mtybilly/Instruct2DS huggingface dataset:
Predicting Visual Overlap of Images Through Interpretable Non-Metric Box Embeddings
Authors: Anita Rau, Guillermo Garcia-Hernando, Danail Stoyanov, Gabriel J. Brostow, Daniyar Turmukhambetov
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: To what extent are two images picturing the same 3D surfaces? Even when this is a known scene, the answer typically requires an expensive search across scale space, with matching and geometric verification of large sets of local features. This expense is further multiplied when a query image is evaluated against a gallery, e.g. in visual relocalization. While we don't obviate the need for geometric verification, we propose an interpretable image-embedding that cuts the search in scale space to essentially a lookup.Our approach measures the asymmetric distance between two images. The model then learns a scene-specific measure of similarity, from training examples with known 3D visible-surface overlaps. The result is that we can quickly identify, for example, which test image is a close-up version of another, and by what scale factor. Subsequently, local features need only be detected at that scale. We validate our scene-specific model by showing how this embedding yields competitive image-matching results, while being simpler, faster, and also interpretable by humans."
Connecting Vision and Language with Localized Narratives
Authors: Jordi Pont-Tuset, Jasper Uijlings, Soravit Changpinyo, Radu Soricut, Vittorio Ferrari
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: We propose Localized Narratives, a new form of multimodal image annotations connecting vision and language. We ask annotators to describe an image with their voice while simultaneously hovering their mouse over the region they are describing. Since the voice and the mouse pointer are synchronized, we can localize every single word in the description. This dense visual grounding takes the form of a mouse trace segment per word and is unique to our data. We annotated 849k images with Localized Narratives: the whole COCO, Flickr30k, and ADE20K datasets, and 671k images of Open Images, all of which we make publicly available. We provide an extensive analysis of these annotations showing they are diverse, accurate, and efficient to produce. We also demonstrate their utility on the application of controlled image captioning."
Adversarial T-shirt! Evading Person Detectors in A Physical World
Authors: Kaidi Xu, Gaoyuan Zhang, Sijia Liu, Quanfu Fan, Mengshu Sun, Hongge Chen, Pin-Yu Chen, Yanzhi Wang, Xue Lin
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: It is known that deep neural networks (DNNs) are vulnerable to adversarial attacks. The so-called physical adversarial examples deceive DNN-based decision makers by attaching adversarial patches to real objects. However, most of the existing works on physical adversarial attacks focus on static objects such as glass frames, stop signs and images attached to cardboard. In this work, we proposed adversarial T-shirts, a robust physical adversarial example for evading person detectors even if it could undergo non-rigid deformation due to a moving person’s pose changes. To the best of our knowledge, this is the first work that models the effect of deformation for designing physical adversarial examples with respect to non-rigid objects such as T-shirts. We show that the proposed method achieves 74% and 57% attack success rates in the digital and physical worlds respectively against YOLOv2. In contrast, the state-of-the-art physical attack method to fool a person detector only achieves 18% attack success rate. Furthermore, by leveraging min-max optimization, we extend our method to the ensemble attack setting against two object detectors YOLO-v2 and Faster R-CNN simultaneously."
Bounding-box Channels for Visual Relationship Detection
Authors: Sho Inayoshi, Keita Otani, Antonio Tejero-de-Pablos, Tatsuya Harada
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Recognizing the relationship between multiple objects in an image is essential for a deeper understanding of the meaning of the image. However, current visual recognition methods are still far from reaching human-level accuracy. Recent approaches have tackled this task by combining image features with semantic and spatial features, but the way they relate them to each other is weak, mostly because the spatial context in the image feature is lost. In this paper, we propose the bounding-box channels, a novel architecture capable of relating the semantic, spatial, and image features strongly. Our network learns bounding-box channels, which are initialized according to the position of the objects and the label of objects, and concatenated to the image features extracted from the objects. Then, they are input together to the relationship estimator. Our method can retain the spatial information in the image features, and strongly associate them with the semantic and spatial features. This way, our method is capable of effectively emphasizing the features in the object area for a better modeling of the relationships within objects. In addition, we experimentally show that our bounding-box channels have a high generalization ability. Our evaluation results show the efficacy of our architecture outperforming previous works in visual relationship detection. "
Minimal Rolling Shutter Absolute Pose with Unknown Focal Length and Radial Distortion
Authors: Zuzana Kukelova, Cenek Albl, Akihiro Sugimoto, Konrad Schindler, Tomas Pajdla
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: The internal geometry of most modern consumer cameras is not adequately described by the perspective projection. Almost all cameras exhibit some radial lens distortion and are equipped with electronic rolling shutter that induces distortions when the camera moves during the image capture. When focal length has not been calibrated offline, the parameters that describe the radial and rolling shutter distortions are usually unknown. While for global shutter cameras, minimal solvers for the absolute camera pose and unknown focal length and radial distortion are available, solvers for the rolling shutter were missing. We present the first minimal solutions for the absolute pose of a rolling shutter camera with unknown rolling shutter parameters, focal length, and radial distortion. Our new minimal solvers combine iterative schemes designed for calibrated rolling shutter cameras with fast generalized eigenvalue and Groebner basis solvers. In a series of experiments, with both synthetic and real data, we show that our new solvers provide accurate estimates of the camera pose, rolling shutter parameters, focal length, and radial distortion parameters."
