciprocity) for 3D reconstruction of surfaces with arbitrary reflectance. In this paper, we present the polarimetric Helmholtz stereopsis (polar-HS), which extends the classical HS by considering the polarization state of light in the reciprocal paths. With the additional phase information from polarization, polar-HS requires only one reciprocal image pair. We formulate new reciprocity and diffuse/specular polarimetric constraints to recover surface depths and normals using an optimization framework. Using a hardware prototype, we show that our approach produces high-quality 3D reconstruction for different types of surfaces, ranging from diffuse to highly specular.