mtybilly/Instruct2DS huggingface dataset:
LSM: Learning Subspace Minimization for Low-Level Vision
Authors: Chengzhou Tang, Lu Yuan, Ping Tan
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: We study the energy minimization problem in low-level vision tasks from a novel perspective. We replace the heuristic regularization term with a data-driven learnable subspace constraint, and preserve the data term to exploit domain knowledge derived from the first principles of a task. This learning subspace minimization (LSM) framework unifies the network structures and the parameters for many different low-level vision tasks, which allows us to train a single network for multiple tasks simultaneously with shared parameters, and even generalizes the trained network to an unseen task as long as the data term can be formulated. We validate our LSM frame on four low-level tasks including edge detection, interactive segmentation, stereo matching, and optical flow, and validate the network on various datasets. The experiments demonstrate that the proposed LSM generates state-of-the-art results with smaller model size, faster training convergence, and real-time inference.
Erasing Integrated Learning: A Simple Yet Effective Approach for Weakly Supervised Object Localization
Authors: Jinjie Mai, Meng Yang, Wenfeng Luo
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Weakly supervised object localization (WSOL) aims to localize object with only weak supervision like image-level labels. However, a long-standing problem for available techniques based on the classification network is that they often result in highlighting the most discriminative parts rather than the entire extent of object. Nevertheless, trying to explore the integral extent of the object could degrade the performance of image classification on the contrary. To remedy this, we propose a simple yet powerful approach by introducing a novel adversarial erasing technique, erasing integrated learning (EIL). By integrating discriminative region mining and adversarial erasing in a single forward-backward propagation in a vanilla CNN, the proposed EIL explores the high response class-specific area and the less discriminative region simultaneously, thus could maintain high performance in classification and jointly discover the full extent of the object. Furthermore, we apply multiple EIL (MEIL) modules at different levels of the network in a sequential manner, which for the first time integrates semantic features of multiple levels and multiple scales through adversarial erasing learning. In particular, the proposed EIL and advanced MEIL both achieve a new state-of-the-art performance in CUB-200-2011 and ILSVRC 2016 benchmark, making significant improvement in localization while advancing high performance in image classification.
Self-Supervised Deep Visual Odometry With Online Adaptation
Authors: Shunkai Li, Xin Wang, Yingdian Cao, Fei Xue, Zike Yan, Hongbin Zha
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Self-supervised VO methods have shown great success in jointly estimating camera pose and depth from videos. However, like most data-driven methods, existing VO networks suffer from a notable decrease in performance when confronted with scenes different from the training data, which makes them unsuitable for practical applications. In this paper, we propose an online meta-learning algorithm to enable VO networks to continuously adapt to new environments in a self-supervised manner. The proposed method utilizes convolutional long short-term memory (convLSTM) to aggregate rich spatial-temporal information in the past. The network is able to memorize and learn from its past experience for better estimation and fast adaptation to the current frame. When running VO in the open world, in order to deal with the changing environment, we propose an online feature alignment method by aligning feature distributions at different time. Our VO network is able to seamlessly adapt to different environments. Extensive experiments on unseen outdoor scenes, virtual to real world and outdoor to indoor environments demonstrate that our method consistently outperforms state-of-the-art self-supervised VO baselines considerably.
Weakly-Supervised Semantic Segmentation via Sub-Category Exploration
Authors: Yu-Ting Chang, Qiaosong Wang, Wei-Chih Hung, Robinson Piramuthu, Yi-Hsuan Tsai, Ming-Hsuan Yang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Existing weakly-supervised semantic segmentation methods using image-level annotations typically rely on initial responses to locate object regions. However, such response maps generated by the classification network usually focus on discriminative object parts, due to the fact that the network does not need the entire object for optimizing the objective function. To enforce the network to pay attention to other parts of an object, we propose a simple yet effective approach that introduces a self-supervised task by exploiting the sub-category information. Specifically, we perform clustering on image features to generate pseudo sub-categories labels within each annotated parent class, and construct a sub-category objective to assign the network to a more challenging task. By iteratively clustering image features, the training process does not limit itself to the most discriminative object parts, hence improving the quality of the response maps. We conduct extensive analysis to validate the proposed method and show that our approach performs favorably against the state-of-the-art approaches.
