mtybilly/Instruct2DS huggingface dataset:
DeFMO: Deblurring and Shape Recovery of Fast Moving Objects
Authors: Martin R. Oswald, Vittorio Ferrari, Jiri Matas, Marc Pollefeys Denys Rozumnyi
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Objects moving at high speed appear significantly blurred when captured with cameras. The blurry appearance is especially ambiguous when the object has complex shape or texture. In such cases, classical methods, or even humans, are unable to recover the object's appearance and motion. We propose a method that, given a single image with its estimated background, outputs the object's appearance and position in a series of sub-frames as if captured by a high-speed camera (i.e. temporal super-resolution). The proposed generative model embeds an image of the blurred object into a latent space representation, disentangles the background, and renders the sharp appearance. Inspired by the image formation model, we design novel self-supervised loss function terms that boost performance and show good generalization capabilities. The proposed DeFMO method is trained on a complex synthetic dataset, yet it performs well on real-world data from several datasets. DeFMO outperforms the state of the art and generates high-quality temporal super-resolution frames.
PISE: Person Image Synthesis and Editing With Decoupled GAN
Authors: Kun Li, Yu-Kun Lai, Jingyu Yang Jinsong Zhang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Person image synthesis, e.g., pose transfer, is a challenging problem due to large variation and occlusion. Existing methods have difficulties predicting reasonable invisible regions and fail to decouple the shape and style of clothing, which limits their applications on person image editing. In this paper, we propose PISE, a novel two-stage generative model for person image synthesis and editing, which can generate realistic person images with desired poses, textures, and semantic layouts. To better predict the invisible region, we first synthesize a human parsing map aligned with the target pose to represent the shape of clothing by a parsing generator, and then generate the final image by an image generator. To decouple the shape and style of clothing, we propose joint global and local per-region encoding and normalization to predict the reasonable style of clothing for invisible regions. We also propose spatial-aware normalization to retain the spatial context relationship in the source image. The results of qualitative and quantitative experiments demonstrate the superiority of our model. Besides, the results of texture transfer and parsing editing show that our model can be applied to person image editing.
4D Hyperspectral Photoacoustic Data Restoration With Reliability Analysis
Authors: Art Subpa-asa, Yinqiang Zheng, Imari Sato Weihang Liao
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Hyperspectral photoacoustic (HSPA) spectroscopy is an emerging bi-modal imaging technology that is able to show the wavelength-dependent absorption distribution of the interior of a 3D volume. However, HSPA devices have to scan an object exhaustively in the spatial and spectral domains; and the acquired data tend to suffer from complex noise. This time-consuming scanning process and noise severely affects the usability of HSPA. It is therefore critical to examine the feasibility of 4D HSPA data restoration from an incomplete and noisy observation. In this work, we present a data reliability analysis for the depth and spectral domain. On the basis of this analysis, we explore the inherent data correlations and develop a restoration algorithm to recover 4D HSPA cubes. Experiments on real data verify that the proposed method achieves satisfactory restoration results.
OBoW: Online Bag-of-Visual-Words Generation for Self-Supervised Learning
Authors: Andrei Bursuc, Gilles Puy, Nikos Komodakis, Matthieu Cord, Patrick Perez Spyros Gidaris
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Learning image representations without human supervision is an important and active research field. Several recent approaches have successfully leveraged the idea of making such a representation invariant under different types of perturbations, especially via contrastive-based instance discrimination training. Although effective visual representations should indeed exhibit such invariances, there are other important characteristics, such as encoding contextual reasoning skills, for which alternative reconstruction-based approaches might be better suited. With this in mind, we propose a teacher-student scheme to learn representations by training a convolutional net to reconstruct a bag-of-visual-words (BoW) representation of an image, given as input a perturbed version of that same image. Our strategy performs an online training of both the teacher network (whose role is to generate the BoW targets) and the student network (whose role is to learn representations), along with an online update of the visual-words vocabulary (used for the BoW targets). This idea effectively enables fully online BoW-guided unsupervised learning. Extensive experiments demonstrate the interest of our BoW-based strategy, which surpasses previous state-of-the-art methods (including contrastive-based ones) in several applications. For instance, in downstream tasks such Pascal object detection, Pascal classification and Places205 classification, our method improves over all prior unsupervised approaches, thus establishing new state-of-the-art results that are also significantly better even than those of supervised pre-training. We provide the implementation code at https://github.com/valeoai/obow.
