mtybilly/Instruct2DS huggingface dataset:
Deep Space-Time Video Upsampling Networks
Authors: Jaeyeon Kang, Younghyun Jo, Seoung Wug Oh, Peter Vajda, Seon Joo Kim
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Video super-resolution (VSR) and frame interpolation (FI) are traditional computer vision problems, and the performance have been improving by incorporating deep learning recently. In this paper, we investigate the problem of jointly upsampling videos both in space and time, which is becoming more important with advances in display systems. One solution for this is to run VSR and FI, one by one, independently. This is highly inefficient as heavy deep neural networks (DNN) are involved in each solution. To this end, we propose an end-to-end DNN framework for the space-time video upsampling by efficiently merging VSR and FI into a joint framework. In our framework, a novel weighting scheme is proposed to fuse input frames effectively without explicit motion compensation for efficient processing of videos. The results show better results both quantitatively and qualitatively, while reducing the computation time (x7 faster) and the number of parameters (30%) compared to baselines. Our source code is available at https://github.com/JaeYeonKang/STVUN-Pytorch."
Large-Scale Few-Shot Learning via Multi-Modal Knowledge Discovery
Authors: Shuo Wang, Jun Yue, Jianzhuang Liu, Qi Tian, Meng Wang
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Large-scale few-shot learning aims at identifying hundreds of novel object categories where each category has only a few samples. It is a challenging problem since (1) the identifying process is susceptible to over-fitting with limited samples of an object, and (2) the sample imbalance between a base (known knowledge) category and a novel category is easy to bias the recognition results. To solve these problems, we propose a method based on multi-modal knowledge discovery. First, we use the visual knowledge to help the feature extractors focus on different visual parts. Second, we design a classifier to learn the distribution over all categories. In the second stage, we develop three schemes to minimize the prediction error and balance the training procedure: (1) Hard labels are used to provide precise supervision. (2) Semantic textual knowledge is utilized as weak supervision to find the potential relations between the novel and the base categories. (3) An imbalance control is presented from the data distribution to alleviate the recognition bias towards the base categories. We apply our method on three benchmark datasets, and it achieves state-of-the-art performances in all the experiments."
Fast Video Object Segmentation using the Global Context Module
Authors: Yu Li, Zhuoran Shen, Ying Shan
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: We developed a real-time, high-quality semi-supervised video object segmentation algorithm. Its accuracy is on par with the most accurate, time-consuming online-learning model, while its speed is similar to the fastest template-matching method with sub-optimal accuracy. The core component of the model is a novel global context module that effectively summarizes and propagates information through the entire video. Compared to previous approaches that only use one frame or a few frames to guide the segmentation of the current frame, the global context module uses all past frames. Unlike the previous state-of-the-art space-time memory network that caches a memory at each spatio-temporal position, the global context module uses a fixed-size feature representation. Therefore, it uses constant memory regardless of the video length and costs substantially less memory and computation. With the novel module, our model achieves top performance on standard benchmarks at a real-time speed."
Uncertainty-Aware Weakly Supervised Action Detection from Untrimmed Videos
Authors: Anurag Arnab, Chen Sun, Arsha Nagrani, Cordelia Schmid
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Despite the recent advances in video classification, progress in spatio-temporal action recognition has lagged behind. A major contributing factor has been the prohibitive cost of annotating videos frame-by-frame. In this paper, we present a spatio-temporal action recognition model that is trained with only video-level labels, which are significantly easier to annotate. Our method leverages per-frame person detectors which have been trained on large image datasets within a Multiple Instance Learning framework. We show how we can apply our method in cases where the standard Multiple Instance Learning assumption, that each bag contains at least one instance with the specified label, is invalid using a novel probabilistic variant of MIL where we estimate the uncertainty of each prediction. Furthermore, we report the first weakly-supervised results on the AVA dataset and state-of-the-art results among weakly-supervised methods on UCF101-24."
Selecting Relevant Features from a Multi-domain Representation for Few-shot Classification
Authors: Nikita Dvornik, Cordelia Schmid, Julien Mairal
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Popular approaches for few-shot classification consist of first learning a generic data representation based on a large annotated dataset, before adapting the representation to new classes given only a few labeled samples. In this work, we propose a new strategy based on feature selection, which is both simpler and more effective than previous feature adaptation approaches. First, we obtain a multi-domain representation by training a set of semantically different feature extractors. Then, given a few-shot learning task, we use our multi-domain feature bank to automatically select the most relevant representations. We show that a simple non-parametric classifier built on top of such features produces high accuracy and generalizes to domains never seen during training, which leads to state-of-the-art results on MetaDataset and improved accuracy on mini-ImageNet."
