mtybilly/Instruct2DS huggingface dataset:
TomoFluid: Reconstructing Dynamic Fluid From Sparse View Videos
Authors: Ramzi Idoughi,  Congli Wang,  Anthony Bennett,  Jianguo Du,  Scott Skeen,  William L. Roberts,  Peter Wonka,  Wolfgang Heidrich Guangming Zang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Visible light tomography is a promising and increasingly popular technique for fluid imaging. However, the use of a sparse number of viewpoints in the capturing setups makes the reconstruction of fluid flows very challenging. In this paper, we present a state-of-the-art 4D tomographic reconstruction framework that integrates several regularizers into a multi-scale matrix free optimization algorithm. In addition to existing regularizers, we propose two new regularizers for improved results: a regularizer based on view interpolation of projected images and a regularizer to encourage reprojection consistency. We demonstrate our method with extensive experiments on both simulated and real data.
Point Cloud Completion by Skip-Attention Network With Hierarchical Folding
Authors: Tianyang Li,  Zhizhong Han,  Yu-Shen Liu Xin Wen
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Point cloud completion aims to infer the complete geometries for missing regions of 3D objects from incomplete ones. Previous methods usually predict the complete point cloud based on the global shape representation extracted from the incomplete input. However, the global representation often suffers from the information loss of structure details on local regions of incomplete point cloud. To address this problem, we propose Skip-Attention Network (SA-Net) for 3D point cloud completion. Our main contributions lie in the following two-folds. First, we propose a skip-attention mechanism to effectively exploit the local structure details of incomplete point clouds during the inference of missing parts. The skip-attention mechanism selectively conveys geometric information from the local regions of incomplete point clouds for the generation of complete ones at different resolutions, where the skip-attention reveals the completion process in an interpretable way. Second, in order to fully utilize the selected geometric information encoded by skip-attention mechanism at different resolutions, we propose a novel structure-preserving decoder with hierarchical folding for complete shape generation. The hierarchical folding preserves the structure of complete point cloud generated in upper layer by progressively detailing the local regions, using the skip-attentioned geometry at the same resolution. We conduct comprehensive experiments on ShapeNet and KITTI datasets, which demonstrate that the proposed SA-Net outperforms the state-of-the-art point cloud completion methods.
Revisiting Knowledge Distillation via Label Smoothing Regularization
Authors: Francis EH Tay,  Guilin Li,  Tao Wang,  Jiashi Feng Li Yuan
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Knowledge Distillation (KD) aims to distill the knowledge of a cumbersome teacher model into a lightweight student model. Its success is generally attributed to the privileged information on similarities among categories provided by the teacher model, and in this sense, only strong teacher models are deployed to teach weaker students in practice. In this work, we challenge this common belief by following experimental observations: 1) beyond the acknowledgment that the teacher can improve the student, the student can also enhance the teacher significantly by reversing the KD procedure; 2) a poorly-trained teacher with much lower accuracy than the student can still improve the latter significantly. To explain these observations, we provide a theoretical analysis of the relationships between KD and label smoothing regularization. We prove that 1) KD is a type of learned label smoothing regularization and 2) label smoothing regularization provides a virtual teacher model for KD. From these results, we argue that the success of KD is not fully due to the similarity information between categories from teachers, but also to the regularization of soft targets, which is equally or even more important. Based on these analyses, we further propose a novel Teacher-free Knowledge Distillation (Tf-KD) framework, where a student model learns from itself or manually-designed regularization distribution. The Tf-KD achieves comparable performance with normal KD from a superior teacher, which is well applied when a stronger teacher model is unavailable. Meanwhile, Tf-KD is generic and can be directly deployed for training deep neural networks. Without any extra computation cost, Tf-KD achieves up to 0.65% improvement on ImageNet over well-established baseline models, which is superior to label smoothing regularization.
Modeling Biological Immunity to Adversarial Examples
Authors: Jocelyn Rego,  Yijing Watkins,  Garrett T. Kenyon Edward Kim
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: While deep learning continues to permeate through all fields of signal processing and machine learning, a critical exploit in these frameworks exists and remains unsolved. These exploits, or adversarial examples, are a type of signal attack that can change the output class of a classifier by perturbing the stimulus signal by an imperceptible amount. The attack takes advantage of statistical irregularities within the training data, where the added perturbations can move the image across deep learning decision boundaries. What is even more alarming is the transferability of these attacks to different deep learning models and architectures. This means a successful attack on one model has adversarial effects on other, unrelated models. In a general sense, adversarial attack through perturbations is not a machine learning vulnerability. Human and biological vision can also be fooled by various methods, i.e. mixing high and low frequency images together, by altering semantically related signals, or by sufficiently distorting the input signal. However, the amount and magnitude of such a distortion required to alter biological perception is at a much larger scale. In this work, we explored this gap through the lens of biology and neuroscience in order to understand the robustness exhibited in human perception. Our experiments show that by leveraging sparsity and modeling the biological mechanisms at a cellular level, we are able to mitigate the effect of adversarial alterations to the signal that have no perceptible meaning. Furthermore, we present and illustrate the effects of top-down functional processes that contribute to the inherent immunity in human perception in the context of exploiting these properties to make a more robust machine vision system.