Learning-Based Image Registration With Meta-Regularization
Authors: Xubo Song Ebrahim Al Safadi
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: We introduce a meta-regularization framework for learning-based image registration. Current learning-based image registration methods use high-resolution architectures such as U-Nets to produce spatial transformations, and impose simple and explicit regularization on the output of the network to ensure that the estimated displacements are smooth. While this approach works well on small deformations, it has been known to struggle when the deformations are large. Our method uses a more advanced form of meta-regularization to increase the generalization ability of learned registration models. We motivate our approach based on Reproducing Kernel Hilbert Space (RKHS) theory, and approximate that framework via a meta-regularization convolutional layer with radially symmetric, positive semi-definite filters that inherent its regularization properties. We then provide a method to learn such regularization filters while also learning to register. Our experiments on synthetic and real datasets as well as ablation analysis show that our method can improve anatomical correspondence compared to competing methods, and reduce the percentage of folding and tear in the large deformation setting, reflecting better regularization and model generalization.
A Hyperbolic-to-Hyperbolic Graph Convolutional Network
Authors: Yuwei Wu, Zhi Gao, Yunde Jia Jindou Dai
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Hyperbolic graph convolutional networks (GCNs) demonstrate powerful representation ability to model graphs with hierarchical structure. Existing hyperbolic GCNs resort to tangent spaces to realize graph convolution on hyperbolic manifolds, which is inferior because tangent space is only a local approximation of a manifold. In this paper, we propose a hyperbolic-to-hyperbolic graph convolutional network (H2H-GCN) that directly works on hyperbolic manifolds. Specifically, we developed a manifold-preserving graph convolution that consists of a hyperbolic feature transformation and a hyperbolic neighborhood aggregation. The hyperbolic feature transformation works as linear transformation on hyperbolic manifolds. It ensures the transformed node representations still lie on the hyperbolic manifold by imposing the orthogonal constraint on the transformation sub-matrix. The hyperbolic neighborhood aggregation updates each node representation via the Einstein midpoint. The H2H-GCN avoids the distortion caused by tangent space approximations and keeps the global hyperbolic structure. Extensive experiments show that the H2H-GCN achieves substantial improvements on the link prediction, node classification, and graph classification tasks.
Deep Homography for Efficient Stereo Image Compression
Authors: Wenzhe Yang, Ren Yang, Mai Xu, Enpeng Liu, Qianhan Feng, Radu Timofte Xin Deng
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: In this paper, we propose HESIC, an end-to-end trainable deep network for stereo image compression (SIC). To fully explore the mutual information across two stereo images, we use a deep regression model to estimate the homography matrix, i.e., H matrix. Then, the left image is spatially transformed by the H matrix, and only the residual information between the left and right images is encoded to save bit-rates. A two-branch auto-encoder architecture is adopted in HESIC, corresponding to the left and right images, respectively. For entropy coding, we propose two conditional stereo entropy models, i.e., Gaussian mixture model (GMM) based and context based entropy models, to fully explore the correlation between the two images to reduce the coding bit-rates. In decoding, a cross quality enhancement module is proposed to enhance the image quality based on inverse H matrix. Experimental results show that our HESIC outperforms state-of-the-art SIC methods on InStereo2K and KITTI datasets both quantitatively and qualitatively.
Point2Skeleton: Learning Skeletal Representations from Point Clouds
Authors: Changjian Li, Yuan Liu, Nenglun Chen, Yi-King Choi, Wenping Wang Cheng Lin
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: We introduce Point2Skeleton, an unsupervised method to learn skeletal representations from point clouds. Existing skeletonization methods are limited to tubular shapes and the stringent requirement of watertight input, while our method aims to produce more generalized skeletal representations for complex structures and handle point clouds. Our key idea is to use the insights of the medial axis transform (MAT) to capture the intrinsic geometric and topological natures of the original input points. We first predict a set of skeletal points by learning a geometric transformation, and then analyze the connectivity of the skeletal points to form skeletal mesh structures. Extensive evaluations and comparisons show our method has superior performance and robustness. The learned skeletal representation will benefit several unsupervised tasks for point clouds, such as surface reconstruction and segmentation.