MessyTable: Instance Association in Multiple Camera Views
Authors: Zhongang Cai, Junzhe Zhang, Daxuan Ren, Cunjun Yu, Haiyu Zhao, Shuai Yi, Chai Kiat Yeo, Chen Change Loy
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: We present an interesting and challenging dataset that features a large number of scenes with messy tables captured from multiple camera views. Each scene in this dataset is highly complex, containing multiple object instances that could be identical, stacked and occluded by other instances. The key challenge is to associate all instances given the RGB image of all views. The seemingly simple task surprisingly fails many popular methods or heuristics that we assume good performance in object association. The dataset challenges existing methods in mining subtle appearance differences, reasoning based on contexts, and fusing appearance with geometric cues for establishing an association. We report interesting findings with some popular baselines, and discuss how this dataset could help inspire new problems and catalyse more robust formulations to tackle real-world instance association problems.ootnote{project page: extcolor{magenta}{\url{https://caizhongang.github.io/projects/MessyTable/}}.}"
A Unified Framework for Shot Type Classification Based on Subject Centric Lens
Authors: Anyi Rao, Jiaze Wang, Linning Xu, Xuekun Jiang, Qingqiu Huang, Bolei Zhou, Dahua Lin
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: In film making, shot has a profound influence on how the story is delivered and how the audiences are echoed. As different scale and movement types of shots can express different emotions and contents, recognizing shots and their attributes is important to the understanding of movies as well as general videos. Classifying shot type is challenging due to the additional information required beyond the video content, such as the spatial composition of a frame and the video camera movement. To address these issues, we propose a learning framework Subject Guidance Network (SGNet) for shot type recognition. SGNet separates the subject and background of a shot into two streams, serving as maps to guide scale and movement type classification respectively. To facilitate shot type analysis and model evaluations, we build a large-scale dataset MovieShots, which contains 46K shots from 7K movie trailers with annotations of their scale and movement types. Experiments show that our framework is able to recognize these two attributes of shot accurately, outperforming all the previous methods. "
BSL-1K: Scaling up co-articulated sign language recognition using mouthing cues
Authors: Samuel Albanie, Gül Varol, Liliane Momeni, Triantafyllos Afouras, Joon Son Chung, Neil Fox, Andrew Zisserman
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Recent progress in fine-grained gesture and action classification, and machine translation, point to the possibility of automated sign language recognition becoming a reality. A key stumbling block in making progress towards this goal is a lack of appropriate training data, stemming from the high complexity of sign annotation and a limited supply of qualified annotators. In this work, we introduce a new scalable approach to data collection for sign recognition in continuous videos. We make use of weakly-aligned subtitles for broadcast footage together with a keyword spotting method to automatically localise sign-instances for a vocabulary of 1,000 signs in 1,000 hours of video. We make the following contributions: (1) We show how to use mouthing cues from signers to obtain high-quality annotations from video data - the result is the BSL-1K dataset, a collection of British Sign Language (BSL) signs of unprecedented scale; (2) We show that we can use BSL-1K to train strong sign recognition models for co-articulated signs in BSL and that these models additionally form excellent pretraining for other sign languages and benchmarks---we exceed the state of the art on both the MSASL and WLASL benchmarks. Finally, (3) we propose new large-scale evaluation sets for the tasks of sign recognition and sign spotting and provide baselines which we hope will serve to stimulate research in this area."
HTML: A Parametric Hand Texture Model for 3D Hand Reconstruction and Personalization
Authors: Neng Qian, Jiayi Wang, Franziska Mueller, Florian Bernard, Vladislav Golyanik, Christian Theobalt
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: 3D hand reconstruction from images is a widely-studied problem in computer vision and graphics, and has a particularly high relevance for virtual and augmented reality. Although several 3D hand reconstruction approaches leverage hand models as a strong prior to resolve ambiguities and achieve more robust results, most existing models account only for the hand shape and poses and do not model the texture. To fill this gap, in this work we present HTML, the first parametric texture model of human hands. Our model spans several dimensions of hand appearance variability (e.g. related to gender, ethnicity, or age) and only requires a commodity camera for data acquisition. Experimentally, we demonstrate that our appearance model can be used to tackle a range of challenging problems such as 3D hand reconstruction from a single monocular image. Furthermore, our appearance model can be used to define a neural rendering layer that enables training with a self-supervised photometric loss. We make our model publicly available."