Rethinking Differentiable Search for Mixed-Precision Neural Networks
Authors: Nuno Vasconcelos Zhaowei Cai
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Low-precision networks, with weights and activations quantized to low bit-width, are widely used to accelerate inference on edge devices. However, current solutions are uniform, using identical bit-width for all filters. This fails to account for the different sensitivities of different filters and is suboptimal. Mixed-precision networks address this problem, by tuning the bit-width to individual filter requirements. In this work, the problem of optimal mixed-precision network search (MPS) is considered. To circumvent its difficulties of discrete search space and combinatorial optimization, a new differentiable search architecture is proposed, with several novel contributions to advance the efficiency by leveraging the unique properties of the MPS problem. The resulting Efficient differentiable MIxed-Precision network Search (EdMIPS) method is effective at finding the optimal bit allocation for multiple popular networks, and can search a large model, e.g. Inception-V3, directly on ImageNet without proxy task in a reasonable amount of time. The learned mixed-precision networks significantly outperform their uniform counterparts.
Wavelet Synthesis Net for Disparity Estimation to Synthesize DSLR Calibre Bokeh Effect on Smartphones
Authors: Yingmao Li,  Kaimo Lin,  George Chen,  Seok-Jun Lee,  Jihwan Choi,  Youngjun Francis Yoo,  Michael O. Polley Chenchi Luo
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Modern smartphone cameras can match traditional DSLR cameras in many areas thanks to the introduction of camera arrays and multi-frame processing. Among all types of DSLR effects, the narrow depth of field (DoF) or so called bokeh probably arouses most interest. Today's smartphones try to overcome the physical lens and sensor limitations by introducing computational methods that utilize a depth map to synthesize the narrow DoF effect from all-in-focus images. However, a high quality depth map remains to be the key differentiator between computational bokeh and DSLR optical bokeh. Empowered by a novel wavelet synthesis network architecture, we have greatly narrowed the gap between DSLR and smartphone camera in terms of the bokeh more than ever before. We describe three key Modern smartphone cameras can match traditional digital single lens reflex (DSLR) cameras in many areas thanks to the introduction of camera arrays and multi-frame processing. Among all types of DSLR effects, the narrow depth of field (DoF) or so called bokeh probably arouses most interest. Today's smartphones try to overcome the physical lens and sensor limitations by introducing computational methods that utilize a depth map to synthesize the narrow DoF effect from all-in-focus images. However, a high quality depth map remains to be the key differentiator between computational bokeh and DSLR optical bokeh. Empowered by a novel wavelet synthesis network architecture, we have narrowed the gap between DSLR and smartphone camera in terms of bokeh more than ever before. We describe three key enablers of our bokeh solution: a synthetic graphics engine to generate training data with precisely prescribed characteristics that match the real smartphone captures, a novel wavelet synthesis neural network (WSN) architecture to produce unprecedented high definition disparity map promptly on smartphones, and a new evaluation metric to quantify the quality of the disparity map for real images from the bokeh rendering perspective. Experimental results show that the disparity map produced from our neural network achieves much better accuracy than the other state-of-the-art CNN based algorithms. Combining the high resolution disparity map with our rendering algorithm, we demonstrate visually superior bokeh pictures compared with existing top rated flagship smartphones listed on the DXOMARK mobiles.
Structure-Guided Ranking Loss for Single Image Depth Prediction
Authors: Jianming Zhang,  Oliver Wang,  Long Mai,  Zhe Lin,  Zhiguo Cao Ke Xian
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Single image depth prediction is a challenging task due to its ill-posed nature and challenges with capturing ground truth for supervision. Large-scale disparity data generated from stereo photos and 3D videos is a promising source of supervision, however, such disparity data can only approximate the inverse ground truth depth up to an affine transformation. To more effectively learn from such pseudo-depth data, we propose to use a simple pair-wise ranking loss with a novel sampling strategy. Instead of randomly sampling point pairs, we guide the sampling to better characterize structure of important regions based on the low-level edge maps and high-level object instance masks. We show that the pair-wise ranking loss, combined with our structure-guided sampling strategies, can significantly improve the quality of depth map prediction. In addition, we introduce a new relative depth dataset of about 21K diverse high-resolution web stereo photos to enhance the generalization ability of our model. In experiments, we conduct cross-dataset evaluation on six benchmark datasets and show that our method consistently improves over the baselines, leading to superior quantitative and qualitative results.
