GenAI4ELab/papercli-papers huggingface dataset:
Ladybird: Quasi-Monte Carlo Sampling for Deep Implicit Field Based 3D Reconstruction with Symmetry
Authors: Yifan Xu; Tianqi Fan; Yi Yuan; Gurprit Singh
Venue: ECCV 2020 (main)

Abstract: Deep implicit field regression methods are effective for 3D reconstruction from single-view images. However, the impact of different sampling patterns on the reconstruction quality is not well-understood. In this work, we first study the effect of point set discrepancy on the network training. Based on Farthest Point Sampling algorithm, we propose a sampling scheme that theoretically encourages better generalization performance, and results in fast convergence for SGD-based optimization algorithms. Secondly, based on the reflective symmetry of an object, we propose a feature fusion method that alleviates issues due to self-occlusions which makes it difficult to utilize local image features. Our proposed system Ladybird is able to create high quality 3D object reconstructions from a single input image. We evaluate Ladybird on a large scale 3D dataset (ShapeNet) demonstrating highly competitive results in terms of Chamfer distance, Earth Mover's distance and Intersection Over Union (IoU). "
Segment as Points for Efficient Online Multi-Object Tracking and Segmentation
Authors: Zhenbo Xu; Wei Zhang; Xiao Tan; Wei Yang; Huan Huang; Shilei Wen; Errui Ding; Liusheng Huang
Venue: ECCV 2020 (main)

Abstract: Current multi-object tracking and segmentation (MOTS) methods follow the tracking-by-detection paradigm and adopt convolutions for feature extraction. However, as affected by the inherent receptive field, convolution based feature extraction inevitably mixes up the foreground features and the background features, resulting in ambiguities in the subsequent instance association. In this paper, we propose a highly effective method for learning instance embeddings based on segments by converting the compact image representation to un-ordered 2D point cloud representation. Our method generates a new tracking-by-points paradigm where discriminative instance embeddings are learned from randomly selected points rather than images. Furthermore, multiple informative data modalities are converted into point-wise representations to enrich point-wise features. The resulting online MOTS framework, named PointTrack, surpasses all the state-of-the-art methods including 3D tracking methods by large margins (5.4\% higher MOTSA and 18 times faster over MOTSFusion) with the near real-time speed (22 FPS). Evaluations across three datasets demonstrate both the effectiveness and efficiency of our method. Moreover, based on the observation that current MOTS datasets lack crowded scenes, we build a more challenging MOTS dataset named APOLLO MOTS with higher instance density. Both APOLLO MOTS and our codes are publicly available at https://github.com/detectRecog/PointTrack."
Conditional Convolutions for Instance Segmentation
Authors: Zhi Tian; Chunhua Shen; Hao Chen
Venue: ECCV 2020 (main)

Abstract: We propose a simple yet effective instance segmentation framework, termed CondInst (conditional convolutions for instance segmentation). Top-performing instance segmentation methods such as Mask R-CNN rely on ROI operations (typically ROIPool or ROIAlign) to obtain the final instance masks. In contrast, we propose to solve instance segmentation from a new perspective. Instead of using instance-wise ROIs as inputs to a network of fixed weights, we employ dynamic instance-aware networks, conditioned on instances. CondInst enjoys two advantages: 1) Instance segmentation is solved by a fully convolutional network, eliminating the need for ROI cropping and feature alignment. 2) Due to the much improved capacity of dynamically-generated conditional convolutions, the mask head can be very compact (e.g., 3 conv. layers, each having only 8 channels), leading to significantly faster inference. We demonstrate a simpler instance segmentation method that can achieve improved performance in both accuracy and inference speed. On the COCO dataset, we outperform a few recent methods including well-tuned Mask R-CNN baselines, without longer training schedules needed. Code is available: https://git.io/AdelaiDet"
MutualNet: Adaptive ConvNet via Mutual Learning from Network Width and Resolution
Authors: Taojiannan Yang; Sijie Zhu; Chen Chen; Shen Yan; Mi Zhang; Andrew Willis
Venue: ECCV 2020 (main)