SRFlow: Learning the Super-Resolution Space with Normalizing Flow
Authors: Andreas Lugmayr, Martin Danelljan, Luc Van Gool, Radu Timofte
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Super-resolution is an ill-posed problem, since it allows for multiple predictions for a given low-resolution image. This fundamental fact is largely ignored by state-of-the-art deep learning based approaches. These methods instead train a deterministic mapping using combinations of reconstruction and adversarial losses. In this work, we therefore propose SRFlow: a normalizing flow based super-resolution method capable of learning the conditional distribution of the output given the low-resolution input. Our model is trained in a principled manner using a single loss, namely the negative log-likelihood. SRFlow therefore directly accounts for the ill-posed nature of the problem, and learns to predict diverse photo-realistic high-resolution images. Moreover, we utilize the strong image posterior learned by SRFlow to design flexible image manipulation techniques, capable of enhancing super-resolved images by, e.g., transferring content from other images. We perform extensive experiments on faces, as well as on super-resolution in general. SRFlow outperforms state-of-the-art GAN-based approaches in terms of both PSNR and perceptual quality metrics, while allowing for diversity through the exploration of the space of super-resolved solutions."
DeepGMR: Learning Latent Gaussian Mixture Models for Registration
Authors: Wentao Yuan, Benjamin Eckart, Kihwan Kim, Varun Jampani, Dieter Fox, Jan Kautz
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Point cloud registration is a fundamental problem in 3D computer vision, graphics and robotics. For the last few decades, existing registration algorithms have struggled in situations with large transformations, noise, and time constraints. In this paper, we introduce Deep Gaussian Mixture Registration (DeepGMR), the first learning-based registration method that explicitly leverages a probabilistic registration paradigm by formulating registration as the minimization of KL-divergence between two probability distributions modeled as mixtures of Gaussians. We design a neural network that extracts pose-invariant correspondences between raw point clouds and Gaussian Mixture Model (GMM) parameters and two differentiable compute blocks that recover the optimal transformation from matched GMM parameters. This construction allows the network learn an SE(3)-invariant feature space, producing a global registration method that is real-time, generalizable, and robust to noise. Across synthetic and real-world data, our proposed method shows favorable performance when compared with state-of-the-art geometry-based and learning-based registration methods."
Active Perception using Light Curtains for Autonomous Driving
Authors: Siddharth Ancha, Yaadhav Raaj, Peiyun Hu, Srinivasa G. Narasimhan, David Held
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Most real-world 3D sensors such as LiDARs are passive, meaning that they sense the entire environment, while being decoupled from the recognition system that processes the sensor data. In this work, we propose a method for 3D object recognition using light curtains, a resource-efficient active sensor that measures depth at selected locations in the environment in a controllable manner. Crucially, we propose using prediction uncertainty of a deep learning based 3D point cloud detector to guide active sensing. Given a neural network's uncertainty, we develop a novel optimization algorithm to optimally place light curtains to maximize coverage of uncertain regions. Efficient optimization is achieved by encoding the physical constraints of the device into a constraint graph, which is optimized with dynamic programming. We show how a 3D detector can be trained to detect objects in a scene by sequentially placing uncertainty-guided light curtains to successively improve detection accuracy."
Invertible Neural BRDF for Object Inverse Rendering
Authors: Zhe Chen, Shohei Nobuhara, Ko Nishino
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: We introduce a novel neural network-based BRDF model and a Bayesian framework for object inverse rendering, i.e., joint estimation of reflectance and natural illumination from a single image of an object of known geometry. The BRDF is expressed with an invertible neural network, namely, normalizing flow, which provides the expressive power of a high-dimensional representation, computational simplicity of a compact analytical model, and physical plausibility of a real-world BRDF. We extract the latent space of real-world reflectance by conditioning this model, which directly results in a strong reflectance prior. We refer to this model as the invertible neural BRDF model (iBRDF). We also devise a deep illumination prior by leveraging the structural bias of deep neural networks. By integrating this novel BRDF model and reflectance and illumination priors in a MAP estimation formulation, we show that this joint estimation can be computed efficiently with stochastic gradient descent. We experimentally validate the accuracy of the invertible neural BRDF model on a large number of measured data and demonstrate its use in object inverse rendering on a number of synthetic and real images. The results show new ways in which deep neural networks can help solve challenging radiometric inverse problems."