Perspective Plane Program Induction From a Single Image
Authors: Jiayuan Mao,  Xiuming Zhang,  William T. Freeman,  Joshua B. Tenenbaum,  Jiajun Wu Yikai Li
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: We study the inverse graphics problem of inferring a holistic representation for natural images. Given an input image, our goal is to induce a neuro-symbolic, program-like representation that jointly models camera poses, object locations, and global scene structures. Such high-level, holistic scene representations further facilitate low-level image manipulation tasks such as inpainting. We formulate this problem as jointly finding the camera pose and scene structure that best describe the input image. The benefits of such joint inference are two-fold: scene regularity serves as a new cue for perspective correction, and in turn, correct perspective correction leads to a simplified scene structure, similar to how the correct shape leads to the most regular texture in shape from texture. Our proposed framework, Perspective Plane Program Induction (P3I), combines search-based and gradient-based algorithms to efficiently solve the problem. P3I outperforms a set of baselines on a collection of Internet images, across tasks including camera pose estimation, global structure inference, and down-stream image manipulation tasks.
ActionBytes: Learning From Trimmed Videos to Localize Actions
Authors: Amir Ghodrati,  Cees G. M. Snoek Mihir Jain
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: This paper tackles the problem of localizing actions in long untrimmed videos. Different from existing works, which all use annotated untrimmed videos during training, we learn only from short trimmed videos. This enables learning from large-scale datasets originally designed for action classification. We propose a method to train an action localization network that segments a video into interpretable fragments, we call ActionBytes. Our method jointly learns to cluster ActionBytes and trains the localization network using the cluster assignments as pseudo-labels. By doing so, we train on short trimmed videos that become untrimmed for ActionBytes. In isolation, or when merged, the ActionBytes also serve as effective action proposals. Experiments demonstrate that our boundary-guided training generalizes to unknown action classes and localizes actions in long videos of Thumos14, MultiThumos, and ActivityNet1.2. Furthermore, we show the advantage of ActionBytes for zero-shot localization as well as traditional weakly supervised localization, that train on long videos, to achieve state-of-the-art results.
Conv-MPN: Convolutional Message Passing Neural Network for Structured Outdoor Architecture Reconstruction
Authors: Nelson Nauata,  Yasutaka Furukawa Fuyang Zhang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: This paper proposes a novel message passing neural (MPN) architecture Conv-MPN, which reconstructs an outdoor building as a planar graph from a single RGB image. Conv-MPN is specifically designed for cases where nodes of a graph have explicit spatial embedding. In our problem, nodes correspond to building edges in an image. Conv-MPN is different from MPN in that 1) the feature associated with a node is represented as a feature volume instead of a 1D vector; and 2) convolutions encode messages instead of fully connected layers. Conv-MPN learns to select a true subset of nodes (i.e., building edges) to reconstruct a building planar graph. Our qualitative and quantitative evaluations over 2,000 buildings show that Conv-MPN makes significant improvements over the existing fully neural solutions. We believe that the paper has a potential to open a new line of graph neural network research for structured geometry reconstruction.
Novel Object Viewpoint Estimation Through Reconstruction Alignment
Authors: Jason J. Corso,  David F. Fouhey Mohamed El Banani
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: The goal of this paper is to estimate the viewpoint for a novel object. Standard viewpoint estimation approaches generally fail on this task due to their reliance on a 3D model for alignment or large amounts of class-specific training data and their corresponding canonical pose. We overcome those limitations by learning a reconstruct and align approach. Our key insight is that although we do not have an explicit 3D model or a predefined canonical pose, we can still learn to estimate the object's shape in the viewer's frame and then use an image to provide our reference model or canonical pose. In particular, we propose learning two networks: the first maps images to a 3D geometry-aware feature bottleneck and is trained via an image-to-image translation loss; the second learns whether two instances of features are aligned. At test time, our model finds the relative transformation that best aligns the bottleneck features of our test image to a reference image. We evaluate our method on novel object viewpoint estimation by generalizing across different datasets, analyzing the impact of our different modules, and providing a qualitative analysis of the learned features to identify what representation is being learnt for alignment.