CycAs: Self-supervised Cycle Association for Learning Re-identifiable Descriptions
Authors: Zhongdao Wang, Jingwei Zhang, Liang Zheng, Yixuan Liu, Yifan Sun, Yali Li, Shengjin Wang
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: This paper proposes a self-supervised learning method for the person re-identification (re-ID) problem, where existing unsupervised methods usually rely on pseudo labels, such as those from video tracklets or clustering. A potential drawback of using pseudo labels is that errors may accumulate and it is challenging to estimate the number of pseudo IDs. We introduce a different unsupervised method that allows us to learn pedestrian embeddings from raw videos, without resorting to pseudo labels. The goal is to construct a self-supervised pretext task that matches the person re-ID objective. Inspired by the mph{data association} concept in multi-object tracking, we propose the extbf{Cyc}le extbf{As}sociation ( extbf{CycAs}) task: after performing data association between a pair of video frames forward and then backward, a pedestrian instance is supposed to be associated to itself. To fulfill this goal, the model must learn a meaningful representation that can well describe correspondences between instances in frame pairs. We adapt the discrete association process to a differentiable form, such that end-to-end training becomes feasible. Experiments are conducted in two aspects: We first compare our method with existing unsupervised re-ID methods on seven benchmarks and demonstrate CycAs' superiority. Then, to further validate the practical value of CycAs in real-world applications, we perform training on self-collected videos and report promising performance on the standard test sets."
Open-Edit: Open-Domain Image Manipulation with Open-Vocabulary Instructions
Authors: Xihui Liu, Zhe Lin, Jianming Zhang, Handong Zhao, Quan Tran, Xiaogang Wang, Hongsheng Li
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: We propose a novel algorithm, named Open-Edit, which is the first attempt on open-domain image manipulation with open-vocabulary instructions. It is a challenging task considering the large variation of image domains and the lack of training supervision. Our approach takes advantage of the unified visual-semantic embedding space pretrained on a general image-caption dataset, and manipulates the embedded visual features by applying text-guided vector arithmetic on the image feature maps. A structure-preserving image decoder then generates the manipulated images from the manipulated feature maps. We further propose an on-the-fly sample-specific optimization approach with cycle-consistency constraints to regularize the manipulated images and force them to preserve details of the source images. Our approach shows promising results in manipulating open-vocabulary color, texture, and high-level attributes for various scenarios of open-domain images."
Towards Real-Time Multi-Object Tracking
Authors: Zhongdao Wang, Liang Zheng, Yixuan Liu, Yali Li, Shengjin Wang
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Modern multiple object tracking (MOT) systems usually follow the tracking-by-detection paradigm. It has 1) a detection model for target localization and 2) an appearance embedding model for data association. Having the two models separately executed might lead to efficiency problems, as the running time is simply a sum of the two steps without investigating potential structures that can be shared between them. Existing research efforts on real-time MOT usually focus on the association step, so they are essentially real-time association methods but not real-time MOT system. In this paper, we propose an MOT system that allows target detection and appearance embedding to be learned in a shared model. Specifically, we incorporate the appearance embedding model into a single-shot detector, such that the model can simultaneously output detections and the corresponding embeddings. We further propose a simple and fast association method that works in conjunction with the joint model. In both components the computation cost is significantly reduced compared with former MOT systems, resulting in a neat and fast baseline for future follow-ups on real-time MOT algorithm design. To our knowledge, this work reports the first (near) real-time MOT system, with a running speed of 22 to 40 FPS depending on the input resolution. Meanwhile, its tracking accuracy is comparable to the state-of-the-art trackers embodying separate detection and embedding (SDE) learning ($64.4\%$ MOTA s $66.1\%$ MOTA on MOT-16 challenge). Code and models will be available at \url{https://github.com/Zhongdao/Towards-Realtime-MOT}."
A Balanced and Uncertainty-aware Approach for Partial Domain Adaptation
Authors: Jian Liang, Yunbo Wang, Dapeng Hu, Ran He, Jiashi Feng
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: This work addresses the unsupervised domain adaptation problem, especially in the case of class labels in the target domain being only a subset of those in the source domain. Such a partial transfer setting is realistic but challenging and existing methods always suffer from two key problems, negative transfer and uncertainty propagation. In this paper, we build on domain adversarial learning and propose a novel domain adaptation method BA$^3$US with two new techniques termed Balanced Adversarial Alignment (BAA) and Adaptive Uncertainty Suppression (AUS), respectively. On one hand, negative transfer results in misclassification of target samples to the classes only present in the source domain. To address this issue, BAA pursues the balance between label distributions across domains in a fairly simple manner. Specifically, it randomly leverages a few source samples to augment the smaller target domain during domain alignment so that classes in different domains are symmetric. On the other hand, a source sample would be denoted as uncertain if there is an incorrect class that has a relatively high prediction score, and such uncertainty easily propagates to unlabeled target data around it during alignment, which severely deteriorates adaptation performance. Thus we present AUS that emphasizes uncertain samples and exploits an adaptive weighted complement entropy objective to encourage incorrect classes to have uniform and low prediction scores. Experimental results on multiple benchmarks demonstrate our BA$^3$US surpasses state-of-the-arts for partial domain adaptation tasks. Code is available at \url{https://github.com/tim-learn/BA3US}."