Normalizing Flows With Multi-Scale Autoregressive Priors
Authors: Apratim Bhattacharyya, Shweta Mahajan, Mario Fritz, Bernt Schiele, Stefan Roth
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Flow-based generative models are an important class of exact inference models that admit efficient inference and sampling for image synthesis. Owing to the efficiency constraints on the design of the flow layers, e.g. split coupling flow layers in which approximately half the pixels do not undergo further transformations, they have limited expressiveness for modeling long-range data dependencies compared to autoregressive models that rely on conditional pixel-wise generation. In this work, we improve the representational power of flow-based models by introducing channel-wise dependencies in their latent space through multi-scale autoregressive priors (mAR). Our mAR prior for models with split coupling flow layers (mAR-SCF) can better capture dependencies in complex multimodal data. The resulting model achieves state-of-the-art density estimation results on MNIST, CIFAR-10, and ImageNet. Furthermore, we show that mAR-SCF allows for improved image generation quality, with gains in FID and Inception scores compared to state-of-the-art flow-based models.
Dynamic Neural Relational Inference
Authors: Colin Graber, Alexander G. Schwing
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Understanding interactions between entities, e.g., joints of the human body, team sports players, etc., is crucial for tasks like forecasting. However, interactions between entities are commonly not observed and often hard to quantify. To address this challenge, recently, `Neural Relational Inference' was introduced. It predicts static relations between entities in a system and provides an interpretable representation of the underlying system dynamics that are used for better trajectory forecasting. However, generally, relations between entities change as time progresses. Hence, static relations improperly model the data. In response to this, we develop Dynamic Neural Relational Inference (dNRI), which incorporates insights from sequential latent variable models to predict separate relation graphs for every time-step. We demonstrate on several real-world datasets that modeling dynamic relations improves forecasting of complex trajectories.
Embedding Expansion: Augmentation in Embedding Space for Deep Metric Learning
Authors: Byungsoo Ko, Geonmo Gu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Learning the distance metric between pairs of samples has been studied for image retrieval and clustering. With the remarkable success of pair-based metric learning losses, recent works have proposed the use of generated synthetic points on metric learning losses for augmentation and generalization. However, these methods require additional generative networks along with the main network, which can lead to a larger model size, slower training speed, and harder optimization. Meanwhile, post-processing techniques, such as query expansion and database augmentation, have proposed the combination of feature points to obtain additional semantic information. In this paper, inspired by query expansion and database augmentation, we propose an augmentation method in an embedding space for pair-based metric learning losses, called embedding expansion. The proposed method generates synthetic points containing augmented information by a combination of feature points and performs hard negative pair mining to learn with the most informative feature representations. Because of its simplicity and flexibility, it can be used for existing metric learning losses without affecting model size, training speed, or optimization difficulty. Finally, the combination of embedding expansion and representative metric learning losses outperforms the state-of-the-art losses and previous sample generation methods in both image retrieval and clustering tasks. The implementation is publicly available.
LT-Net: Label Transfer by Learning Reversible Voxel-Wise Correspondence for One-Shot Medical Image Segmentation
Authors: Shuxin Wang, Shilei Cao, Dong Wei, Renzhen Wang, Kai Ma, Liansheng Wang, Deyu Meng, Yefeng Zheng
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: We introduce a one-shot segmentation method to alleviate the burden of manual annotation for medical images. The main idea is to treat one-shot segmentation as a classical atlas-based segmentation problem, where voxel-wise correspondence from the atlas to the unlabelled data is learned. Subsequently, segmentation label of the atlas can be transferred to the unlabelled data with the learned correspondence. However, since ground truth correspondence between images is usually unavailable, the learning system must be well-supervised to avoid mode collapse and convergence failure. To overcome this difficulty, we resort to the forward-backward consistency, which is widely used in correspondence problems, and additionally learn the backward correspondences from the warped atlases back to the original atlas. This cycle-correspondence learning design enables a variety of extra, cycle-consistency-based supervision signals to make the training process stable, while also boost the performance. We demonstrate the superiority of our method over both deep learning-based one-shot segmentation methods and a classical multi-atlas segmentation method via thorough experiments.
Transferring Dense Pose to Proximal Animal Classes
Authors: Artsiom Sanakoyeu, Vasil Khalidov, Maureen S. McCarthy, Andrea Vedaldi, Natalia Neverova
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Recent contributions have demonstrated that it is possible to recognize the pose of humans densely and accurately given a large dataset of poses annotated in detail. In principle, the same approach could be extended to any animal class, but the effort required for collecting new annotations for each case makes this strategy impractical, despite important applications in natural conservation, science and business. We show that, at least for proximal animal classes such as chimpanzees, it is possible to transfer the knowledge existing in dense pose recognition for humans, as well as in more general object detectors and segmenters, to the problem of dense pose recognition in other classes. We do this by (1) establishing a DensePose model for the new animal which is also geometrically aligned to humans (2) introducing a multi-head R-CNN architecture that facilitates transfer of multiple recognition tasks between classes, (3) finding which combination of known classes can be transferred most effectively to the new animal and (4) using self-calibrated uncertainty heads to generate pseudo-labels graded by quality for training a model for this class. We also introduce two benchmark datasets labelled in the manner of DensePose for the class chimpanzee and use them to evaluate our approach, showing excellent transfer learning performance.