Semi-supervised Semantic Segmentation via Strong-weak Dual-branch Network
Authors: Wenfeng Luo, Meng Yang
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: While existing works have explored a variety of techniques to push the envelop of weakly-supervised semantic segmentation, there is still a significant gap compared to the supervised methods. In real-world application, besides massive amount of weakly-supervised data there are usually a few available pixel-level annotations, based on which semi-supervised track becomes a promising way for semantic segmentation. Current methods simply bundle these two different sets of annotations together to train a segmentation network. However, we discover that such treatment is problematic and achieves even worse results than just using strong labels, which indicates the misuse of the weak ones. To fully explore the potential of the weak labels, we propose to impose separate treatments of strong and weak annotations via a strong-weak dual-branch network, which discriminates the massive inaccurate weak supervisions from those strong ones. We design a shared network component to exploit the joint discrimination of strong and weak annotations; meanwhile, the proposed dual branches separately handle full and weak supervised learning and effectively eliminate their mutual interference. This simple architecture requires only slight additional computational costs during training yet brings significant improvements over the previous methods. Experiments on two standard benchmark datasets show the effectiveness of the proposed method."
Practical Deep Raw Image Denoising on Mobile Devices
Authors: Yuzhi Wang, Haibin Huang, Qin Xu, Jiaming Liu, Yiqun Liu, Jue Wang
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Deep learning-based image denoising approaches have been extensively studied in recent years, prevailing in many public benchmark datasets. However, the stat-of-the-art networks are computationally too expensive to be directly applied on mobile devices. In this work, we propose a light-weight, efficient neural network-based raw image denoiser that runs smoothly on mainstream mobile devices, and produces high quality denoising results. Our key insights are twofold:(1) by measuring and estimating sensor noise level, a smaller network trained on synthetic sensor-specific data can out-perform larger ones trained on general data;(2) the large noise level variation under different ISO settings can be removed by a novel k-Sigma Transform, allowing a small network to efficiently handle a wide range of noise levels. We conduct extensive experiments to demonstrate the efficiency and accuracy of our approach. Our proposed mobile-friendly denoising model runs at ~70 milliseconds per megapixel on Qualcomm Snapdragon 855 chipset, and it is the basis of the night shot feature of several flagship smartphones released in 2019. "
SoundSpaces: Audio-Visual Navigation in 3D Environments
Authors: Changan Chen, Unnat Jain, Carl Schissler, Sebastia Vicenc Amengual Gari, Ziad Al-Halah, Vamsi Krishna Ithapu, Philip Robinson, and Kristen Grauman
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Moving around in the world is naturally a multi-sensory experience, but today's embodied agents are deaf - restricted to solely their visual perception of the environment. We introduce audio-visual navigation for complex, acoustically and visually realistic 3D environments. By both seeing and hearing, the agent must learn to navigate to an audio-based target. We develop a multi-modal deep reinforcement learning pipeline to train navigation policies end-to-end from a stream of egocentric audio-visual observations, allowing the agent to (1) discover elements of the geometry of the physical space indicated by the reverberating audio and (2) detect and follow sound-emitting targets. We further introduce audio renderings based on geometrical acoustic simulations for a set of publicly available 3D assets and instrument AI-Habitat to support the new sensor, making it possible to insert arbitrary sound sources in an array of apartment, office, and hotel environments. Our results show that audio greatly benefits embodied visual navigation in 3D spaces."
Two-Stream Consensus Network for Weakly-Supervised Temporal Action Localization
Authors: Yuanhao Zhai, Le Wang, Wei Tang, Qilin Zhang, Junsong Yuan, Gang Hua
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Weakly-supervised Temporal Action Localization (W-TAL) aims to classify and localize all action instances in an untrimmed video under only video-level supervision. However, without frame-level annotations, it is challenging for W-TAL methods to identify false positive action proposals and generate action proposals with precise temporal boundaries. In this paper, we present a Two-Stream Consensus Network (TSCN) to simultaneously address these challenges. The proposed TSCN features an iterative refinement training method, where a frame-level pseudo ground truth is iteratively updated, and used to provide frame-level supervision for improved model training and false positive action proposal elimination. Furthermore, we propose a new attention normalization loss to encourage the predicted attention to act like a binary selection, and promote the precise localization of action instance boundaries. Experiments conducted on the THUMOS14 and ActivityNet datasets show that the proposed TSCN outperforms current state-of-the-art methods, and even achieves comparable results with some recent fully-supervised methods."