Unsupervised Deep Metric Learning with Transformed Attention Consistency and Contrastive Clustering Loss
Authors: Yang Li, Shichao Kan, Zhihai He
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Existing approaches for unsupervised metric learning focus on exploring self-supervision information within the input image itself. We observe that, when analyzing images, human eyes often compare images against each other instead of examining images individually. In addition, they often pay attention to certain keypoints, image regions, or objects which are discriminative between image classes but highly consistent within classes. Even if the image is being transformed, the attention pattern will be consistent. Motivated by this observation, we develop a new approach to unsupervised deep metric learning where the network is learned based on self-supervision information across images instead of within one single image. To characterize the consistent pattern of human attention during image comparisons, we introduce the idea of transformed attention consistency. It assumes that visually similar images, even undergoing different image transforms, should share the same consistent visual attention map. This consistency leads to a pairwise self-supervision loss, allowing us to learn a Siamese deep neural network to encode and compare images against their transformed or matched pairs. To further enhance the inter-class discriminative power of the feature generated by this network, we adapt the concept of triplet loss from supervised metric learning to our unsupervised case and introduce the contrastive clustering loss. Our extensive experimental results on benchmark datasets demonstrate that our proposed method outperforms current state-of-the-art methods for unsupervised metric learning by a large margin."
STEm-Seg: Spatio-temporal Embeddings for Instance Segmentation in Videos
Authors: Ali Athar, Sabarinath Mahadevan, Aljosa Osep, Laura Leal-Taixé, Bastian Leibe
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Existing methods for instance segmentation in videos typically involve multi-stage pipelines that follow the tracking-by detection paradigm and model a video clip as a sequence of images. Multiple networks are used to detect objects in individual frames, and then associate these detections over time. Hence, these methods are often non-end-to-end trainable and highly tailored to specific tasks. In this paper, we propose a different approach that is well-suited to a variety of tasks involving instance segmentation in videos. In particular, we model a video clip as a single 3D spatio-temporal volume, and propose a novel approach that segments and tracks instances across space and time in a single stage. Our problem formulation is centered around the idea of spatio-temporal embeddings which are trained to cluster pixels belonging to a specific object instance over an entire video clip. To this end, we introduce (i) novel mixing functions that enhance the feature representation of spatio-temporal embeddings, and (ii) a single-stage, proposal-free network that can reason about temporal context. Our network is trained end-to-end to learn spatio-temporal embeddings as well as parameters required to cluster these embeddings, thus simplifying inference. Our method achieves state-of-the-art results across multiple datasets and tasks."
Hierarchical Style-based Networks for Motion Synthesis
Authors: Jingwei Xu, Huazhe Xu, Bingbing Ni, Xiaokang Yang, Xiaolong Wang, Trevor Darrell
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Generating diverse and natural behaviors is one of the long-standing goals for creating intelligent characters in the animated world. In this paper, we propose an unsupervised method for generating long-range, diverse and plausible behaviors to achieve a specific goal location. Our proposed method learns to model the motion of human by decomposing a long-range generation task in a hierarchical manner. Given the starting and ending states, a memory bank is used to retrieve motion references as source material for short-range clip generation. We first propose to explicitly disentangle the provided motion material into style and content counterparts via bi-linear transformation modelling, where diverse synthesis is achieved by free-form combination of these two components. The short-range clips are then connected to form a long-range motion sequence. Without ground truth annotation, we propose a parameterized bi-directional interpolation scheme to guarantee the physical validity and visual naturalness of generated results. On large-scale skeleton dataset, we show that the proposed method is able to synthesise long-range, diverse and plausible motion, which is also generalizable to unseen motion data during testing. Moreover, we demonstrate the generated sequences are useful as subgoals for actual physical execution in the animated world. Please refer to our project page~ootnote{https://sites.google.com/view/hsnms/home} for more synthesised results."