Suppressing Uncertainties for Large-Scale Facial Expression Recognition
Authors: Kai Wang, Xiaojiang Peng, Jianfei Yang, Shijian Lu, Yu Qiao
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Annotating a qualitative large-scale facial expression dataset is extremely difficult due to the uncertainties caused by ambiguous facial expressions, low-quality facial images, and the subjectiveness of annotators. These uncertainties suspend the progress of large-scale Facial Expression Recognition (FER) in data-driven deep learning era. To address this problelm, this paper proposes to suppress the uncertainties by a simple yet efficient Self-Cure Network (SCN). Specifically, SCN suppresses the uncertainty from two different aspects: 1) a self-attention mechanism over FER dataset to weight each sample in training with a ranking regularization, and 2) a careful relabeling mechanism to modify the labels of these samples in the lowest-ranked group. Experiments on synthetic FER datasets and our collected WebEmotion dataset validate the effectiveness of our method. Results on public benchmarks demonstrate that our SCN outperforms current state-of-the-art methods with 88.14% on RAF-DB, 60.23% on AffectNet, and 89.35% on FERPlus.
Scale-Space Flow for End-to-End Optimized Video Compression
Authors: Eirikur Agustsson, David Minnen, Nick Johnston, Johannes Balle, Sung Jin Hwang, George Toderici
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Despite considerable progress on end-to-end optimized deep networks for image compression, video coding remains a challenging task. Recently proposed methods for learned video compression use optical flow and bilinear warping for motion compensation and show competitive rate-distortion performance relative to hand-engineered codecs like H.264 and HEVC. However, these learning-based methods rely on complex architectures and training schemes including the use of pre-trained optical flow networks, sequential training of sub-networks, adaptive rate control, and buffering intermediate reconstructions to disk during training. In this paper, we show that a generalized warping operator that better handles common failure cases, e.g. disocclusions and fast motion, can provide competitive compression results with a greatly simplified model and training procedure. Specifically, we propose scale-space flow, an intuitive generalization of optical flow that adds a scale parameter to allow the network to better model uncertainty. Our experiments show that a low-latency video compression model (no B-frames) using scale-space flow for motion compensation can outperform analogous state-of-the art learned video compression models while being trained using a much simpler procedure and without any pre-trained optical flow networks.
StyleRig: Rigging StyleGAN for 3D Control Over Portrait Images
Authors: Ayush Tewari, Mohamed Elgharib, Gaurav Bharaj, Florian Bernard, Hans-Peter Seidel, Patrick Perez, Michael Zollhofer, Christian Theobalt
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: StyleGAN generates photorealistic portrait images of faces with eyes, teeth, hair and context (neck, shoulders, background), but lacks a rig-like control over semantic face parameters that are interpretable in 3D, such as face pose, expressions, and scene illumination. Three-dimensional morphable face models (3DMMs) on the other hand offer control over the semantic parameters, but lack photorealism when rendered and only model the face interior, not other parts of a portrait image (hair, mouth interior, background). We present the first method to provide a face rig-like control over a pretrained and fixed StyleGAN via a 3DMM. A new rigging network, RigNet is trained between the 3DMM's semantic parameters and StyleGAN's input. The network is trained in a self-supervised manner, without the need for manual annotations. At test time, our method generates portrait images with the photorealism of StyleGAN and provides explicit control over the 3D semantic parameters of the face.
Semantic Pyramid for Image Generation
Authors: Assaf Shocher, Yossi Gandelsman, Inbar Mosseri, Michal Yarom, Michal Irani, William T. Freeman, Tali Dekel
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: We present a novel GAN-based model that utilizes the space of deep features learned by a pre-trained classification model. Inspired by classical image pyramid representations, we construct our model as a Semantic Generation Pyramid -- a hierarchical framework which leverages the continuum of semantic information encapsulated in such deep features; this ranges from low level information contained in fine features to high level, semantic information contained in deeper features. More specifically, given a set of features extracted from a reference image, our model generates diverse image samples, each with matching features at each semantic level of the classification model. We demonstrate that our model results in a versatile and flexible framework that can be used in various classic and novel image generation tasks. These include: generating images with a controllable extent of semantic similarity to a reference image, and different manipulation tasks such as semantically-controlled inpainting and compositing; all achieved with the same model, with no further training.