Neighborhood Contrastive Learning for Novel Class Discovery
Authors: Enrico Fini, Subhankar Roy, Zhiming Luo, Elisa Ricci, Nicu Sebe Zhun Zhong
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: In this paper, we address Novel Class Discovery (NCD), the task of unveiling new classes in a set of unlabeled samples given a labeled dataset with known classes. We exploit the peculiarities of NCD to build a new framework, named Neighborhood Contrastive Learning (NCL), to learn discriminative representations that are important to clustering performance. Our contribution is twofold. First, we find that a feature extractor trained on the labeled set generates representations in which a generic query sample and its neighbors are likely to share the same class. We exploit this observation to retrieve and aggregate pseudo positive pairs with contrastive learning, thus encouraging the model to learn more discriminative representations. Second, we notice that most of the instances are easily discriminated by the network, contributing less to the contrastive loss. To overcome this issue, we propose to generate hard negatives by mixing labeled and unlabeled samples in the feature space. We experimentally demonstrate that these two ingredients significantly contribute to clustering performance and lead our model to outperform state of the art by a large margin (e.g., clustering accuracy +13% on CIFAR-100 and +8% on ImageNet).
SimPoE: Simulated Character Control for 3D Human Pose Estimation
Authors: Shih-En Wei, Tomas Simon, Kris Kitani, Jason Saragih Ye Yuan
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Accurate estimation of 3D human motion from monocular video requires modeling both kinematics (body motion without physical forces) and dynamics (motion with physical forces). To demonstrate this, we present SimPoE, a Simulation-based approach for 3D human Pose Estimation, which integrates image-based kinematic inference and physics-based dynamics modeling. SimPoE learns a policy that takes as input the current-frame pose estimate and the next image frame to control a physically-simulated character to output the next-frame pose estimate. The policy contains a learnable kinematic pose refinement unit that uses 2D keypoints to iteratively refine its kinematic pose estimate of the next frame. Based on this refined kinematic pose, the policy learns to compute dynamics-based control (e.g., joint torques) of the character to advance the current-frame pose estimate to the pose estimate of the next frame. This design couples the kinematic pose refinement unit with the dynamics-based control generation unit, which are learned jointly with reinforcement learning to achieve accurate and physically-plausible pose estimation. Furthermore, we propose a meta-control mechanism that dynamically adjusts the character's dynamics parameters based on the character state to attain more accurate pose estimates. Experiments on large-scale motion datasets demonstrate that our approach establishes the new state of the art in pose accuracy while ensuring physical plausibility.
Neural Camera Simulators
Authors: Zifan Shi, Chenyang Lei, Ka Lung Law, Qifeng Chen Hao Ouyang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: We present a controllable camera simulator based on deep neural networks to synthesize raw image data under different camera settings, including exposure time, ISO, and aperture. The proposed simulator includes an exposure module that utilizes the principle of modern lens designs for correcting the luminance level. It also contains a noise module using the noise level function and an aperture module with adaptive attention to simulate the side effects on noise and defocus blur. To facilitate the learning of a simulator model, we collect a dataset of the 10,000 raw images of 450 scenes with different exposure settings. Quantitative experiments and qualitative comparisons show that our approach outperforms relevant baselines in raw data synthesize on multiple cameras. Furthermore, the camera simulator enables various applications, including large-aperture enhancement, HDR, auto exposure, and data augmentation for training local feature detectors. Our work represents the first attempt to simulate a camera sensor's behavior leveraging both the advantage of traditional raw sensor features and the power of data-driven deep learning.
Neighborhood Normalization for Robust Geometric Feature Learning
Authors: Benjamin D. Killeen, Ayushi Sinha, Masaru Ishii, Gregory D. Hager, Russell H. Taylor, Mathias Unberath Xingtong Liu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Extracting geometric features from 3D models is a common first step in applications such as 3D registration, tracking, and scene flow estimation. Many hand-crafted and learning-based methods aim to produce consistent and distinguishable geometric features for 3D models with partial overlap. These methods work well in cases where the point density and scale of the overlapping 3D objects are similar, but struggle in applications where 3D data are obtained independently with unknown global scale and scene overlap. Unfortunately, instances of this resolution mismatch are common in practice, e.g., when aligning data from multiple sensors. In this work, we introduce a new normalization technique, Batch-Neighborhood Normalization, aiming to improve robustness to mean-std variation of local feature distributions that presumably can happen in samples with varying point density. We empirically demonstrate that the presented normalization method's performance compares favorably to comparison methods in indoor and outdoor environments, and on a clinical dataset, on common point registration benchmarks in both standard and, particularly, resolution-mismatch settings. The source code and clinical dataset are available at https://github.com/lppllppl920/NeighborhoodNormalization-Pytorch.