PaStaNet: Toward Human Activity Knowledge Engine
Authors: Liang Xu,  Xinpeng Liu,  Xijie Huang,  Yue Xu,  Shiyi Wang,  Hao-Shu Fang,  Ze Ma,  Mingyang Chen,  Cewu Lu Yong-Lu Li
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Existing image-based activity understanding methods mainly adopt direct mapping, i.e. from image to activity concepts, which may encounter performance bottleneck since the huge gap. In light of this, we propose a new path: infer human part states first and then reason out the activities based on part-level semantics. Human Body Part States (PaSta) are fine-grained action semantic tokens, e.g. , which can compose the activities and help us step toward human activity knowledge engine. To fully utilize the power of PaSta, we build a large-scale knowledge base PaStaNet, which contains 7M+ PaSta annotations. And two corresponding models are proposed: first, we design a model named Activity2Vec to extract PaSta features, which aim to be general representations for various activities. Second, we use a PaSta-based Reasoning method to infer activities. Promoted by PaStaNet, our method achieves significant improvements, e.g. 6.4 and 13.9 mAP on full and one-shot sets of HICO in supervised learning, and 3.2 and 4.2 mAP on V-COCO and images-based AVA in transfer learning. Code and data are available at http://hake-mvig.cn/.
Dynamic Fluid Surface Reconstruction Using Deep Neural Network
Authors: Nianyi Li,  Jinwei Ye Simron Thapa
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Recovering the dynamic fluid surface is a long-standing challenging problem in computer vision. Most existing image-based methods require multiple views or a dedicated imaging system. Here we present a learning-based single-image approach for 3D fluid surface reconstruction. Specifically, we design a deep neural network that estimates the depth and normal maps of a fluid surface by analyzing the refractive distortion of a reference background image. Due to the dynamic nature of fluid surfaces, our network uses recurrent layers that carry temporal information from previous frames to achieve spatio-temporally consistent reconstruction given a video input. Due to the lack of fluid data, we synthesize a large fluid dataset using physics-based fluid modeling and rendering techniques for network training and validation. Through experiments on simulated and real captured fluid images, we demonstrate that our proposed deep neural network trained on our fluid dataset can recover dynamic 3D fluid surfaces with high accuracy.
MPM: Joint Representation of Motion and Position Map for Cell Tracking
Authors: Kazuya Nishimura,  Ryoma Bise Junya Hayashida
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Conventional cell tracking methods detect multiple cells in each frame (detection) and then associate the detection results in successive time-frames (association). Most cell tracking methods perform the association task independently from the detection task. However, there is no guarantee of preserving coherence between these tasks, and lack of coherence may adversely affect tracking performance. In this paper, we propose the Motion and Position Map (MPM) that jointly represents both detection and association for not only migration but also cell division. It guarantees coherence such that if a cell is detected, the corresponding motion flow can always be obtained. It is a simple but powerful method for multi-object tracking in dense environments. We compared the proposed method with current tracking methods under various conditions in real biological images and found that it outperformed the state-of-the-art (+5.2% improvement compared to the second-best).
AdderNet: Do We Really Need Multiplications in Deep Learning?
Authors: Yunhe Wang,  Chunjing Xu,  Boxin Shi,  Chao Xu,  Qi Tian,  Chang Xu Hanting Chen
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Compared with cheap addition operation, multiplication operation is of much higher computation complexity. The widely-used convolutions in deep neural networks are exactly cross-correlation to measure the similarity between input feature and convolution filters, which involves massive multiplications between float values. In this paper, we present adder networks (AdderNets) to trade these massive multiplications in deep neural networks, especially convolutional neural networks (CNNs), for much cheaper additions to reduce computation costs. In AdderNets, we take the L1-norm distance between filters and input feature as the output response. The influence of this new similarity measure on the optimization of neural network have been thoroughly analyzed. To achieve a better performance, we develop a special back-propagation approach for AdderNets by investigating the full-precision gradient. We then propose an adaptive learning rate strategy to enhance the training procedure of AdderNets according to the magnitude of each neuron's gradient. As a result, the proposed AdderNets can achieve 74.9% Top-1 accuracy 91.7% Top-5 accuracy using ResNet-50 on the ImageNet dataset without any multiplication in convolutional layer. The codes are publicly available at: (https://github.com/huaweinoah/AdderNet).