Towards Backward-Compatible Representation Learning
Authors: Yantao Shen, Yuanjun Xiong, Wei Xia, Stefano Soatto
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: We propose a way to learn visual features that are compatible with previously computed ones even when they have different dimensions and are learned via different neural network architectures and loss functions. Compatible means that, if such features are used to compare images, then "new" features can be compared directly to "old" features, so they can be used interchangeably. This enables visual search systems to bypass computing new features for all previously seen images when updating the embedding models, a process known as backfilling. Backward compatibility is critical to quickly deploy new embedding models that leverage ever-growing large-scale training datasets and improvements in deep learning architectures and training methods. We propose a framework to train embedding models, called backward-compatible training (BCT), as a first step towards backward compatible representation learning. In experiments on learning embeddings for face recognition, models trained with BCT successfully achieve backward compatibility without sacrificing accuracy, thus enabling backfill-free model updates of visual embeddings.
Global-Local GCN: Large-Scale Label Noise Cleansing for Face Recognition
Authors: Yaobin Zhang, Weihong Deng, Mei Wang, Jiani Hu, Xian Li, Dongyue Zhao, Dongchao Wen
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: In the field of face recognition, large-scale web-collected datasets are essential for learning discriminative representations, but they suffer from noisy identity labels, such as outliers and label flips. It is beneficial to automatically cleanse their label noise for improving recognition accuracy. Unfortunately, existing cleansing methods cannot accurately identify noise in the wild. To solve this problem, we propose an effective automatic label noise cleansing framework for face recognition datasets, FaceGraph. Using two cascaded graph convolutional networks, FaceGraph performs global-to-local discrimination to select useful data in a noisy environment. Extensive experiments show that cleansing widely used datasets, such as CASIA-WebFace, VGGFace2, MegaFace2, and MS-Celeb-1M, using the proposed method can improve the recognition performance of state-of-the-art representation learning methods like Arcface. Further, we cleanse massive self-collected celebrity data, namely MillionCelebs, to provide 18.8M images of 636K identities. Training with the new data, Arcface surpasses state-of-the-art performance by a notable margin to reach 95.62% TPR at 1e-5 FPR on the IJB-C benchmark.
Adaptive Graph Convolutional Network With Attention Graph Clustering for Co-Saliency Detection
Authors: Kaihua Zhang, Tengpeng Li, Shiwen Shen, Bo Liu, Jin Chen, Qingshan Liu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Co-saliency detection aims to discover the common and salient foregrounds from a group of relevant images. For this task, we present a novel adaptive graph convolutional network with attention graph clustering (GCAGC). Three major contributions have been made, and are experimentally shown to have substantial practical merits. First, we propose a graph convolutional network design to extract information cues to characterize the intra- and inter-image correspondence. Second, we develop an attention graph clustering algorithm to discriminate the common objects from all the salient foreground objects in an unsupervised fashion. Third, we present a unified framework with encoder-decoder structure to jointly train and optimize the graph convolutional network, attention graph cluster, and co-saliency detection decoder in an end-to-end manner. We evaluate our proposed GCAGC method on three co-saliency detection benchmark datasets (iCoseg, Cosal2015 and COCO-SEG). Our GCAGC method obtains significant improvements over the state-of-the-arts on most of them.
UniPose: Unified Human Pose Estimation in Single Images and Videos
Authors: Bruno Artacho, Andreas Savakis
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: We propose UniPose, a unified framework for human pose estimation, based on our "Waterfall" Atrous Spatial Pooling architecture, that achieves state-of-art-results on several pose estimation metrics. UniPose incorporates contextual segmentation and joint localization to estimate the human pose in a single stage, with high accuracy, without relying on statistical postprocessing methods. The Waterfall module in UniPose leverages the efficiency of progressive filtering in the cascade architecture, while maintaining multi-scale fields-of-view comparable to spatial pyramid configurations. Additionally, our method is extended to UniPose-LSTM for multi-frame processing and achieves state-of-the-art results for temporal pose estimation in Video. Our results on multiple datasets demonstrate that UniPose, with a ResNet backbone and Waterfall module, is a robust and efficient architecture for pose estimation obtaining state-of-the-art results in single person pose detection for both single images and videos.