Video Rescaling Networks With Joint Optimization Strategies for Downscaling and Upscaling
Authors: Yi-Hsin Chen, Cheng-You Lu, Hui-Po Wang, Wen-Hsiao Peng, Ching-Chun Huang Yan-Cheng Huang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: This paper addresses the video rescaling task, which arises from the needs of adapting the video spatial resolution to suit individual viewing devices. We aim to jointly optimize video downscaling and upscaling as a combined task. Most recent studies focus on image-based solutions, which do not consider temporal information. We present two joint optimization approaches based on invertible neural networks with coupling layers. Our Long Short-Term Memory Video Rescaling Network (LSTM-VRN) leverages temporal information in the low-resolution video to form an explicit prediction of the missing high-frequency information for upscaling. Our Multi-input Multi-output Video Rescaling Network (MIMO-VRN) proposes a new strategy for downscaling and upscaling a group of video frames simultaneously. Not only do they outperform the image-based invertible model in terms of quantitative and qualitative results, but also show much improved upscaling quality than the video rescaling methods without joint optimization. To our best knowledge, this work is the first attempt at the joint optimization of video downscaling and upscaling.
TPCN: Temporal Point Cloud Networks for Motion Forecasting
Authors: Tongyi Cao, Qifeng Chen Maosheng Ye
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: We propose the Temporal Point Cloud Networks (TPCN), a novel and flexible framework with joint spatial and temporal learning for trajectory prediction. Unlike existing approaches that rasterize agents and map information as 2D images or operate in a graph representation, our approach extends ideas from point cloud learning with dynamic temporal learning to capture both spatial and temporal information by splitting trajectory prediction into both spatial and temporal dimensions. In the spatial dimension, agents can be viewed as an unordered point set, and thus it is straightforward to apply point cloud learning techniques to model agents' locations. While the spatial dimension does not take kinematic and motion information into account, we further propose dynamic temporal learning to model agents' motion over time. Experiments on the Argoverse motion forecasting benchmark show that our approach achieves state-of-the-art results.
TSGCNet: Discriminative Geometric Feature Learning With Two-Stream Graph Convolutional Network for 3D Dental Model Segmentation
Authors: Yue Zhao, Deyu Meng, Zhiming Cui, Chenqiang Gao, Xinbo Gao, Chunfeng Lian, Dinggang Shen Lingming Zhang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: The ability to segment teeth precisely from digitized 3D dental models is an essential task in computer-aided orthodontic surgical planning. To date, deep learning based methods have been popularly used to handle this task. State-of-the-art methods directly concatenate the raw attributes of 3D inputs, namely coordinates and normal vectors of mesh cells, to train a single-stream network for fully-automated tooth segmentation. This, however, has the drawback of ignoring the different geometric meanings provided by those raw attributes. This issue might possibly confuse the network in learning discriminative geometric features and result in many isolated false predictions on the dental model. Against this issue, we propose a two-stream graph convolutional network (TSGCNet) to learn multi-view geometric information from different geometric attributes. Our TSGCNet adopts two graph-learning streams, designed in an input-aware fashion, to extract more discriminative high-level geometric representations from coordinates and normal vectors, respectively. These feature representations learned from the designed two different streams are further fused to integrate the multi-view complementary information for the cell-wise dense prediction task. We evaluate our proposed TSGCNet on a real-patient dataset of dental models acquired by 3D intraoral scanners, and experimental results demonstrate that our method significantly outperforms state-of-the-art methods for 3D shape segmentation.
Meta Batch-Instance Normalization for Generalizable Person Re-Identification
Authors: Taekyung Kim, Minki Jeong, Hyoungseob Park, Changick Kim Seokeon Choi
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Although supervised person re-identification (Re-ID) methods have shown impressive performance, they suffer from a poor generalization capability on unseen domains. Therefore, generalizable Re-ID has recently attracted growing attention. Many existing methods have employed an instance normalization technique to reduce style variations, but the loss of discriminative information could not be avoided. In this paper, we propose a novel generalizable Re-ID framework, named Meta Batch-Instance Normalization (MetaBIN). Our main idea is to generalize normalization layers by simulating unsuccessful generalization scenarios beforehand in the meta-learning pipeline. To this end, we combine learnable batch-instance normalization layers with meta-learning and investigate the challenging cases caused by both batch and instance normalization layers. Moreover, we diversify the virtual simulations via our meta-train loss accompanied by a cyclic inner-updating manner to boost generalization capability. After all, the MetaBIN framework prevents our model from overfitting to the given source styles and improves the generalization capability to unseen domains without additional data augmentation or complicated network design. Extensive experimental results show that our model outperforms the state-of-the-art methods on the large-scale domain generalization Re-ID benchmark and the cross-domain Re-ID problem. The source code is available at: https://github.com/bismex/MetaBIN.