Adaptive Interaction Modeling via Graph Operations Search
Authors: Wei-Shi Zheng,  Yu Tao,  Haifeng Hu,  Jian-Huang Lai Haoxin Li
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Interaction modeling is important for video action analysis. Recently, several works design specific structures to model interactions in videos. However, their structures are manually designed and non-adaptive, which require structures design efforts and more importantly could not model interactions adaptively. In this paper, we automate the process of structures design to learn adaptive structures for interaction modeling. We propose to search the network structures with differentiable architecture search mechanism, which learns to construct adaptive structures for different videos to facilitate adaptive interaction modeling. To this end, we first design the search space with several basic graph operations that explicitly capture different relations in videos. We experimentally demonstrate that our architecture search framework learns to construct adaptive interaction modeling structures, which provides more understanding about the relations between the structures and some interaction characteristics, and also releases the requirement of structures design efforts. Additionally, we show that the designed basic graph operations in the search space are able to model different interactions in videos. The experiments on two interaction datasets show that our method achieves competitive performance with state-of-the-arts.
Differentiable Volumetric Rendering: Learning Implicit 3D Representations Without 3D Supervision
Authors: Lars Mescheder,  Michael Oechsle,  Andreas Geiger Michael Niemeyer
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Learning-based 3D reconstruction methods have shown impressive results. However, most methods require 3D supervision which is often hard to obtain for real-world datasets. Recently, several works have proposed differentiable rendering techniques to train reconstruction models from RGB images. Unfortunately, these approaches are currently restricted to voxel- and mesh-based representations, suffering from discretization or low resolution. In this work, we propose a differentiable rendering formulation for implicit shape and texture representations. Implicit representations have recently gained popularity as they represent shape and texture continuously. Our key insight is that depth gradients can be derived analytically using the concept of implicit differentiation. This allows us to learn implicit shape and texture representations directly from RGB images. We experimentally show that our single-view reconstructions rival those learned with full 3D supervision. Moreover, we find that our method can be used for multi-view 3D reconstruction, directly resulting in watertight meshes.
Memory-Efficient Hierarchical Neural Architecture Search for Image Denoising
Authors: Ying Li,  Hao Chen,  Chunhua Shen Haokui Zhang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Recently, neural architecture search (NAS) methods have attracted much attention and outperformed manually designed architectures on a few high-level vision tasks. In this paper, we propose HiNAS (Hierarchical NAS), an effort towards employing NAS to automatically design effective neural network architectures for image denoising. HiNAS adopts gradient based search strategies and employs operations with adaptive receptive field to build an flexible hierarchical search space. During the search stage, HiNAS shares cells across different feature levels to save memory and employ an early stopping strategy to avoid the collapse issue in NAS, and considerably accelerate the search speed. The proposed HiNAS is both memory and computation efficient, which takes only about 4.5 hours for searching using a single GPU. We evaluate the effectiveness of our proposed HiNAS on two different datasets, namely an additive white Gaussian noise dataset BSD500, and a realistic noise datasetSIM1800. Experimental results show that the architecture found by HiNAS has fewer parameters and enjoys a faster inference speed, while achieving highly competitive performance compared with state-of-the-art methods. We also present analysis on the architectures found by NAS. HiNAS also shows good performance on experiments for image de-raining.
Embodied Language Grounding With 3D Visual Feature Representations
Authors: Hsiao-Yu Fish Tung,  Syed Ashar Javed,  Maximilian Sieb,  Adam W. Harley,  Katerina Fragkiadaki Mihir Prabhudesai
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: We propose associating language utterances to 3D visual abstractions of the scene they describe. The 3D visual abstractions are encoded as 3-dimensional visual feature maps. We infer these 3D visual scene feature maps from RGB images of the scene via view prediction: when the generated 3D scene feature map is neurally projected from a camera viewpoint, it should match the corresponding RGB image. We present generative models that condition on the dependency tree of an utterance and generate a corresponding visual 3D feature map as well as reason about its plausibility, and detector models that condition on both the dependency tree of an utterance and a related image and localize the object referents in the 3D feature map inferred from the image. Our model outperforms models of language and vision that associate language with 2D CNN activations or 2D images by a large margin in a variety of tasks, such as, classifying plausibility of utterances, detecting referential expressions, and supplying rewards for trajectory optimization of object placement policies from language instructions. We perform numerous ablations and show the improved performance of our detectors is due to its better generalization across camera viewpoints and lack of object interferences in the inferred 3D feature space, and the improved performance of our generators is due to their ability to spatially reason about objects and their configurations in 3D when mapping from language to scenes.