Novel View Synthesis of Dynamic Scenes With Globally Coherent Depths From a Monocular Camera
Authors: Jae Shin Yoon, Kihwan Kim, Orazio Gallo, Hyun Soo Park, Jan Kautz
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: This paper presents a new method to synthesize an image from arbitrary views and times given a collection of images of a dynamic scene. A key challenge for the novel view synthesis arises from dynamic scene reconstruction where epipolar geometry does not apply to the local motion of dynamic contents. To address this challenge, we propose to combine the depth from single view (DSV) and the depth from multi-view stereo (DMV), where DSV is complete, i.e., a depth is assigned to every pixel, yet view-variant in its scale, while DMV is view-invariant yet incomplete. Our insight is that although its scale and quality are inconsistent with other views, the depth estimation from a single view can be used to reason about the globally coherent geometry of dynamic contents. We cast this problem as learning to correct the scale of DSV, and to refine each depth with locally consistent motions between views to form a coherent depth estimation. We integrate these tasks into a depth fusion network in a self-supervised fashion. Given the fused depth maps, we synthesize a photorealistic virtual view in a specific location and time with our deep blending network that completes the scene and renders the virtual view. We evaluate our method of depth estimation and view synthesis on a diverse real-world dynamic scenes and show the outstanding performance over existing methods.
Cogradient Descent for Bilinear Optimization
Authors: Li'an Zhuo, Baochang Zhang, Linlin Yang, Hanlin Chen, Qixiang Ye, David Doermann, Rongrong Ji, Guodong Guo
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Conventional learning methods simplify the bilinear model by regarding two intrinsically coupled factors independently, which degrades the optimization procedure. One reason lies in the insufficient training due to the asynchronous gradient descent, which results in vanishing gradients for the coupled variables. In this paper, we introduce a Cogradient Descent algorithm (CoGD) to address the bilinear problem, based on a theoretical framework to coordinate the gradient of hidden variables via a projection function. We solve one variable by considering its coupling relationship with the other, leading to a synchronous gradient descent to facilitate the optimization procedure. Our algorithm is applied to solve problems with one variable under the sparsity constraint, which is widely used in the learning paradigm. We validate our CoGD considering an extensive set of applications including image reconstruction, inpainting, and network pruning. Experiments show that it improves the state-of-the-art by a significant margin.
AdversarialNAS: Adversarial Neural Architecture Search for GANs
Authors: Chen Gao, Yunpeng Chen, Si Liu, Zhenxiong Tan, Shuicheng Yan
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Neural Architecture Search (NAS) that aims to automate the procedure of architecture design has achieved promising results in many computer vision fields. In this paper, we propose an AdversarialNAS method specially tailored for Generative Adversarial Networks (GANs) to search for a superior generative model on the task of unconditional image generation. The AdversarialNAS is the first method that can search the architectures of generator and discriminator simultaneously in a differentiable manner. During searching, the designed adversarial search algorithm does not need to comput any extra metric to evaluate the performance of the searched architecture, and the search paradigm considers the relevance between the two network architectures and improves their mutual balance. Therefore, AdversarialNAS is very efficient and only takes 1 GPU day to search for a superior generative model in the proposed large search space. Experiments demonstrate the effectiveness and superiority of our method. The discovered generative model sets a new state-of-the-art FID score of 10.87 and highly competitive Inception Score of 8.74 on CIFAR-10. Its transferability is also proven by setting new state-of-the-art FID score of 26.98 and Inception score of 9.63 on STL-10. Code is at: https://github.com/chengaopro/AdversarialNAS.
Belief Propagation Reloaded: Learning BP-Layers for Labeling Problems
Authors: Patrick Knobelreiter, Christian Sormann, Alexander Shekhovtsov, Friedrich Fraundorfer, Thomas Pock
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: It has been proposed by many researchers that combining deep neural networks with graphical models can create more efficient and better regularized composite models. The main difficulties in implementing this in practice are associated with a discrepancy in suitable learning objectives as well as with the necessity of approximations for the inference. In this work we take one of the simplest inference methods, a truncated max-product Belief Propagation, and add what is necessary to make it a proper component of a deep learning model: connect it to learning formulations with losses on marginals and compute the backprop operation. This BP-Layer can be used as the final or an intermediate block in convolutional neural networks (CNNs), allowing us to design a hierarchical model composing BP inference and CNNs at different scale levels. The model is applicable to a range of dense prediction problems, is well-trainable and provides parameter-efficient and robust solutions in stereo, flow and semantic segmentation.