Dictionary-Guided Scene Text Recognition
Authors: Thu Nguyen, Vinh Tran, Minh-Triet Tran, Thanh Duc Ngo, Thien Huu Nguyen, Minh Hoai Nguyen Nguyen
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Language prior plays an important role in the way humans perceive and recognize text in the wild. In this work, we present an approach to train and use scene text recognition models by exploiting multiple clues from a language reference. Current scene text recognition methods have used lexicons to improve recognition performance, but their naive approach of simply casting the output into a dictionary word based purely on the edit distance has many limitations. We introduce here a novel approach to incorporate a dictionary in both the training and inference stage of a scene text recognition system. We use the dictionary to generate a list of possible outcomes and find the one that is most compatible with the visual appearance of the text. The proposed method leads to a robust scene text recognition model, which is better at handling ambiguous cases encountered in the wild, and improves the overall performance of a state-of-the-art scene text spotting framework. Our work suggests that incorporating language prior is a potential approach to advance scene text detection and recognition methods. Besides, we contribute a challenging scene text dataset for Vietnamese, where some characters are equivocal in the visual form due to accent symbols. This dataset will serve as a challenging benchmark for measuring the applicability and robustness of scene text detection and recognition algorithms.
Glance and Gaze: Inferring Action-Aware Points for One-Stage Human-Object Interaction Detection
Authors: Xian Qu, Changxing Ding, Dacheng Tao Xubin Zhong
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Modern human-object interaction (HOI) detection approaches can be divided into one-stage methods and two-stage ones. One-stage models are more efficient due to their straightforward architectures, but the two-stage models are still advantageous in accuracy. Existing one-stage models usually begin by detecting predefined interaction areas or points, and then attend to these areas only for interaction prediction; therefore, they lack reasoning steps that dynamically search for discriminative cues. In this paper, we propose a novel one-stage method, namely Glance and Gaze Network (GGNet), which adaptively models a set of action-aware points (ActPoints) via glance and gaze steps. The glance step quickly determines whether each pixel in the feature maps is an interaction point. The gaze step leverages feature maps produced by the glance step to adaptively infer ActPoints around each pixel in a progressive manner. Features of the refined ActPoints are aggregated for interaction prediction. Moreover, we design an action-aware approach that effectively matches each detected interaction with its associated human-object pair, along with a novel hard negative attentive loss to improve the optimization of GGNet. All the above operations are conducted simultaneously and efficiently for all pixels in the feature maps. Finally, GGNet outperforms state-of-the-art methods by significant margins on both V-COCO and HICO-DET benchmarks. Code of GGNet is available at https://github.com/SherlockHolmes221/GGNet.
Activate or Not: Learning Customized Activation
Authors: Xiangyu Zhang, Ming Liu, Jian Sun Ningning Ma
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: We present a simple, effective, and general activation function we term ACON which learns to activate the neurons or not. Interestingly, we find Swish, the recent popular NAS-searched activation, can be interpreted as a smooth approximation to ReLU. Intuitively, in the same way, we approximate the more general Maxout family to our novel ACON family, which remarkably improves the performance and makes Swish a special case of ACON. Next, we present meta-ACON, which explicitly learns to optimize the parameter switching between non-linear (activate) and linear (inactivate) and provides a new design space. By simply changing the activation function, we show its effectiveness on both small models and highly optimized large models (e.g. it improves the ImageNet top-1 accuracy rate by 6.7% and 1.8% on MobileNet-0.25 and ResNet-152, respectively). Moreover, our novel ACON can be naturally transferred to object detection and semantic segmentation, showing that ACON is an effective alternative in a variety of tasks. Code is available at https://github.com/nmaac/acon.
Wide-Baseline Relative Camera Pose Estimation With Directional Learning
Authors: Noah Snavely, Ameesh Makadia Kefan Chen
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Modern deep learning techniques that regress the relative camera pose between two images have difficulty dealing with challenging scenarios, such as large camera motions resulting in occlusions and significant changes in perspective that leave little overlap between images. These models continue to struggle even with the benefit of large supervised training datasets. To address the limitations of these models, we take inspiration from techniques that show regressing keypoint locations in 2D and 3D can be improved by estimating a discrete distribution over keypoint locations. Analogously, in this paper we explore improving camera pose regression by instead predicting a discrete distribution over camera poses. To realize this idea, we introduce DirectionNet, which estimates discrete distributions over the 5D relative pose space using a novel parameterization to make the estimation problem tractable. Specifically, DirectionNet factorizes relative camera pose, specified by a 3D rotation and a translation direction, into a set of 3D direction vectors. Since 3D directions can be identified with points on the sphere, DirectionNet estimates discrete distributions on the sphere as its output. We evaluate our model on challenging synthetic and real pose estimation datasets constructed from Matterport3D and InteriorNet. Promising results show a near 50% reduction in error over direct regression methods.