Erasing Appearance Preservation in Optimization-based Smoothing
Authors: Lvmin Zhang, Chengze Li, Yi JI, Chunping Liu, Tien-tsin Wong
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Optimization-based image smoothing is routinely formulated as the game between a smoothing energy and an appearance preservation energy. Achieving adequate smoothing is a fundamental goal of these image smoothing algorithms. We show that partially ""erasing"" the appearance preservation facilitate adequate image smoothing. In this paper, we call this manipulation as Erasing Appearance Preservation (EAP). We conduct an user study, allowing users to indicate the ""erasing"" positions by drawing scribbles interactively, to verify the correctness and effectiveness of EAP. We observe the characteristics of human-indicated ""erasing"" positions, and then formulate a simple and effective 0-1 knapsack to automatically synthesize the ""erasing"" positions. We test our synthesized erasing positions in a majority of image smoothing methods. Experimental results and large-scale perceptual human judgments show that the EAP solution tends to encourage the pattern separation or elimination capabilities of image smoothing algorithms. We further study the performance of the EAP solution in many image decomposition problems to decompose textures, shadows, and the challenging specular reflections. We also present examinations of diversiform image manipulation applications like texture removal, retexturing, intrinsic decomposition, layer extraction, recoloring, material manipulation, etc. Due to the widespread applicability of image smoothing, the EAP is also likely to be used in more image editing applications."
Counterfactual Vision-and-Language Navigation via Adversarial Path Sampler
Authors: Tsu-Jui Fu, Xin Eric Wang, Matthew F. Peterson, Scott T. Grafton, Miguel P. Eckstein, William Yang Wang
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Vision-and-Language Navigation (VLN) is a task where agents must decide how to move through a 3D environment to reach a goal by grounding natural language instructions to the visual surroundings. One of the problems of the VLN task is data scarcity since it is difficult to collect enough navigation paths with human-annotated instructions for interactive environments. In this paper, we explore the use of counterfactual thinking as a human-inspired data augmentation method that results in robust models. Counterfactual thinking is a concept that describes the human propensity to create possible alternatives to life events that have already occurred. We propose an adversarial-driven counterfactual reasoning model that can consider effective conditions instead of low-quality augmented data. In particular, we present a model-agnostic adversarial path sampler (APS) that learns to sample challenging paths that force the navigator to improve based on the navigation performance. APS also serves to do pre-exploration of unseen environments to strengthen the model's ability to generalize. We evaluate the influence of APS on the performance of different VLN baseline models using the room-to-room dataset (R2R). The results show that the adversarial training process with our proposed APS benefits VLN models under both seen and unseen environments. And the pre-exploration process can further gain additional improvements under unseen environments."
Guided Deep Decoder: Unsupervised Image Pair Fusion
Authors: Tatsumi Uezato, Danfeng Hong, Naoto Yokoya, Wei He
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: The fusion of input and guidance images that have a tradeoff in their information (e.g., hyperspectral and RGB image fusion or pansharpening) can be interpreted as one general problem. However, previous studies applied a task-specific handcrafted prior and did not address the problems with a unified approach. To address this limitation, in this study, we propose a guided deep decoder network as a general prior. The proposed network is composed of an encoder-decoder network that exploits multi-scale features of a guidance image and a deep decoder network that generates an output image. The two networks are connected by feature refinement units to embed the multi-scale features of the guidance image into the deep decoder network. The proposed network allows the network parameters to be optimized in an unsupervised way without training data. Our results show that the proposed network can achieve state-of-the-art performance in various image fusion problems."
Filter Style Transfer between Photos
Authors: Jonghwa Yim, Jisung Yoo, Won-joon Do, Beomsu Kim, Jihwan Choe
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Over the past few years, image-to-image style transfer has risen to the frontiers of neural image processing. While conventional methods were successful in various tasks such as color and texture transfer between images, none could effectively work with the custom filter effects that are applied by users through various platforms like Instagram. In this paper, we introduce a new concept of style transfer, Filter Style Transfer (FST). Unlike conventional style transfer, new technique FST can extract and transfer custom filter style from a filtered style image to a content image. FST first infers the original image from a filtered reference via image-to-image translation. Then it estimates filter parameters from the difference between them. To resolve the ill-posed nature of reconstructing the original image from the reference, we represent each pixel color of an image to class mean and deviation. Besides, to handle the intra-class color variation, we propose an uncertainty based weighted least square method for restoring an original image. To the best of our knowledge, FST is the first style transfer method that can transfer custom filter effects between FHD image under 2ms on a mobile device without any textual context loss."