Who Left the Dogs Out? 3D Animal Reconstruction with Expectation Maximization in the Loop
Authors: Benjamin Biggs, Oliver Boyne, James Charles, Andrew Fitzgibbon, Roberto Cipolla
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: We introduce an automatic, end-to-end method for recovering the 3D pose and shape of dogs from monocular internet images. The large variation in shape between dog breeds, and significant occlusion and low quality of internet images makes this a challenging problem. We learn a richer prior over shapes than previous work, which helps regularize parameter estimation. We demonstrate results on the Stanford Dog Dataset, an ``in-the-wild'' dataset of 20,580 dog images for which we have collected 2D joint and silhouette annotations to split for training and evaluation. In order to capture the large shape variety of dogs, we show that the natural variation in the 2D dataset is enough to learn a detailed 3D prior through expectation maximisation (EM). As a by-product of training, we generate a new parameterised model (including limb scaling) SMBLD which we release alongside the annotation dataset to the research community."
Learning to Count in the Crowd from Limited Labeled Data
Authors: Vishwanath A. Sindagi, Rajeev Yasarla, Deepak Sam Babu, R. Venkatesh Babu, Vishal M. Patel
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Recent crowd counting approaches have achieved excellent performance. However, they are essentially based on fully supervised paradigm and require large number of annotated samples. Obtaining annotations is an expensive and labour-intensive process. In this work, we focus on reducing the annotation efforts by learning to count in the crowd from limited number of labeled samples while leveraging a large pool of unlabeled data. Specifically, we propose a Gaussian Process-based iterative learning mechanism that involves estimation of pseudo-ground truth for the unlabeled data, which is then used as supervision for training the network. The proposed method is shown to be effective under the reduced data (semi-supervised) settings for several datasets like ShanghaiTech, UCF-QNRF, WorldExpo, UCSD, etc. Furthermore, we demonstrate that the proposed method can be leveraged to enable the network in learning to count from synthetic dataset while being able to generalize better to real-world datasets (synthetic-to-real transfer). "
SPOT: Selective Point Cloud Voting for Better Proposal in Point Cloud Object Detection
Authors: Hongyuan Du, Linjun Li, Bo Liu, Nuno Vasconcelos
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: The sparsity of point clouds limits deep learning models on capturing long-range dependencies, which makes features extracted by the models ambiguous. In point cloud object detection, ambiguous features make it hard for detectors to locate object centers and finally lead to bad detection results. In this work, we propose Selective Point clOud voTing (SPOT) module, a simple effective component that can be easily trained end-to-end in point cloud object detectors to solve this problem. Inspired by probabilistic Hough voting, SPOT incorporates an attention mechanism that helps detectors focus on less ambiguous features and preserves their diversity of mapping to multiple object centers. For evaluating our module, we implement SPOT on advanced baseline detectors and test on two benchmark datasets of clutter indoor scenes, ScanNet and SUN RGB-D. Baselines enhanced by our module can stably improve results in agreement by a large margin and achieve new state-or-the-art detection, especially under more strict evaluation metric that adopts larger IoU threshold, implying our module is the key leading to high-quality object detection in point clouds."
Explainable Face Recognition
Authors: Jonathan R. Williford, Brandon B. May, Jeffrey Byrne
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Explainable face recognition (XFR) is the problem of explaining the matches returned by a facial matcher, in order to provide insight into why a probe was matched with one identity over another. In this paper, we provide the first comprehensive benchmark and baseline evaluation for XFR. We define a new evaluation protocol called the ``inpainting game'', which is a curated set of 3648 triplets (probe, mate, nonmate) of 95 subjects, which differ by synthetically inpainting a chosen facial characteristic like the nose, eyebrows or mouth creating an inpainted nonmate. An XFR algorithm is tasked with generating a network attention map which best explains which regions in a probe image match with a mated image, and not with an inpainted nonmate for each triplet. This provides ground truth for quantifying what image regions contribute to face matching. Finally, we provide a comprehensive benchmark on this dataset comparing five state-of-the-art XFR algorithms on three facial matchers. This benchmark includes two new algorithms called subtree EBP and Density-based Input Sampling for Explanation (DISE) which outperform the state-of-the-art XFR by a wide margin."