Improving Unsupervised Image Clustering With Robust Learning
Authors: Sungwon Han, Sundong Kim, Danu Kim, Sungkyu Park, Seunghoon Hong, Meeyoung Cha Sungwon Park
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Unsupervised image clustering methods often introduce alternative objectives to indirectly train the model and are subject to faulty predictions and overconfident results. To overcome these challenges, the current research proposes an innovative model RUC that is inspired by robust learning. RUC's novelty is at utilizing pseudo-labels of existing image clustering models as a noisy dataset that may include misclassified samples. Its retraining process can revise misaligned knowledge and alleviate the overconfidence problem in predictions. The model's flexible structure makes it possible to be used as an add-on module to other clustering methods and helps them achieve better performance on multiple datasets. Extensive experiments show that the proposed model can adjust the model confidence with better calibration and gain additional robustness against adversarial noise.
Neural Surface Maps
Authors: Noam Aigerman, Vladimir G. Kim, Niloy J. Mitra Luca Morreale
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Maps are arguably one of the most fundamental concepts used to define and operate on manifold surfaces in differentiable geometry. Accordingly, in geometry processing, maps are ubiquitous and are used in many core applications, such as paramterization, shape analysis, remeshing, and deformation. Unfortunately, most computational representations of surface maps do not lend themselves to manipulation and optimization, usually entailing hard, discrete problems. While algorithms exist to solve these problems, they are problem-specific, and a general framework for surface maps is still in need. In this paper, we advocate to consider neural networks as encoding surface maps. Since neural networks can be composed on one another and are differentiable, we show it is easy to use them to define surfaces via atlases, compose them for surface-to-surface mappings, and optimize differentiable objectives relating to them, such as any notion of distortion, in a trivial manner. In our experiments, we represent surfaces by generating a neural map that approximates a UV parameterization of a 3D model. Then, we compose this map with other neural maps which we optimize with respect to distortion measures. We show that our formulation enables trivial optimization of rather elusive mapping tasks, such as maps between a collection of surfaces.
Enhance Curvature Information by Structured Stochastic Quasi-Newton Methods
Authors: Dong Xu, Hongyu Chen, Zaiwen Wen, Mengyun Chen Minghan Yang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: In this paper, we consider stochastic second-order methods for minimizing a finite summation of nonconvex functions. One important key is to find an ingenious but cheap scheme to incorporate local curvature information. Since the true Hessian matrix is often a combination of a cheap part and an expensive part, we propose a structured stochastic quasi-Newton method by using partial Hessian information as much as possible. By further exploiting either the low-rank structure or the Kronecker-product properties of the quasi-Newton approximations, the computation of the quasi-Newton direction is affordable. Global convergence to stationary point and local superlinear convergence rate are established under some mild assumptions. Numerical results on logistic regression, deep autoencoder networks and deep convolutional neural networks show that our proposed method is quite competitive to the state-of-the-art methods.
Variational Relational Point Completion Network
Authors: Xinyi Chen, Zhongang Cai, Junzhe Zhang, Haiyu Zhao, Shuai Yi, Ziwei Liu Liang Pan
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Real-scanned point clouds are often incomplete due to viewpoint, occlusion, and noise. Existing point cloud completion methods tend to generate global shape skeletons and hence lack fine local details. Furthermore, they mostly learn a deterministic partial-to-complete mapping, but overlook structural relations in man-made objects. To tackle these challenges, this paper proposes a variational framework, Variational Relational point Completion network (VRCNet) with two appealing properties: 1) Probabilistic Modeling. In particular, we propose a dual-path architecture to enable principled probabilistic modeling across partial and complete clouds. One path consumes complete point clouds for reconstruction by learning a point VAE. The other path generates complete shapes for partial point clouds, whose embedded distribution is guided by distribution obtained from the reconstruction path during training. 2) Relational Enhancement. Specifically, we carefully design point self-attention kernel and point selective kernel module to exploit relational point features, which refines local shape details conditioned on the coarse completion. In addition, we contribute a multi-view partial point cloud dataset (MVP dataset) containing over 100,000 high-quality scans, which renders partial 3D shapes from 26 uniformly distributed camera poses for each 3D CAD model. Extensive experiments demonstrate that VRCNet outperforms state-of-the-art methods on all standard point cloud completion benchmarks. Notably, VRCNet shows great generalizability and robustness on real-world point cloud scans.