JGR-P2O: Joint Graph Reasoning based Pixel-to-Offset Prediction Network for 3D Hand Pose Estimation from a Single Depth Image
Authors: Linpu Fang, Xingyan Liu, Li Liu, Hang Xu, Wenxiong Kang
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: State-of-the-art single depth image-based 3D hand pose estimation methods are based on dense predictions, including voxel-to-voxel predictions, point-to-point regression, and pixel-wise estimations. Despite the good performance, those methods have a few issues in nature, such as the poor trade-off between accuracy and efficiency, and plain feature representation learning with local convolutions. In this paper, a novel pixel-wise prediction-based method is proposed to address the above issues. The key ideas are two-fold: a) explicitly modeling the dependencies among joints and the relations between the pixels and the joints for better local feature representation learning; b) unifying the dense pixel-wise offset predictions and direct joint regression for end-to-end training. Specifically, we first propose a graph convolutional network (GCN) based joint graph reasoning module to model the complex dependencies among joints and augment the representation capability of each pixel. Then we densely estimate all pixels' offsets to joints in both image plane and depth space and calculate the joints' positions by a weighted average over all pixels' predictions, totally discarding the complex post-processing operations. The proposed model is implemented with an efficient 2D fully convolutional network (FCN) backbone and has only about 1.4M parameters. Extensive experiments on multiple 3D hand pose estimation benchmarks demonstrate that the proposed method achieves new state-of-the-art accuracy while running very efficiently with around a speed of 110fps on a single NVIDIA 1080Ti GPU."
Dynamic Group Convolution for Accelerating Convolutional Neural Networks
Authors: Zhuo Su, Linpu Fang, Wenxiong Kang, Dewen Hu, Matti Pietikäinen, Li Liu
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Replacing normal convolutions with group convolutions can significantly increase the computational efficiency of modern deep convolutional networks, which has been widely adopted in compact network architecture designs. However, existing group convolutions undermine the original network structures by cutting off some connections permanently resulting in significant accuracy degradation. In this paper, we propose dynamic group convolution (DGC) that adaptively selects which part of input channels to be connected within each group for individual samples on the fly. Specifically, we equip each group with a small feature selector to automatically select the most important input channels conditioned on the input images. Multiple groups can adaptively capture abundant and complementary visual/semantic features for each input image. The DGC preserves the original network structure and has similar computational efficiency as the conventional group convolution simultaneously. Extensive experiments on multiple image classification benchmarks including CIFAR-10, CIFAR-100 and ImageNet demonstrate its superiority over the existing group convolution techniques and dynamic execution methods. The code is available at https://github.com/zhuogege1943/dgc."
RD-GAN: Few/Zero-Shot Chinese Character Style Transfer via Radical Decomposition and Rendering
Authors: Yaoxiong Huang, Mengchao He, Lianwen Jin, Yongpan Wang
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Style transfer has attracted much interest owing to its various applications. Compared with English character or general artistic style transfer, Chinese character style transfer remains a challenge owing to the large size of the vocabulary(70224 characters in GB18010-2005) and the complexity of the structure. Recently some GAN-based methods were proposed for style transfer; however, they treated Chinese characters as a whole, ignoring the structures and radicals that compose characters. In this paper, a novel radical decomposition-and-rendering-based GAN(RD-GAN) is proposed to utilize the radical-level compositions of Chinese characters and achieves few-shot/zero-shot Chinese character style transfer. The RD-GAN consists of three components: a radical extraction module (REM), radical rendering module (RRM), and multi-level discriminator (MLD). Experiments demonstrate that our method has a powerful few-shot/zero-shot generalization ability by using the radical-level compositions of Chinese characters."