DoveNet: Deep Image Harmonization via Domain Verification
Authors: Wenyan Cong, Jianfu Zhang, Li Niu, Liu Liu, Zhixin Ling, Weiyuan Li, Liqing Zhang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Image composition is an important operation in image processing, but the inconsistency between foreground and background significantly degrades the quality of composite image. Image harmonization, aiming to make the foreground compatible with the background, is a promising yet challenging task. However, the lack of high-quality publicly available dataset for image harmonization greatly hinders the development of image harmonization techniques. In this work, we contribute an image harmonization dataset iHarmony4 by generating synthesized composite images based on COCO (resp., Adobe5k, Flickr, day2night) dataset, leading to our HCOCO (resp., HAdobe5k, HFlickr, Hday2night) sub-dataset. Moreover, we propose a new deep image harmonization method DoveNet using a novel domain verification discriminator, with the insight that the foreground needs to be translated to the same domain as background. Extensive experiments on our constructed dataset demonstrate the effectiveness of our proposed method. Our dataset and code are available at https://github.com/bcmi/Image_Harmonization_Datasets.
Self-Supervised 3D Human Pose Estimation via Part Guided Novel Image Synthesis
Authors: Jogendra Nath Kundu, Siddharth Seth, Varun Jampani, Mugalodi Rakesh, R. Venkatesh Babu, Anirban Chakraborty
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Camera captured human pose is an outcome of several sources of variation. Performance of supervised 3D pose estimation approaches comes at the cost of dispensing with variations, such as shape and appearance, that may be useful for solving other related tasks. As a result, the learned model not only inculcates task-bias but also dataset-bias because of its strong reliance on the annotated samples, which also holds true for weakly-supervised models. Acknowledging this, we propose a self-supervised learning framework to disentangle such variations from unlabeled video frames. We leverage the prior knowledge on human skeleton and poses in the form of a single part-based 2D puppet model, human pose articulation constraints, and a set of unpaired 3D poses. Our differentiable formalization, bridging the representation gap between the 3D pose and spatial part maps, not only facilitates discovery of interpretable pose disentanglement, but also allows us to operate on videos with diverse camera movements. Qualitative results on unseen in-the-wild datasets establish our superior generalization across multiple tasks beyond the primary tasks of 3D pose estimation and part segmentation. Furthermore, we demonstrate state-of-the-art weakly-supervised 3D pose estimation performance on both Human3.6M and MPI-INF-3DHP datasets.
Self-Supervised Learning of Interpretable Keypoints From Unlabelled Videos
Authors: Tomas Jakab, Ankush Gupta, Hakan Bilen, Andrea Vedaldi
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: We propose a new method for recognizing the pose of objects from a single image that for learning uses only unlabelled videos and a weak empirical prior on the object poses. Video frames differ primarily in the pose of the objects they contain, so our method distils the pose information by analyzing the differences between frames. The distillation uses a new dual representation of the geometry of objects as a set of 2D keypoints, and as a pictorial representation, i.e. a skeleton image. This has three benefits: (1) it provides a tight 'geometric bottleneck' which disentangles pose from appearance, (2) it can leverage powerful image-to-image translation networks to map between photometry and geometry, and (3) it allows to incorporate empirical pose priors in the learning process. The pose priors are obtained from unpaired data, such as from a different dataset or modality such as mocap, such that no annotated image is ever used in learning the pose recognition network. In standard benchmarks for pose recognition for humans and faces, our method achieves state-of-the-art performance among methods that do not require any labelled images for training. Project page: http://www.robots.ox.ac.uk/ vgg/research/unsupervised_pose/
Distribution-Aware Coordinate Representation for Human Pose Estimation
Authors: Feng Zhang, Xiatian Zhu, Hanbin Dai, Mao Ye, Ce Zhu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: While being the de facto standard coordinate representation for human pose estimation, heatmap has not been investigated in-depth. This work fills this gap. For the first time, we find that the process of decoding the predicted heatmaps into the final joint coordinates in the original image space is surprisingly significant for the performance. We further probe the design limitations of the standard coordinate decoding method, and propose a more principled distributionaware decoding method. Also, we improve the standard coordinate encoding process (i.e. transforming ground-truth coordinates to heatmaps) by generating unbiased/accurate heatmaps. Taking the two together, we formulate a novel Distribution-Aware coordinate Representation of Keypoints (DARK) method. Serving as a model-agnostic plug-in, DARK brings about significant performance boost to existing human pose estimation models. Extensive experiments show that DARK yields the best results on two common benchmarks, MPII and COCO. Besides, DARK achieves the 2nd place entry in the ICCV 2019 COCO Keypoints Challenge. The code is available online.