StruMonoNet: Structure-Aware Monocular 3D Prediction
Authors: Li Erran Li, Qixing Huang Zhenpei Yang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Monocular 3D prediction is one of the fundamental problems in 3D vision. Recent deep learning-based approaches have brought us exciting progress on this problem. However, existing approaches have predominantly focused on end-to-end depth and normal predictions, which do not fully utilize the underlying 3D environment's geometric structures. This paper introduces StruMonoNet, which detects and enforces a planar structure to enhance pixel-wise predictions. StruMonoNet innovates in leveraging a hybrid representation that combines visual feature and a surfel representation for plane prediction. This formulation allows us to combine the power of visual feature learning and the flexibility of geometric representations in incorporating geometric relations. As a result, StruMonoNet can detect relations between planes such as adjacent planes, perpendicular planes, and parallel planes, all of which are beneficial for dense 3D prediction. Experimental results show that StruMonoNet considerably outperforms state-of-the-art approaches on NYUv2 and ScanNet.
Learning To Relate Depth and Semantics for Unsupervised Domain Adaptation
Authors: Anton Obukhov, Danda Pani Paudel, Menelaos Kanakis, Yuhua Chen, Stamatios Georgoulis, Luc Van Gool Suman Saha
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: We present an approach for encoding visual task relationships to improve model performance in an Unsupervised Domain Adaptation (UDA) setting. Semantic segmentation and monocular depth estimation are shown to be complementary tasks; in a multi-task learning setting, a proper encoding of their relationships can further improve performance on both tasks. Motivated by this observation, we propose a novel Cross-Task Relation Layer (CTRL), which encodes task dependencies between the semantic and depth predictions. To capture the cross-task relationships, we propose a neural network architecture that contains task-specific and cross-task refinement heads. Furthermore, we propose an Iterative Self-Learning (ISL) training scheme, which exploits semantic pseudo-labels to provide extra supervision on the target domain. We experimentally observe improvements in both tasks' performance because the complementary information present in these tasks is better captured. Specifically, we show that: (1) our approach improves performance on all tasks when they are complementary and mutually dependent; (2) the CTRL helps to improve both semantic segmentation and depth estimation tasks performance in the challenging UDA setting; (3) the proposed ISL training scheme further improves the semantic segmentation performance. The implementation is available at https://github.com/susaha/ctrl-uda.
Training Networks in Null Space of Feature Covariance for Continual Learning
Authors: Xiaorong Li, Jian Sun, Zongben Xu Shipeng Wang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: In the setting of continual learning, a network is trained on a sequence of tasks, and suffers from catastrophic forgetting. To balance plasticity and stability of network in continual learning, in this paper, we propose a novel network training algorithm called Adam-NSCL, which sequentially optimizes network parameters in the null space of previous tasks. We first propose two mathematical conditions respectively for achieving network stability and plasticity in continual learning. Based on them, the network training for sequential tasks can be simply achieved by projecting the candidate parameter update into the approximate null space of all previous tasks in the network training process, where the candidate parameter update can be generated by Adam. The approximate null space can be derived by applying singular value decomposition to the uncentered covariance matrix of all input features of previous tasks for each linear layer. For efficiency, the uncentered covariance matrix can be incrementally computed after learning each task. We also empirically verify the rationality of the approximate null space at each linear layer. We apply our approach to training networks for continual learning on benchmark datasets of CIFAR-100 and TinyImageNet, and the results suggest that the proposed approach outperforms or matches the state-ot-the-art continual learning approaches.
PiCIE: Unsupervised Semantic Segmentation Using Invariance and Equivariance in Clustering
Authors: Utkarsh Mall, Kavita Bala, Bharath Hariharan Jang Hyun Cho
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: We present a new framework for semantic segmentation without annotations via clustering. Off-the-shelf clustering methods are limited to curated, single-label, and object-centric images yet real-world data are dominantly uncurated, multi-label, and scene-centric. We extend clustering from images to pixels and assign separate cluster membership to different instances within each image. However, solely relying on pixel-wise feature similarity fails to learn high-level semantic concepts and overfits to low-level visual cues. We propose a method to incorporate geometric consistency as an inductive bias to learn invariance and equivariance for photometric and geometric variations. With our novel learning objective, our framework can learn high-level semantic concepts. Our method, PiCIE (Pixel-level feature Clustering using Invariance and Equivariance), is the first method capable of segmenting both things and stuff categories without any hyperparameter tuning or task-specific pre-processing. Our method largely outperforms existing baselines on COCO and Cityscapes with +17.5 Acc. and +4.5 mIoU. We show that PiCIE gives a better initialization for standard supervised training. The code is available at https:// github.com/janghyuncho/PiCIE.