Abstract: We propose the width-resolution mutual learning method (MutualNet) to train a network that is executable at dynamic resource constraints to achieve adaptive accuracy-efficiency trade-offs at runtime. Our method trains a cohort of sub-networks with different widths using different input resolutions to mutually learn multi-scale representations for each sub-network. It achieves consistently better ImageNet top-1 accuracy over the state-of-the-art adaptive network US-Net under different computation constraints, and outperforms the best compound scaled MobileNet in EfficientNet by 1.5%. The superiority of our method is also validated on COCO object detection and instance segmentation as well as transfer learning. Surprisingly, the training strategy of MutualNet can also boost the performance of a single network, which substantially outperforms the powerful AutoAugmentation in both efficiency (GPU search hours: 15000 vs. 0) and accuracy (ImageNet: 77.6% vs. 78.6%). Code is provided in supplementary material."
Fashionpedia: Ontology, Segmentation, and an Attribute Localization Dataset
Authors: Menglin Jia; Mengyun Shi; Mikhail Sirotenko; Yin Cui; Claire Cardie; Bharath Hariharan; Hartwig Adam; Serge Belongie
Venue: ECCV 2020 (main)

Abstract: Segmentation, and an Attribute Localization Dataset","In this work, we focus on the task of instance segmentation with attribute localization. This unifies instance segmentation (detect and segment each object instance) and visual categorization of fine-grained attributes (classify one or multiple attributes). The proposed task requires both localizing an object and describing its properties. To illustrate the various aspects of this task, we focus on the domain of fashion and introduce Fashionpedia as a step toward mapping out the visual aspects of the fashion world. Fashionpedia consists of two parts: (1) an ontology built by fashion experts containing 27 main apparel categories, 19 apparel parts, and 294 fine-grained attributes and their relationships and (2) a dataset consisting of everyday and celebrity event fashion images annotated with segmentation masks and their associated fine-grained attributes, built upon the backbone of the Fashionpedia ontology. In order to solve this challenging task, we propose a novel Attribute-Mask R-CNN model to jointly perform instance segmentation and localized attribute recognition, and provide a novel evaluation metric for the task. Fashionpedia is available at https://fashionpedia.github.io/home/. "
Privacy Preserving Structure-from-Motion
Authors: Marcel Geppert; Viktor Larsson; Pablo Speciale; Johannes L. Schönberger; Marc Pollefeys
Venue: ECCV 2020 (main)

Abstract: Over the last years, visual localization and mapping solutions have been adopted by an increasing number of mixed reality and robotics systems. The recent trend towards cloud-based localization and mapping systems has raised significant privacy concerns. These are mainly grounded by the fact that these services require users to upload visual data to their servers, which can reveal potentially confidential information, even if only derived image features are uploaded. Recent research addresses some of these concerns for the task of image-based localization by concealing the geometry of the query images and database maps. The core idea of the approach is to lift 2D/3D feature points to random lines, while still providing sufficient constraints for camera pose estimation. In this paper, we further build upon this idea and propose solutions to the different core algorithms of an incremental Structure-from-Motion pipeline based on random line features. With this work, we make another fundamental step towards enabling privacy preserving cloud-based mapping solutions. Various experiments on challenging real-world datasets demonstrate the practicality of our approach achieving comparable results to standard Structure-from-Motion systems."
Rewriting a Deep Generative Model
Authors: David Bau; Steven Liu; Tongzhou Wang; Jun-Yan Zhu; Antonio Torralba
Venue: ECCV 2020 (main)