Attention Mechanism Exploits Temporal Contexts: Real-Time 3D Human Pose Reconstruction
Authors: Ruixu Liu, Ju Shen, He Wang, Chen Chen, Sen-ching Cheung, Vijayan Asari
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: We propose a novel attention-based framework for 3D human pose estimation from a monocular video. Despite the general success of end-to-end deep learning paradigms, our approach is based on two key observations: (1) temporal incoherence and jitter are often yielded from a single frame prediction; (2) error rate can be remarkably reduced by increasing the receptive field in a video. Therefore, we design an attentional mechanism to adaptively identify significant frames and tensor outputs from each deep neural net layer, leading to a more optimal estimation. To achieve large temporal receptive fields, multi-scale dilated convolutions are employed to model long-range dependencies among frames. The architecture is straightforward to implement and can be flexibly adopted for real-time applications. Any off-the-shelf 2D pose estimation system, e.g. Mocap libraries, can be easily integrated in an ad-hoc fashion. We both quantitatively and qualitatively evaluate our method on various standard benchmark datasets (e.g. Human3.6M, HumanEva). Our method considerably outperforms all the state-of-the-art algorithms up to 8% error reduction (average mean per joint position error: 34.7) as compared to the best-reported results. Code is available at: (https://github.com/lrxjason/Attention3DHumanPose)
MaskFlownet: Asymmetric Feature Matching With Learnable Occlusion Mask
Authors: Shengyu Zhao, Yilun Sheng, Yue Dong, Eric I-Chao Chang, Yan Xu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Feature warping is a core technique in optical flow estimation; however, the ambiguity caused by occluded areas during warping is a major problem that remains unsolved. In this paper, we propose an asymmetric occlusion-aware feature matching module, which can learn a rough occlusion mask that filters useless (occluded) areas immediately after feature warping without any explicit supervision. The proposed module can be easily integrated into end-to-end network architectures and enjoys performance gains while introducing negligible computational cost. The learned occlusion mask can be further fed into a subsequent network cascade with dual feature pyramids with which we achieve state-of-the-art performance. At the time of submission, our method, called MaskFlownet, surpasses all published optical flow methods on the MPI Sintel, KITTI 2012 and 2015 benchmarks. Code is available at https://github.com/microsoft/MaskFlownet.
3FabRec: Fast Few-Shot Face Alignment by Reconstruction
Authors: Bjorn Browatzki, Christian Wallraven
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Current supervised methods for facial landmark detection require a large amount of training data and may suffer from overfitting to specific datasets due to the massive number of parameters. We introduce a semi-supervised method in which the crucial idea is to first generate implicit face knowledge from the large amounts of unlabeled images of faces available today. In a first, completely unsupervised stage, we train an adversarial autoencoder to reconstruct faces via a low-dimensional face embedding. In a second, supervised stage, we interleave the decoder with transfer layers to retask the generation of color images to the prediction of landmark heatmaps. Our framework (3FabRec) achieves state-of-the-art performance on several common benchmarks and, most importantly, is able to maintain impressive accuracy on extremely small training sets down to as few as 10 images. As the interleaved layers only add a low amount of parameters to the decoder, inference runs at several hundred FPS on a GPU.
MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction
Authors: Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: The ambiguity in image matching is one of main factors decreasing the quality of the 3D model reconstructed by PatchMatch based multiple view stereo. In this paper, we present a novel method, matching ambiguity reduced multiple view stereo (MARMVS) to address this issue. The MARMVS handles the ambiguity in image matching process with three newly proposed strategies: 1) The matching ambiguity is measured by the differential geometry property of image surface with epipolar constraint, which is used as a critical criterion for optimal scale selection of every single pixel with corresponding neighbouring images. 2) The depth of every pixel is initialized to be more close to the true depth by utilizing the depths of its surrounding sparse feature points, which yields faster convergency speed in the following PatchMatch stereo and alleviates the ambiguity introduced by self similar structures of the image. 3) In the last propagation of the PatchMatch stereo, higher priorities are given to those planes with the related 2D image patch possesses less ambiguity, this strategy further propagates a correctly reconstructed surface to raw texture regions. In addition, the proposed method is very efficient even running on consumer grade CPUs, due to proper parameterization and discretization in the depth map computation step. The MARMVS is validated on public benchmarks, and experimental results demonstrate competing performance against the state of the art.