DyCo3D: Robust Instance Segmentation of 3D Point Clouds Through Dynamic Convolution
Authors: Chunhua Shen, Anton van den Hengel Tong He
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Previous top-performing approaches for point cloud instance segmentation involve a bottom-up strategy, which often includes inefficient operations or complex pipelines, such as grouping over-segmented components, introducing additional steps for refining, or designing complicated loss functions. The inevitable variation in the instance scales can lead bottom-up methods to become particularly sensitive to hyper-parameter values. To this end, we propose instead a dynamic, proposal-free, data-driven approach that generates the appropriate convolution kernels to apply in response to the nature of the instances. To make the kernels discriminative, we explore a large context by gathering homogeneous points that share identical semantic categories and have close votes for the geometric centroids. Instances are then decoded by several simple convolutional layers. Due to the limited receptive field introduced by the sparse convolution, a small light-weight transformer is also devised to capture the long-range dependencies and high-level interactions among point samples. The proposed method achieves promising results on both ScanetNetV2 and S3DIS, and this performance is robust to the particular hyper-parameter values chosen. It also improves inference speed by more than 25% over the current state-of-the-art. Code is available at: https://git.io/DyCo3D
SSLayout360: Semi-Supervised Indoor Layout Estimation From 360deg Panorama
Authors: Phi Vu Tran
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Recent years have seen flourishing research on both semi-supervised learning and 3D room layout reconstruction. In this work, we explore the intersection of these two fields to advance the research objective of enabling more accurate 3D indoor scene modeling with less labeled data. We propose the first approach to learn representations of room corners and boundaries by using a combination of labeled and unlabeled data for improved layout estimation in a 360-degree panoramic scene. Through extensive comparative experiments, we demonstrate that our approach can advance layout estimation of complex indoor scenes using as few as 20 labeled examples. When coupled with a layout predictor pre-trained on synthetic data, our semi-supervised method matches the fully supervised counterpart using only 12% of the labels. Our work takes an important first step towards robust semi-supervised layout estimation that can enable many applications in 3D perception with limited labeled data.
SLADE: A Self-Training Framework for Distance Metric Learning
Authors: Yen-Liang Lin, Son Tran, Larry S. Davis, C.-C. Jay Kuo Jiali Duan
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Most existing distance metric learning approaches use fully labeled data to learn the sample similarities in an embedding space. We present a self-training framework, SLADE, to improve retrieval performance by leveraging additional unlabeled data. We first train a teacher model on the labeled data and use it to generate pseudo labels for the unlabeled data. We then train a student model on both labels and pseudo labels to generate final feature embeddings. We use self-supervised representation learning to initialize the teacher model. To better deal with noisy pseudo labels generated by the teacher network, we design a new feature basis learning component for the student network, which learns basis functions of feature representations for unlabeled data. The learned basis vectors better measure the pairwise similarity and are used to select high-confident samples for training the student network. We evaluate our method on standard retrieval benchmarks: CUB-200, Cars-196 and In-shop. Experimental results demonstrate that with additional unlabeled data, our approach significantly improves the performance over the state-of-the-art methods.
NormalFusion: Real-Time Acquisition of Surface Normals for High-Resolution RGB-D Scanning
Authors: Joo Ho Lee, Andreas Meuleman, Min H. Kim Hyunho Ha
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Multiview shape-from-shading (SfS) has achieved high-detail geometry, but its computation is expensive for solving a multiview registration and an ill-posed inverse rendering problem. Therefore, it has been mainly used for offline methods. Volumetric fusion enables real-time scanning using a conventional RGB-D camera, but its geometry resolution has been limited by the grid resolution of the volumetric distance field and depth registration errors. In this paper, we propose a real-time scanning method that can acquire high-detail geometry by bridging volumetric fusion and multiview SfS in two steps. First, we propose the first real-time acquisition of photometric normals stored in texture space to achieve high-detail geometry. We also introduce geometry-aware texture mapping, which progressively refines geometric registration between the texture space and the volumetric distance field by means of normal texture, achieving real-time multiview SfS. We demonstrate our scanning of high-detail geometry using an RGB-D camera at  20 fps. Results verify that the geometry quality of our method is strongly competitive with that of offline multi-view SfS methods.