From Shadow Segmentation to Shadow Removal
Authors: Hieu Le, Dimitris Samaras
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: The requirement for paired shadow and shadow-free images limits the size and diversity of shadow removal datasets and hinders the possibility of training large-scale, robust shadow removal algorithms. We propose a shadow removal method that can be trained using only shadow and non-shadow patches cropped from the shadow images themselves. Our method is trained via an adversarial framework, following a physical model of shadow formation. Our central contribution is a set of physics-based constraints that enables this adversarial training. Our method achieves competitive shadow removal results compared to state-of-the-art methods that are trained with fully paired shadow and shadow-free images. The advantages of our training regime are even more pronounced in shadow removal for videos. Our method can be fine-tuned on a testing video with only the shadow masks generated by a pre-trained shadow detector and outperforms state-of-the-art methods on this challenging test. We illustrate the advantages of our method on our proposed video shadow removal dataset. "
Diverse and Admissible Trajectory Prediction through Multimodal Context Understanding
Authors: Seong Hyeon Park, Gyubok Lee, Jimin Seo, Manoj Bhat, Minseok Kang, Jonathan Francis, Ashwin Jadhav, Paul Pu Liang, Louis-Philippe Morency
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Multi-agent trajectory forecasting in autonomous driving requires an agent to accurately anticipate the behaviors of the surrounding vehicles and pedestrians, for safe and reliable decision-making. Due to partial observability in these dynamical scenes, directly obtaining the posterior distribution over future agent trajectories remains a challenging problem. In realistic embodied environments, each agent's future trajectories should be both diverse since multiple plausible sequences of actions can be used to reach its intended goals, and admissible since they must obey physical constraints and stay in drivable areas. In this paper, we propose a model that synthesizes multiple input signals from the multimodal world|the environment's scene context and interactions between multiple surrounding agents|to best model all diverse and admissible trajectories. We compare our model with strong baselines and ablations across two public datasets and show a significant performance improvement over previous state-of-the-art methods. Lastly, we offer new metrics incorporating admissibility criteria to further study and evaluate the diversity of predictions. Codes are at: https://github.com/kami93/CMU-DATF"
CONFIG: Controllable Neural Face Image Generation
Authors: Marek Kowalski, Stephan J. Garbin, Virginia Estellers, Tadas Baltrušaitis, Matthew Johnson, Jamie Shotton
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Our ability to sample realistic natural images, particularly faces, has advanced by leaps and bounds in recent years, yet our ability to exert fine-tuned control over the generative process has lagged behind. If this new technology is to find practical uses, we need to achieve a level of control over generative networks which, without sacrificing realism, is on par with that seen in computer graphics and character animation. To this end we propose ConfigNet, a neural face model that allows for controlling individual aspects of output images in semantically meaningful ways and that is a significant step on the path towards finely-controllable neural rendering. ConfigNet is trained on real face images as well as synthetic face renders. Our novel method uses synthetic data to factorize the latent space into elements that correspond to the inputs of a traditional rendering pipeline, separating aspects such as head pose, facial expression, hair style, illumination, and many others which are very hard to annotate in real data. The real images, which are presented to the network without labels, extend the variety of the generated images and encourage realism. Finally, we propose an evaluation criterion using an attribute detection network combined with a user study and demonstrate state-of-the-art individual control over attributes in the output images."
Single View Metrology in the Wild
Authors: Rui Zhu, Xingyi Yang, Yannick Hold-Geoffroy, Federico Perazzi, Jonathan Eisenmann, Kalyan Sunkavalli, Manmohan Chandraker
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Most 3D reconstruction methods may only recover scene properties up to a global scale ambiguity. We present a novel approach to single view metrology that can recover the absolute scale of a scene represented by 3D heights of objects or camera height above the ground as well as camera parameters of orientation and field of view, using just a monocular image acquired in unconstrained condition. Our method relies on data-driven priors learned by a deep network specifically designed to imbibe weakly supervised constraints from the interplay of the unknown camera with 3D entities such as object heights, through estimation of bounding box projections. We leverage categorical priors for objects such as humans or cars that commonly occur in natural images, as references for scale estimation. We demonstrate state-of-the-art qualitative and quantitative results on several datasets as well as applications including virtual object insertion. Furthermore, the perceptual quality of our outputs is validated by a user study."
Procedure Planning in Instructional Videos
Authors: Chien-Yi Chang, De-An Huang, Danfei Xu, Ehsan Adeli, Li Fei-Fei, Juan Carlos Niebles
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: In this paper, we study the problem of procedure planning in instructional videos, which can be seen as the first step towards enabling autonomous agents to plan for complex tasks in everyday settings such as cooking. Given the current visual observation of the world and a visual goal, we ask the question ""What actions need to be taken in order to achieve the goal?"". The key technical challenge is how to learn structured and plannable state and action spaces directly from unstructured real videos. We address this challenge by proposing Dual Dynamics Networks (DDN), a framework that explicitly leverages the structured priors imposed by the conjugate relationships between states and actions in a learned plannable latent space. We evaluate our method on real-world instructional videos. Our experiments show that DDN learns plannable representations that lead to better planning performance compared to existing planning approaches and neural network policies."