Cascade Cost Volume for High-Resolution Multi-View Stereo and Stereo Matching
Authors: Zhiwen Fan,  Siyu Zhu,  Zuozhuo Dai,  Feitong Tan,  Ping Tan Xiaodong Gu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: The deep multi-view stereo (MVS) and stereo matching approaches generally construct 3D cost volumes to regularize and regress the output depth or disparity. These methods are limited when high-resolution outputs are needed since the memory and time costs grow cubically as the volume resolution increases. In this paper, we propose a both memory and time efficient cost volume formulation that is complementary to existing multi-view stereo and stereo matching approaches based on 3D cost volumes. First, the proposed cost volume is built upon a standard feature pyramid encoding geometry and context at gradually finer scales. Then, we can narrow the depth (or disparity) range of each stage by the depth (or disparity) map from the previous stage. With gradually higher cost volume resolution and adaptive adjustment of depth (or disparity) intervals, the output is recovered in a coarser to fine manner. We apply the cascade cost volume to the representative MVS-Net, and obtain a 35.6% improvement on DTU benchmark (1st place), with 50.6% and 59.3% reduction in GPU memory and run-time. It is also the state-of-the-art learning-based method on Tanks and Temples benchmark. The statistics of accuracy, run-time and GPU memory on other representative stereo CNNs also validate the effectiveness of our proposed method. Our source code is available at https://github.com/alibaba/cascade-stereo.
All in One Bad Weather Removal Using Architectural Search
Authors: Robby T. Tan,  Loong-Fah Cheong Ruoteng Li
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Many methods have set state-of-the-art performance on restoring images degraded by bad weather such as rain, haze, fog, and snow, however they are designed specifically to handle one type of degradation. In this paper, we propose a method that can handle multiple bad weather degradations: rain, fog, snow and adherent raindrops using a single network. To achieve this, we first design a generator with multiple task-specific encoders, each of which is associated with a particular bad weather degradation type. We utilize a neural architecture search to optimally process the image features extracted from all encoders. Subsequently, to convert degraded image features to clean background features, we introduce a series of tensor-based operations encapsulating the underlying physics principles behind the formation of rain, fog, snow and adherent raindrops. These operations serve as the basic building blocks for our architectural search. Finally, our discriminator simultaneously assesses the correctness and classifies the degradation type of the restored image. We design a novel adversarial learning scheme that only backpropagates the loss of a degradation type to the respective task-specific encoder. Despite being designed to handle different types of bad weather, extensive experiments demonstrate that our method performs competitively to the individual and dedicated state-of-the-art image restoration methods.
Fast-MVSNet: Sparse-to-Dense Multi-View Stereo With Learned Propagation and Gauss-Newton Refinement
Authors: Shenghua Gao Zehao Yu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Almost all previous deep learning-based multi-view stereo (MVS) approaches focus on improving reconstruction quality. Besides quality, efficiency is also a desirable feature for MVS in real scenarios. Towards this end, this paper presents a Fast-MVSNet, a novel sparse-to-dense coarse-to-fine framework, for fast and accurate depth estimation in MVS. Specifically, in our Fast-MVSNet, we first construct a sparse cost volume for learning a sparse and high-resolution depth map. Then we leverage a small-scale convolutional neural network to encode the depth dependencies for pixels within a local region to densify the sparse high-resolution depth map. At last, a simple but efficient Gauss-Newton layer is proposed to further optimize the depth map. On one hand, the high-resolution depth map, the data-adaptive propagation method and the Gauss-Newton layer jointly guarantee the effectiveness of our method. On the other hand, all modules in our Fast-MVSNet are lightweight and thus guarantee the efficiency of our approach. Besides, our approach is also memory-friendly because of the sparse depth representation. Extensive experimental results show that our method is 5 times and 14 times faster than Point-MVSNet and R-MVSNet, respectively, while achieving comparable or even better results on the challenging Tanks and Temples dataset as well as the DTU dataset. Code is available at https://github.com/svip-lab/FastMVSNet.
Auxiliary Training: Towards Accurate and Robust Models
Authors: Muzhou Yu,  Tong Chen,  Zuoqiang Shi,  Chenglong Bao,  Kaisheng Ma Linfeng Zhang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Training process is crucial for the deployment of the network in applications which have two strict requirements on both accuracy and robustness. However, most existing approaches are in a dilemma, i.e. model accuracy and robustness form an embarrassing tradeoff - the improvement of one leads to the drop of the other. The challenge remains as for we try to improve the accuracy and robustness simultaneously. In this paper, we propose a novel training method via introducing the auxiliary classifiers for training on corrupted samples, while the clean samples are normally trained with the primary classifier. In the training stage, a novel distillation method named input-aware self distillation is proposed to facilitate the primary classifier to learn the robust information from auxiliary classifiers. Along with it, a new normalization method - selective batch normalization is proposed to prevent the model from the negative influence of corrupted images. At the end of training period, a L2-norm penalty is applied to the weights of primary and auxiliary classifiers such that their weights are asymptotically identical. In the stage of inference, only the primary classifier is used and thus no extra computation and storage are needed. Extensive experiments on CIFAR10, CIFAR100 and ImageNet show that noticeable improvements on both accuracy and robustness can be observed by the proposed auxiliary training. On average, auxiliary training achieves 2.21% accuracy and 21.64% robustness (measured by corruption error) improvements over traditional training methods on CIFAR100. Codes has been released on github.