Object-Contextual Representations for Semantic Segmentation
Authors: Yuhui Yuan, Xilin Chen, Jingdong Wang
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: In this paper, we address the semantic segmentation problem with a focus on the context aggregation strategy. Our motivation is that the label of a pixel is the category of the object that the pixel belongs to. We present a simple yet effective approach, object-contextual representations, characterizing a pixel by exploiting the representation of the corresponding object class. First, we learn object regions under the supervision of the ground-truth segmentation. Second, we compute the object region representation by aggregating the representations of the pixels lying in the object region. Last, % the representation similarity we compute the relation between each pixel and each object region, and augment the representation of each pixel with the object-contextual representation which is a weighted aggregation of all the object region representations according to their relations with the pixel. We empirically demonstrate that the proposed approach achieves competitive performance on various challenging semantic segmentation benchmarks: Cityscapes, ADE20K, LIP, PASCAL-Context, and COCO-Stuff. Cityscapes, ADE20K, LIP, PASCAL-Context and COCO-Stuff. Our submission ""HRNet + OCR + SegFix"" achieves the 1-st place on the Cityscapes leaderboard by the time of submission. "
Efficient Spatio-Temporal Recurrent Neural Network for Video Deblurring
Authors: Zhihang Zhong, Ye Gao, Yinqiang Zheng, Bo Zheng
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Real-time video deblurring still remains a challenging task due to the complexity of spatially and temporally varying blur itself and the requirement of low computational cost. To improve the network efficiency, we adopt residual dense blocks into RNN cells, so as to efficiently extract the spatial features of the current frame. Furthermore, a global spatio-temporal attention module is proposed to fuse the effective hierarchical features from past and future frames to help better deblur the current frame. For evaluation, we also collect a novel dataset with paired blurry/sharp video clips by using a co-axis beam splitter system. Through experiments on synthetic and realistic datasets, we show that our proposed method can achieve better deblurring performance both quantitatively and qualitatively with less computational cost against state-of-the-art video deblurring methods. "
Joint Semantic Instance Segmentation on Graphs with the Semantic Mutex Watershed
Authors: Steffen Wolf, Yuyan Li, Constantin Pape, Alberto Bailoni, Anna Kreshuk, Fred A. Hamprecht
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Semantic instance segmentation is the task of simultaneously partitioning an image into distinct segments while associating each pixel with a class label. In commonly used pipelines, segmentation and label assignment are solved separately since joint optimization is computationally expensive. We propose a greedy algorithm for joint graph partitioning and labeling derived from the efficient Mutex Watershed partitioning algorithm. It optimizes an objective function closely related to the Symmetric Multiway Cut objective and empirically shows efficient scaling behavior. Due to the algorithm's efficiency, it can operate directly on pixels without prior over-segmentation of the image into superpixels. We evaluate the performance on the Cityscapes dataset (2D urban scenes) and on a 3D microscopy volume. In urban scenes, the proposed algorithm combined with current deep neural networks outperforms the strong baseline of `Panoptic Feature Pyramid Networks' by Kirillov et al. (2019). In the 3D electron microscopy images, we show explicitly that our joint formulation outperforms a separate optimization of the partitioning and labeling problems. "
Photon-Efficient 3D Imaging with A Non-Local Neural Network
Authors: Jiayong Peng, Zhiwei Xiong, Xin Huang, Zheng-Ping Li, Dong Liu, Feihu Xu
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Photon-efficient imaging has enabled a number of applications relying on single-photon sensors that can capture a 3D image with as few as one photon per pixel. In practice, however, measurements of low photon counts are often mixed with heavy background noise, which poses a great challenge for existing computational reconstruction algorithms. In this paper, we first analyze the long-range correlations in both spatial and temporal dimensions of the measurements. Then we propose a non-local neural network for depth reconstruction by exploiting the long-range correlations. The proposed network achieves decent reconstruction fidelity even under photon counts (and signal-to-background ratio, SBR) as low as 1 photon/pixel (and 0.01 SBR), which significantly surpasses the state-of-the-art. Moreover, our non-local network trained on simulated data can be well generalized to different real-world imaging systems, which could extend the application scope of photon-efficient imaging in challenging scenarios with a strict limit on optical flux. Code is available at https://github.com/JiayongO-O/PENonLocal."
GeLaTO: Generative Latent Textured Objects
Authors: Ricardo Martin-Brualla, Rohit Pandey, Sofien Bouaziz, Matthew Brown, Dan B Goldman
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Accurate modeling of 3D objects exhibiting transparency, reflections and thin structures is an extremely challenging problem. Inspired by billboards and geometric proxies used in computer graphics, this paper proposes Generative Latent Textured Objects (GeLaTO), a compact representation that combines a set of coarse shape proxies defining low frequency geometry with learned neural textures, to encode both medium and fine scale geometry as well as view-dependent appearance. To generate the proxies' textures, we learn a joint latent space allowing category-level appearance and geometry interpolation. The proxies are independently rasterized with their corresponding neural texture and composited using a U-Net, which generates an output photorealistic image including an alpha map. We demonstrate the effectiveness of our approach by reconstructing complex objects from a sparse set of views. We show results on a dataset of real images of eyeglasses frames, which are particularly challenging to reconstruct using classical methods. We also demonstrate that these coarse proxies can be handcrafted when the underlying object geometry is easy to model, like eyeglasses, or generated using a neural network for more complex categories, such as cars."