Funnel Activation for Visual Recognition
Authors: Ningning Ma, Xiangyu Zhang, Jian Sun
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: We present a conceptually simple but effective funnel activation for image recognition tasks, called Funnel activation (FReLU), that extends ReLU and PReLU to a 2D activation by adding a negligible overhead of spatial condition. The forms of ReLU and PReLU are y = max(x, 0) and y = max(x, px), respectively, while FReLU is in the form of y = max(x,T(x)), where T(·) is the 2D spatial condition. Moreover, the spatial condition achieves a pixel-wise modeling capacity in a simple way, capturing complicated visual layouts with regular convolutions. We conduct experiments on ImageNet, COCO detection, and semantic segmentation tasks, showing great improvements and robustness of FReLU in the visual recognition tasks."
GIQA: Generated Image Quality Assessment
Authors: Shuyang Gu, Jianmin Bao, Dong Chen, Fang Wen
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Generative adversarial networks (GANs) have achieved impressive results today, but not all generated images are perfect. A number of quantitative criteria have recently emerged for generative model, but none of them are designed for a single generated image. In this paper, we propose a new research topic, Generated Image Quality Assessment (GIQA), which quantitatively evaluates the quality of each generated image. We introduce three GIQA algorithms from two perspectives: learning-based and data-based. We evaluate a number of images generated by various recent GAN models on different datasets and demonstrate that they are consistent with human assessments. Furthermore, GIQA is available to many applications, like separately evaluate the realism and diversity of generative models, and enable online hard negative mining (OHEM) in the training of GANs to improve the results."
Adversarial Continual Learning
Authors: Sayna Ebrahimi, Franziska Meier, Roberto Calandra, Trevor Darrell, Marcus Rohrbach
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Continual learning aims to learn new tasks without forgetting previously learned ones. We hypothesize that representations learned to solve each task in a sequence have a shared structure while containing some task-specific properties. We show that shared features are significantly less prone to forgetting and propose a novel hybrid continual learning framework that learns a disjoint representation for task-invariant and task-specific features required to solve a sequence of tasks. Our model combines architecture growth to prevent forgetting of task-specific skills and an experience replay approach to preserve shared skills. We demonstrate our hybrid approach is effective in avoiding forgetting and show it is superior to both architecture-based and memory-based approaches on class incrementally learning of a single dataset as well as a sequence of multiple datasets in image classification. Our code is available at https://github.com/facebookresearch/Adversarial-Continual-Learning
Adapting Object Detectors with Conditional Domain Normalization
Authors: Peng Su, Kun Wang, Xingyu Zeng, Shixiang Tang, Dapeng Chen, Di Qiu, Xiaogang Wang
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Real-world object detectors are often challenged by the domain gaps between different datasets. In this work, we present the Conditional Domain Normalization (CDN) to bridge the domain distribution gap. CDN is designed to encode different domain inputs into a shared latent space, where the features from different domains carry the same domain attribute. To achieve this, we first disentangle the domain-specific attribute out of the semantic features from source domain via a domain embedding module, which learns a domain-vector to characterize the domain attribute information. Then this domain-vector is used to encode the features from target domain through a conditional normalization, resulting in different domains' features carrying the same domain attribute. We incorporate CDN into various convolution stages of an object detector to adaptively address the domain shifts of different level's representation. In contrast to existing adaptation works that conduct domain confusion learning on semantic features to remove domain-specific factors, CDN aligns different domain distributions by modulating the semantic features of target domains conditioned on the learned domain-vector of the source domain. Extensive experiments show that CDN outperforms existing methods remarkably on both real-to-real and synthetic-to-real adaptation benchmarks, including 2D image detection and 3D point cloud detection."
HARD-Net: Hardness-AwaRe Discrimination Network for 3D Early Activity Prediction
Authors: Tianjiao Li, Jun Liu, Wei Zhang, Lingyu Duan
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Predicting the class label from the partially observed activity sequence is a very hard task, as the observed early segments of different activities can be very similar. In this paper, we propose a novel Hardness-AwaRe Discrimination Network (HARD-Net) to specifically investigate the relationships between the similar activity pairs that are hard to be discriminated. Specifically, a Hard Instance-Interference Class (HI-IC) bank is designed, which dynamically records the hard similar pairs. Based on the HI-IC bank, a novel adversarial learning scheme is proposed to train our HARD-Net, which thus grants our network with the strong capability in mining subtle discrimination information for 3D early activity prediction. We evaluate our proposed HARD-Net on two public activity datasets and achieve state-of-the-art performance."