Cascaded Human-Object Interaction Recognition
Authors: Wenguan Wang,  Siyuan Qi,  Haibin Ling,  Jianbing Shen Tianfei Zhou
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Rapid progress has been witnessed for human-object interaction (HOI) recognition, but most existing models are confined to single-stage reasoning pipelines. Considering the intrinsic complexity of the task, we introduce a cascade architecture for a multi-stage, coarse-to-fine HOI understanding. At each stage, an instance localization network progressively refines HOI proposals and feeds them into an interaction recognition network. Each of the two networks is also connected to its predecessor at the previous stage, enabling cross-stage information propagation. The interaction recognition network has two crucial parts: a relation ranking module for high-quality HOI proposal selection and a triple-stream classifier for relation prediction. With our carefully-designed human-centric relation features, these two modules work collaboratively towards effective interaction understanding. Further beyond relation detection on a bounding-box level, we make our framework flexible to perform fine-grained pixel-wise relation segmentation; this provides a new glimpse into better relation modeling. Our approach reached the 1^ st  place in the ICCV2019 Person in Context Challenge, on both relation detection and segmentation tasks. It also shows promising results on V-COCO.
Holistically-Attracted Wireframe Parsing
Authors: Tianfu Wu,  Song Bai,  Fudong Wang,  Gui-Song Xia,  Liangpei Zhang,  Philip H.S. Torr Nan Xue
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: This paper presents a fast and parsimonious parsing method to accurately and robustly detect a vectorized wireframe in an input image with a single forward pass. The proposed method is end-to-end trainable, consisting of three components: (i) line segment and junction proposal generation, (ii) line segment and junction matching, and (iii) line segment and junction verification. For computing line segment proposals, a novel exact dual representation is proposed which exploits a parsimonious geometric reparameterization for line segments and forms a holistic 4-dimensional attraction field map for an input image. Junctions can be treated as the "basins" in the attraction field. The proposed method is thus called Holistically-Attracted Wireframe Parser (HAWP). In experiments, the proposed method is tested on two benchmarks, the Wireframe dataset [14] and the YorkUrban dataset [8]. On both benchmarks, it obtains state-of-the-art performance in terms of accuracy and efficiency. For example, on the Wireframe dataset, compared to the previous state-of-the-art method L-CNN [36], it improves the challenging mean structural average precision (msAP) by a large margin (2.8% absolute improvements), and achieves 29.5 FPS on a single GPU (89% relative improvement). A systematic ablation study is performed to further justify the proposed method.
Strip Pooling: Rethinking Spatial Pooling for Scene Parsing
Authors: Li Zhang,  Ming-Ming Cheng,  Jiashi Feng Qibin Hou
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Spatial pooling has been proven highly effective to capture long-range contextual information for pixel-wise prediction tasks, such as scene parsing. In this paper, beyond conventional spatial pooling that usually has a regular shape of NxN, we rethink the formulation of spatial pooling by introducing a new pooling strategy, called strip pooling, which considers a long but narrow kernel, i.e., 1xN or Nx1. Based on strip pooling, we further investigate spatial pooling architecture design by 1) introducing a new strip pooling module that enables backbone networks to efficiently model long-range dependencies; 2) presenting a novel building block with diverse spatial pooling as a core; and 3) systematically comparing the performance of the proposed strip pooling and conventional spatial pooling techniques. Both novel pooling-based designs are lightweight and can serve as an efficient plug-and-play modules in existing scene parsing networks. Extensive experiments on Cityscapes and ADE20K benchmarks demonstrate that our simple approach establishes new state-of-the-art results. Code is available at https://github.com/Andrew-Qibin/SPNet.