Improving Vision-and-Language Navigation with Image-Text Pairs from the Web
Authors: Arjun Majumdar, Ayush Shrivastava, Stefan Lee, Peter Anderson, Devi Parikh, Dhruv Batra
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Following a navigation instruction such as 'Walk down the stairs and stop at the brown sofa' requires embodied AI agents to ground referenced scene elements referenced (e.g. 'stairs') to visual content in the environment (pixels corresponding to 'stairs'). We ask the following question -- can we leverage abundant `disembodied' web-scraped vision-and-language corpora (e.g. Conceptual Captions) to learn the visual groundings that improve performance on a relatively data-starved embodied perception task (Vision-and-Language Navigation)? Specifically, we develop VLN-BERT, a visiolinguistic transformer-based model for scoring the compatibility between an instruction ('...stop at the brown sofa') and a trajectory of panoramic RGB images captured by the agent. We demonstrate that pretraining VLN-BERT on image-text pairs from the web before fine-tuning on embodied path-instruction data significantly improves performance on VLN -- outperforming prior state-of-the-art in the fully-observed setting by 4 absolute percentage points on success rate. Ablations of our pretraining curriculum show each stage to be impactful -- with their combination resulting in further gains."
Directional Temporal Modeling for Action Recognition
Authors: Xinyu Li, Bing Shuai, Joseph Tighe
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Many current activity recognition models use 3D convolutional neural networks (e.g. I3D, I3D-NL) to generate local spatial-temporal features. However, such features do not encode clip-level ordered temporal information. In this paper, we introduce a channel independent directional convolution (CIDC) operation, which learns to model the temporal evolution among local features. By applying multiple CIDC units we construct a light-weight network that models the clip-level temporal evolution across multiple spatial scales. Our CIDC network can be attached to any activity recognition backbone network. We evaluate our method on four popular activity recognition datasets and consistently improve upon state-of-the-art techniques. We further visualize the activation map of our CIDC network and show that it is able to focus on more meaningful, action related parts of the frame."
Shonan Rotation Averaging: Global Optimality by Surfing SO(p)(n)
Authors: Frank Dellaert, David M. Rosen, Jing Wu, Robert Mahony, Luca Carlone
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Shonan Rotation Averaging is a fast, simple, and elegant rotation averaging algorithm that is guaranteed to recover globally optimal solutions under mild assumptions on the measurement noise. Our method employs semidefinite relaxation in order to recover provably globally optimal solutions of the rotation averaging problem. In contrast to prior work, we show how to solve large-scale instances of these relaxations using manifold minimization on (only slightly) higher-dimensional rotation manifolds, re-using existing high-performance (but local) structure-from-motion pipelines. Our method thus preserves the speed and scalability of those, while enabling the recovery of globally optimal solutions."
Semantic Curiosity for Active Visual Learning
Authors: Devendra Singh Chaplot, Helen Jiang, Saurabh Gupta, Abhinav Gupta
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: In this paper, we study the task of embodied interactive learning for object detection. Given a set of environments (and some labeling budget), our goal is to learn an object detector by having an agent select what data to obtain labels for. How should an exploration policy decide which trajectory should be labeled? One possibility is to use a trained object detector's failure cases as an external reward. However, this will require labeling millions of frames required for training RL policies, which is infeasible. Instead, we explore a self-supervised approach for training our exploration policy by introducing a notion of semantic curiosity. Our semantic curiosity policy is based on a simple observation -- the detection outputs should be consistent. Therefore, our semantic curiosity rewards trajectories with inconsistent labeling behavior and encourages the exploration policy to explore such areas. The exploration policy trained via semantic curiosity generalizes to novel scenes and helps train an object detector that outperforms baselines trained with other possible alternatives such as random exploration, prediction-error curiosity, and coverage-maximizing exploration."
Multi-Temporal Recurrent Neural Networks For Progressive Non-Uniform Single Image Deblurring With Incremental Temporal Training
Authors: Dongwon Park, Dong Un Kang, Jisoo Kim, Se Young Chun
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Blind non-uniform image deblurring for severe blurs induced by large motions is still challenging. Multi-scale (MS) approach has been widely used for deblurring that sequentially recovers the downsampled original image in low spatial scale first and then further restores in high spatial scale using the result(s) from lower spatial scale(s). Here, we investigate a novel alternative approach to MS, called multi-temporal (MT), for non-uniform single image deblurring by exploiting time-resolved deblurring dataset from high-speed cameras. MT approach models severe blurs as a series of small blurs so that it deblurs small amount of blurs in the original spatial scale progressively instead of restoring the images in different spatial scales. To realize MT approach, we propose progressive deblurring over iterations and incremental temporal training with temporally augmented training data. Our MT approach, that can be seen as a form of curriculum learning in a wide sense, allows a number of state-of-the-art MS based deblurring methods to yield improved performances without using MS approach. We also proposed a MT recurrent neural network with recurrent feature maps that outperformed state-of-the-art deblurring methods with the smallest number of parameters."