Abstract: A deep generative model such as a GAN learns to model a rich set of semantic and physical rules about the target distribution, but up to now, it has been obscure how such rules are encoded in the network, or how a rule could be changed. In this paper, we introduce a new problem setting: manipulation of specific rules encoded by a deep generative model. To address the problem, we propose a formulation in which the desired rule is changed by manipulating a layer of a deep network as a linear associative memory. We derive an algorithm for modifying one entry of the associative memory, and we demonstrate that several interesting structural rules can be located and modified within the layers of state-of-the-art generative models. We present a user interface to enable users to interactively change the rules of a generative model to achieve desired effects, and we show several proof-of-concept applications. Finally, results on multiple datasets demonstrate the advantage of our method against standard fine-tuning methods and edit transfer algorithms."
Compare and Reweight: Distinctive Image Captioning Using Similar Images Sets
Authors: Jiuniu Wang; Wenjia Xu; Qingzhong Wang; Antoni B. Chan
Venue: ECCV 2020 (main)

Abstract: A wide range of image captioning models has been developed, achieving significant improvement based on popular metrics, such as BLEU, CIDEr, and SPICE. However, although the generated captions can accurately describe the image, they are generic for similar images and lack distinctiveness, i.e., cannot properly describe the uniqueness of each image. In this paper, we aim to improve the distinctiveness of image captions through training with sets of similar images. First, we propose a distinctiveness metric --- between-set CIDEr (CIDErBtw) to evaluate the distinctiveness of a caption with respect to those of similar images. Our metric shows that the human annotations of each image are not equivalent based on distinctiveness. Thus we propose several new training strategies to encourage the distinctiveness of the generated caption for each image, which are based on using CIDErBtw in a weighted loss function or as a reinforcement learning reward. Finally, extensive experiments are conducted, showing that our proposed approach significantly improves both distinctiveness (as measured by CIDErBtw and retrieval metrics) and accuracy (e.g., as measured by CIDEr) for a wide variety of image captioning baselines. These results are further confirmed through a user study."
Long-term Human Motion Prediction with Scene Context
Authors: Zhe Cao; Hang Gao; Karttikeya Mangalam; Qi-Zhi Cai; Minh Vo; Jitendra Malik
Venue: ECCV 2020 (main)

Abstract: Human movement is goal-directed and influenced by the spatial layout of the objects in the scene. To plan future human motion, it is crucial to perceive the environment -- imagine how hard it is to navigate a new room with lights off. Existing works on predicting human motion do not pay attention to the scene context and thus struggle in long-term prediction. In this work, we propose a novel three-stage framework that exploits scene context to tackle this task. Given a single scene image and 2D pose histories, our method first samples multiple human motion goals, then plans 3D human paths towards each goal, and finally predicts 3D human pose sequences following each path. For stable training and rigorous evaluation, we contribute a diverse synthetic dataset with clean annotations. In both synthetic and real datasets, our method shows consistent quantitative and qualitative improvements over existing methods. "
NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
Authors: Ben Mildenhall; Pratul P. Srinivasan; Matthew Tancik; Jonathan T. Barron; Ravi Ramamoorthi; Ren Ng
Venue: ECCV 2020 (main)

Abstract: We present a method that achieves state-of-the-art results for synthesizing novel views of complex scenes by optimizing an underlying continuous volumetric scene function using a sparse set of input views. Our algorithm represents a scene using a fully-connected (non-convolutional) deep network, whose input is a single continuous 5D coordinate (spatial location $(x,y,z)$ and viewing direction $( heta,\phi)$) and whose output is the volume density and view-dependent emitted radiance at that spatial location. We synthesize views by querying 5D coordinates along camera rays and use classic volume rendering techniques to project the output colors and densities into an image. Because volume rendering is naturally differentiable, the only input required to optimize our representation is a set of images with known camera poses. We describe how to effectively optimize neural radiance fields to render photorealistic novel views of scenes with complicated geometry and appearance, and demonstrate results that outperform prior work on neural rendering and view synthesis. View synthesis results are best viewed as videos, so we urge readers to view our supplementary video for convincing comparisons. We will make our code and data available upon publication."
ReferIt3D: Neural Listeners for Fine-Grained 3D Object Identification in Real-World Scenes
Authors: Panos Achlioptas; Ahmed Abdelreheem; Fei Xia; Mohamed Elhoseiny; Leonidas Guibas
Venue: ECCV 2020 (main)