Bodies at Rest: 3D Human Pose and Shape Estimation From a Pressure Image Using Synthetic Data
Authors: Henry M. Clever, Zackory Erickson, Ariel Kapusta, Greg Turk, Karen Liu, Charles C. Kemp
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: People spend a substantial part of their lives at rest in bed. 3D human pose and shape estimation for this activity would have numerous beneficial applications, yet line-of-sight perception is complicated by occlusion from bedding. Pressure sensing mats are a promising alternative, but training data is challenging to collect at scale. We describe a physics-based method that simulates human bodies at rest in a bed with a pressure sensing mat, and present PressurePose, a synthetic dataset with 206K pressure images with 3D human poses and shapes. We also present PressureNet, a deep learning model that estimates human pose and shape given a pressure image and gender. PressureNet incorporates a pressure map reconstruction (PMR) network that models pressure image generation to promote consistency between estimated 3D body models and pressure image input. In our evaluations, PressureNet performed well with real data from participants in diverse poses, even though it had only been trained with synthetic data. When we ablated the PMR network, performance dropped substantially.
Cars Can't Fly Up in the Sky: Improving Urban-Scene Segmentation via Height-Driven Attention Networks
Authors: Sungha Choi, Joanne T. Kim, Jaegul Choo
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: This paper exploits the intrinsic features of urban-scene images and proposes a general add-on module, called height-driven attention networks (HANet), for improving semantic segmentation for urban-scene images. It emphasizes informative features or classes selectively according to the vertical position of a pixel. The pixel-wise class distributions are significantly different from each other among horizontally segmented sections in the urban-scene images. Likewise, urban-scene images have their own distinct characteristics, but most semantic segmentation networks do not reflect such unique attributes in the architecture. The proposed network architecture incorporates the capability exploiting the attributes to handle the urban scene dataset effectively. We validate the consistent performance (mIoU) increase of various semantic segmentation models on two datasets when HANet is adopted. This extensive quantitative analysis demonstrates that adding our module to existing models is easy and cost-effective. Our method achieves a new state-of-the-art performance on the Cityscapes benchmark with a large margin among ResNet101 based segmentation models. Also, we show that the proposed model is coherent with the facts observed in the urban scene by visualizing and interpreting the attention map. Our code and trained models are publicly available.
Compressed Volumetric Heatmaps for Multi-Person 3D Pose Estimation
Authors: Matteo Fabbri, Fabio Lanzi, Simone Calderara, Stefano Alletto, Rita Cucchiara
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: In this paper we present a novel approach for bottom-up multi-person 3D human pose estimation from monocular RGB images. We propose to use high resolution volumetric heatmaps to model joint locations, devising a simple and effective compression method to drastically reduce the size of this representation. At the core of the proposed method lies our Volumetric Heatmap Autoencoder, a fully-convolutional network tasked with the compression of ground-truth heatmaps into a dense intermediate representation. A second model, the Code Predictor, is then trained to predict these codes, which can be decompressed at test time to re-obtain the original representation. Our experimental evaluation shows that our method performs favorably when compared to state of the art on both multi-person and single-person 3D human pose estimation datasets and, thanks to our novel compression strategy, can process full-HD images at the constant runtime of 8 fps regardless of the number of subjects in the scene. Code and models are publicly available.
3D-MPA: Multi-Proposal Aggregation for 3D Semantic Instance Segmentation
Authors: Francis Engelmann, Martin Bokeloh, Alireza Fathi, Bastian Leibe, Matthias Niessner
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: We present 3D-MPA, a method for instance segmentation on 3D point clouds. Given an input point cloud, we propose an object-centric approach where each point votes for its object center. We sample object proposals from the predicted object centers. Then, we learn proposal features from grouped point features that voted for the same object center. A graph convolutional network introduces inter-proposal relations, providing higher-level feature learning in addition to the lower-level point features. Each proposal comprises a semantic label, a set of associated points over which we define a foreground-background mask, an objectness score and aggregation features. Previous works usually perform non-maximum-suppression (NMS) over proposals to obtain the final object detections or semantic instances. However, NMS can discard potentially correct predictions. Instead, our approach keeps all proposals and groups them together based on the learned aggregation features. We show that grouping proposals improves over NMS and outperforms previous state-of-the-art methods on the tasks of 3D object detection and semantic instance segmentation on the ScanNetV2 benchmark and the S3DIS dataset.