OASIS: A Large-Scale Dataset for Single Image 3D in the Wild
Authors: Shengyi Qian,  David Fan,  Noriyuki Kojima,  Max Hamilton,  Jia Deng Weifeng Chen
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Single-view 3D is the task of recovering 3D properties such as depth and surface normals from a single image. We hypothesize that a major obstacle to single-image 3D is data. We address this issue by presenting Open Annotations of Single Image Surfaces (OASIS), a dataset for single-image 3D in the wild consisting of annotations of detailed 3D geometry for 140,000 images. We train and evaluate leading models on a variety of single-image 3D tasks. We expect OASIS to be a useful resource for 3D vision research. Project site: https://pvl.cs.princeton.edu/OASIS.
CONSAC: Robust Multi-Model Fitting by Conditional Sample Consensus
Authors: Eric Brachmann,  Hanno Ackermann,  Carsten Rother,  Michael Ying Yang,  Bodo Rosenhahn Florian Kluger
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: We present a robust estimator for fitting multiple parametric models of the same form to noisy measurements. Applications include finding multiple vanishing points in man-made scenes, fitting planes to architectural imagery, or estimating multiple rigid motions within the same sequence. In contrast to previous works, which resorted to hand-crafted search strategies for multiple model detection, we learn the search strategy from data. A neural network conditioned on previously detected models guides a RANSAC estimator to different subsets of all measurements, thereby finding model instances one after another. We train our method supervised, as well as, self-supervised. For supervised training of the search strategy, we contribute a new dataset for vanishing point estimation. Leveraging this dataset, the proposed algorithm is superior with respect to other robust estimators, as well as, to designated vanishing point estimation algorithms. For self-supervised learning of the search, we evaluate the proposed algorithm on multi-homography estimation and demonstrate an accuracy that is superior to state-of-the-art methods.
Deep Global Registration
Authors: Wei Dong,  Vladlen Koltun Christopher Choy
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: We present Deep Global Registration, a differentiable framework for pairwise registration of real-world 3D scans. Deep global registration is based on three modules: a 6-dimensional convolutional network for correspondence confidence prediction, a differentiable Weighted Procrustes algorithm for closed-form pose estimation, and a robust gradient-based SE(3) optimizer for pose refinement. Experiments demonstrate that our approach outperforms state-of-the-art methods, both learning-based and classical, on real-world data.
CycleISP: Real Image Restoration via Improved Data Synthesis
Authors: Aditya Arora,  Salman Khan,  Munawar Hayat,  Fahad Shahbaz Khan,  Ming-Hsuan Yang,  Ling Shao Syed Waqas Zamir
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: The availability of large-scale datasets has helped unleash the true potential of deep convolutional neural networks (CNNs). However, for the single-image denoising problem, capturing a real dataset is an unacceptably expensive and cumbersome procedure. Consequently, image denoising algorithms are mostly developed and evaluated on synthetic data that is usually generated with a widespread assumption of additive white Gaussian noise (AWGN). While the CNNs achieve impressive results on these synthetic datasets, they do not perform well when applied on real camera images, as reported in recent benchmark datasets. This is mainly because the AWGN is not adequate for modeling the real camera noise which is signal-dependent and heavily transformed by the camera imaging pipeline. In this paper, we present a framework that models camera imaging pipeline in forward and reverse directions. It allows us to produce any number of realistic image pairs for denoising both in RAW and sRGB spaces. By training a new image denoising network on realistic synthetic data, we achieve the state-of-the-art performance on real camera benchmark datasets. The parameters in our models are  5 times lesser than the previous best method for RAW denoising. Furthermore, we demonstrate that the proposed framework generalizes beyond image denoising problem e.g., for color matching in stereoscopic cinema. The source code and pre-trained models are available at https://github.com/swz30/CycleISP.
Neural Network Pruning With Residual-Connections and Limited-Data
Authors: Jianxin Wu Jian-Hao Luo
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Filter level pruning is an effective method to accelerate the inference speed of deep CNN models. Although numerous pruning algorithms have been proposed, there are still two open issues. The first problem is how to prune residual connections. We propose to prune both channels inside and outside the residual connections via a KL-divergence based criterion. The second issue is pruning with limited data. We observe an interesting phenomenon: directly pruning on a small dataset is usually worse than fine-tuning a small model which is pruned or trained from scratch on the large dataset. Knowledge distillation is an effective approach to compensate for the weakness of limited data. However, the logits of a teacher model may be noisy. In order to avoid the influence of label noise, we propose a label refinement approach to solve this problem. Experiments have demonstrated the effectiveness of our method (CURL, Compression Using Residual-connections and Limited-data). CURL significantly outperforms previous state-of-the-art methods on ImageNet. More importantly, when pruning on small datasets, CURL achieves comparable or much better performance than fine-tuning a pretrained small model.