Abstract: In this work we study the problem of using referential language to identify common objects in real-world 3D scenes. We focus on a challenging setup where the referred object belongs to a extit{fine-grained} object class and the underlying scene contains extit{multiple} object instances of that class. Due to the scarcity and unsuitability of existent 3D-oriented linguistic resources for this task, we first develop two large-scale and complementary visio-linguistic datasets: i) extbf{ extit{Sr3D}}, which contains 83.5K template-based utterances leveraging extit{spatial relations} with other fine-grained object classes to localize a referred object in a given scene, and ii) extbf{ extit{Nr3D}} which contains 41.5K extit{natural, free-form}, utterances collected by deploying a 2-player object reference game in 3D scenes. Using utterances of either datasets, human listeners can recognize the referred object with high ($>$86\%, 92\% resp.) accuracy. By tapping on this data, we develop novel neural listeners that can comprehend object-centric natural language and identify the referred object extit{directly} in a 3D scene. Our key technical contribution is designing an approach for combining linguistic and geometric information (in the form of 3D point-clouds) and creating multi-modal (3D) neural listeners. We also show that architectures which promote object-to-object communication via graph neural networks outperform less context-aware alternatives, and that language-assisted 3D object identification outperforms language-agnostic object classifiers."
MatryODShka: Real-time 6DoF Video View Synthesis using Multi-Sphere Images
Authors: Benjamin Attal; Selena Ling; Aaron Gokaslan; Christian Richardt; James Tompkin
Venue: ECCV 2020 (main)

Abstract: We introduce a method to convert stereo 360 (omnidirectional stereo) imagery into a layered, multi-sphere image representation for six degree-of-freedom (6DoF) rendering. Stereo 360 imagery can be captured from multi-camera systems for virtual reality (VR) rendering, but lacks motion parallax and correct-in-all-directions disparity cues. Together, these can quickly lead to VR sickness when viewing content. One solution is to try and generate a format suitable for 6DoF rendering, such as by estimating depth. However, this raises questions as to how to handle disoccluded regions in dynamic scenes. Our approach is to simultaneously learn depth and blending weights via a multi-sphere image representation, which can be rendered with correct 6DoF disparity and motion parallax in VR. This significantly improves comfort for the viewer, and can be inferred and rendered in real time on modern GPU hardware. Together, these move towards making VR video a more comfortable immersive medium."
Learning and Aggregating Deep Local Descriptors for Instance-level Recognition
Authors: Giorgos Tolias; Tomas Jenicek; Ondřej Chum
Venue: ECCV 2020 (main)

Abstract: We propose an efficient method to learn deep local descriptors for instance-level recognition. The training only requires examples of positive and negative image pairs and is performed as metric learning of sum-pooled global image descriptors. At inference, the local descriptors are provided by the activations of internal components of the network. We demonstrate why such an approach learns local descriptors that work well for image similarity estimation with classical efficient match kernel methods. The experimental validation studies the trade-off between performance and memory requirements of the state-of-the-art image search approach based on match kernels. Compared to existing local descriptors, the proposed ones perform better in two instance-level recognition tasks and keep memory requirements lower. We experimentally show that global descriptors are not effective enough at large scale and that local descriptors are essential. We achieve state-of-the-art performance, in some cases even with a backbone network as small as ResNet18."
A Consistently Fast and Globally Optimal Solution to the Perspective-n-Point Problem
Authors: George Terzakis; Manolis Lourakis
Venue: ECCV 2020 (main)

Abstract: An approach for estimating the pose of a camera given a set of 3D points and their corresponding 2D image projections is presented. It formulates the problem as a non-linear quadratic program and identifies regions in the parameter space that contain unique minima with guarantees that at least one of them will be the global minimum. Each regional minimum is computed with a sequential quadratic programming scheme. These premises result in an algorithm that always determines the global minima of the perspective-n-point problem for any number of input correspondences, regardless of possible coplanar arrangements of the imaged 3D points. For its implementation, the algorithm merely requires ordinary operations available in any standard off-the-shelf linear algebra library. Comparative evaluation demonstrates that the algorithm achieves state-of-the-art results at a consistently low computational cost."
Learn to Recover Visible Color for Video Surveillance in a Day
Authors: Guangming Wu; Yinqiang Zheng; Zhiling Guo; Zekun Cai; Xiaodan Shi; Xin Ding; Yifei Huang; Yimin Guo; Ryosuke Shibasaki
Venue: ECCV 2020 (main)