ProgressFace: Scale-Aware Progressive Learning for Face Detection
Authors: Jiashu Zhu, Dong Li, Tiantian Han, Lu Tian, Yi Shan
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Scale variation stands out as one of key challenges in face detection. Recent attempts have been made to cope with this issue by incorporating image / feature pyramids or adjusting anchor sampling / matching strategies. In this work, we propose a novel scale-aware progressive training mechanism to address large scale variations across faces. Inspired by curriculum learning, our method gradually learns large-to-small face instances. The preceding models learned with easier samples (i.e., large faces) can provide good initialization for succeeding learning with harder samples (i.e., small faces), ultimately deriving a better optimum of face detectors. Moreover, we propose an auxiliary anchor-free enhancement module to facilitate the learning of small faces by supplying positive anchors that may be not covered according to the criterion of IoU overlap. Such anchor-free module will be removed during inference and hence no extra computation cost is introduced. Extensive experimental results demonstrate the superiority of our method compared to the state-of-the-arts on the standard FDDB and WIDER FACE benchmarks. Especially, our ProgressFace-Light with MobileNet-0.25 backbone achieves 87.9% AP on the hard set of WIDER FACE, surpassing largely RetinaFace with the same backbone by 9.7%. Code and our trained face detection models are available at https://github.com/jiashu-zhu/ProgressFace."
Learning Multi-layer Latent Variable Model via Variational Optimization of Short Run MCMC for Approximate Inference
Authors: Erik Nijkamp, Bo Pang, Tian Han, Linqi Zhou, Song-Chun Zhu, Ying Nian Wu
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: This paper studies the fundamental problem of learning deep generative models that consist of multiple layers of latent variables organized in top-down architectures. Such models have high expressivity and allow for learning hierarchical representations. Learning such a generative model requires inferring the latent variables for each training example based on the posterior distribution of these latent variables. The inference typically requires Markov chain Monte Caro (MCMC) that can be time consuming. In this paper, we propose to use noise initialized non-persistent short run MCMC, such as finite step Langevin dynamics initialized from the prior distribution of the latent variables, as an approximate inference engine, where the step size of the Langevin dynamics is variationally optimized by minimizing the Kullback-Leibler divergence between the distribution produced by the short run MCMC and the posterior distribution. Our experiments show that the proposed method outperforms variational auto-encoder (VAE) in terms of reconstruction error and synthesis quality. The advantage of the proposed method is that it is simple and automatic without the need to design an inference model."
CoTeRe-Net: Discovering Collaborative Ternary Relations in Videos
Authors: Zhensheng Shi, Cheng Guan, Liangjie Cao, Qianqian Li, Ju Liang, Zhaorui Gu, Haiyong Zheng, Bing Zheng
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Modeling relations is crucial to understand videos for action and behavior recognition. Current relation models mainly reason about relations of invisibly implicit cues, while important relations of visually explicit cues are rarely considered, and the collaboration between them is usually ignored. In this paper, we propose a novel relation model that discovers relations of both implicit and explicit cues as well as their collaboration in videos. Our model concerns Collaborative Ternary Relations (CoTeRe), where the ternary relation involves channel (C, for implicit), temporal (T, for implicit), and spatial (S, for explicit) relation (R). We devise a flexible and effective CTSR module to collaborate ternary relations for 3D-CNNs, and then construct CoTeRe-Nets for action recognition. Extensive experiments on both ablation study and performance evaluation demonstrate that our CTSR module is significantly effective with approximate 3% gains and our CoTeRe-Nets outperform state-of-the-art approaches on three popular benchmarks. Boosts analysis and relations visualization also validate that relations of both implicit and explicit cues are discovered with efficacy by our method. Our code is available at https://github.com/zhenglab/cotere-net ."
Modeling the Effects of Windshield Refraction for Camera Calibration
Authors: Frank Verbiest, Marc Proesmans, Luc Van Gool
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: In this paper, we study the effects of windshield refraction for autonomous driving applications. These distortion effects are surprisingly large and can not be explained by traditional camera models. Instead of using a generalized camera approach, we propose a novel approach to jointly optimize a traditional camera model, and a mathematical representation of the windshield’s surface. First, using the laws of geometric optics, the refraction is modeled using a local spherical approximation to the windshield’s geometry. Next, a spline-based model is proposed as a refinement to better adapt to deviations from the ideal shape and manufacturing variations. By jointly optimizing refraction and camera parameters, the projection error can be significantly reduced. The proposed models are validated on real windshield observations and custom setups to compare recordings with and without windshield, with accurate laser scan measurements as 3D ground truth."