Abstract: In silicon sensors, the interference between visible and near-infrared (NIR) signals is a crucial problem. For all-day video surveillance, commercial camera systems usually adopt auxiliary NIR cut filter and NIR LED illumination to selectively block or enhance NIR signal according to the surrounding light conditions. This switching between the daytime and the nighttime mode inevitably involves mechanical parts, and thus requires frequent maintenance. Furthermore, images captured at nighttime mode are in shortage of chrominance, which might hinder human interpretation and high-level computer vision algorithms in succession. In this paper, we present a deep learning based approach that directly generates human-friendly, visible color for video surveillance in a day. To enable training, we capture well-aligned video pairs through a customized optical device and contribute a large-scale dataset, video surveillance in a day (VSIAD). We propose a novel multi-task deep network with state synchronization modules to better utilize texture and chrominance information. Our trained model generates high-quality visible color images and achieves state-of-the-art performance on multiple metrics as well as subjective judgment."
Deep Fashion3D: A Dataset and Benchmark for 3D Garment Reconstruction from Single Images
Authors: Heming Zhu; Yu Cao; Hang Jin; Weikai Chen; Dong Du; Zhangye Wang; Shuguang Cui; Xiaoguang Han
Venue: ECCV 2020 (main)

Abstract: High-fidelity clothing reconstruction is the key to achieving photorealism in a wide range of applications including human digitization, virtual try-on, etc. Recent advances in learning-based approaches have accomplished unprecedented accuracy in recovering unclothed human shape and pose from single images, thanks to the availability of powerful statistical models, e.g. SMPL[38], learned from a large number of body scans. In contrast, modeling and recovering clothed human and 3D garments remains notoriously difficult, mostly due to the lack of large-scale clothing models available for the research community. We propose to fill this gap by introducing DeepFashion3D, the largest collection to date of 3D garment models, with the goal of establishing a novel benchmark and dataset for the evaluation of image-based garment reconstruction systems. Deep Fashion3D contains 2078 models reconstructed from real garments, which covers 10 different categories and 563 garment instances. It provides rich annotations including 3D feature lines, 3D body pose and the corresponded multi-view real images. In addition, each garment is randomly posed to enhance the variety of real clothing deformations. To demonstrate the advantage of \datasetName{}, we propose a novel baseline approach for single-view garment reconstruction, which leverages the merits of both mesh and implicit representations. A novel adaptable template is proposed to enable the learning of all types of clothing in a single network. Extensive experiments have been conducted on the proposed dataset to verify its significance and usefulness. We will make Deep Fashion3D publicly available upon publication. "
Spatially Adaptive Inference with Stochastic Feature Sampling and Interpolation
Authors: Zhenda Xie; Zheng Zhang; Xizhou Zhu; Gao Huang; Stephen Lin
Venue: ECCV 2020 (main)

Abstract: In the feature maps of CNNs, there commonly exists considerable spatial redundancy that leads to much repetitive processing. Towards reducing this superfluous computation, we propose to compute features only at sparsely sampled locations, which are probabilistically chosen according to activation responses, and then densely reconstruct the feature map with an efficient interpolation procedure. With this sampling-interpolation scheme, our network avoids expending computation on spatial locations that can be effectively interpolated, while being robust to activation prediction errors through broadly distributed sampling. A technical challenge of this sampling-based approach is that the binary decision variables for representing discrete sampling locations are non-differentiable, making them incompatible with backpropagation. To circumvent this issue, we make use of a reparameterization trick based on the Gumbel-Softmax distribution, with which backpropagation can iterate these variables towards binary values. The presented network is experimentally shown to save substantial computation while maintaining accuracy over a variety of computer vision tasks."