Pseudo RGB-D for Self-Improving Monocular SLAM and Depth Prediction
Authors: Lokender Tiwari, Pan Ji, Quoc-Huy Tran, Bingbing Zhuang, Saket Anand, Manmohan Chandraker
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Classical monocular Simultaneous Localization And Mapping (SLAM) and the recently emerging convolutional neural networks (CNNs) for monocular depth prediction represent two largely disjoint approaches towards building a 3D map of the surrounding environment. In this paper, we demonstrate that the coupling of these two by leveraging the strengths of each mitigates the other’s shortcomings. Specifically, we propose a joint narrow and wide baseline based self-improving framework, where on the one hand the CNN-predicted depth is leveraged to perform $ extit{pseudo}$ RGB-D feature-based SLAM, leading to better accuracy and robustness than the monocular RGB SLAM baseline. On the other hand, the bundle-adjusted 3D scene structures and camera poses from the more principled geometric SLAM are injected back into the depth network through novel wide baseline losses proposed for improving the depth prediction network, which then continues to contribute towards better pose and 3D structure estimation in the next iteration. We emphasize that our framework only requires $ extit{ unlabeled monocular}$ videos in both training and inference stages, and yet is able to outperform state-of-the-art self-supervised $ extit{monocular}$ and $ extit{stereo}$ depth prediction networks (e.g, Monodepth2) and feature based monocular SLAM system (i.e, ORB-SLAM). Extensive experiments on KITTI and TUM RGB-D datasets verify the superiority of our self-improving geometry-CNN framework."
Interpretable and Generalizable Person Re-Identification with Query-Adaptive Convolution and Temporal Lifting
Authors: Shengcai Liao, Ling Shao
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: For person re-identification, existing deep networks often focus on representation learning. However, without transfer learning, the learned model is fixed as is, which is not adaptable for handling various unseen scenarios. In this paper, beyond representation learning, we consider how to formulate person image matching directly in deep feature maps. We treat image matching as finding local correspondences in feature maps, and construct query-adaptive convolution kernels on the fly to achieve local matching. In this way, the matching process and results are interpretable, and this explicit matching is more generalizable than representation features to unseen scenarios, such as unknown misalignments, pose or viewpoint changes. To facilitate end-to-end training of this architecture, we further build a class memory module to cache feature maps of the most recent samples of each class, so as to compute image matching losses for metric learning. Through direct cross-dataset evaluation, the proposed Query-Adaptive Convolution (QAConv) method gains large improvements over popular learning methods (about 10%+ mAP), and achieves comparable results to many transfer learning methods. Besides, a model-free temporal cooccurrence based score weighting method called TLift is proposed, which improves the performance to a further extent, achieving state-of-the-art results in cross-dataset person re-identification. Code is available at https://github.com/ShengcaiLiao/QAConv"
Self-supervised Bayesian Deep Learning for Image Recovery with Applications to Compressive Sensing
Authors: Tongyao Pang, Yuhui Quan, Hui Ji
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: In recent years, deep learning emerges as one promising technique for solving many ill-posed inverse problems in image recovery, and most deep-learning-based solutions are based on supervised learning. Motivated by the practical value of reducing the cost and complexity of constructing labeled training datasets, this paper proposed a self-supervised deep learning approach for image recovery, which is dataset-free. Built upon Bayesian deep network, the proposed method trains a network with random weights that predicts the target image for recovery with uncertainty. Such uncertainty enables the prediction of the target image with small mean squared error by averaging multiple predictions. The proposed method is applied for image reconstruction in compressive sensing (CS), i.e., reconstructing an image from few measurements. The experiments showed that the proposed dataset-free deep learning method not only significantly outperforms traditional non-learning methods, but also is very competitive to the state-of-the-art supervised deep learning methods, especially when the measurements are few and noisy."
Graph-PCNN: Two Stage Human Pose Estimation with Graph Pose Refinement
Authors: Jian Wang, Xiang Long, Yuan Gao, Errui Ding, Shilei Wen
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Recently, most of the state-of-the-art human pose estimation methods are based on heatmap regression. The final coordinates of keypoints are obtained by decoding heatmap directly. In this paper, we aim to find a better approach to get more accurate localization results. We mainly put forward two suggestions for improvement: 1) different features and methods should be applied for rough and accurate localization, 2) relationship between keypoints should be considered. Specifically, we propose a two-stage graph-based and model-agnostic framework, called Graph-PCNN, with a localization subnet and a graph pose refinement module added onto the original heatmap regression network. In the first stage, heatmap regression network is applied to obtain a rough localization result, and a set of proposal keypoints, called guided points, are sampled. In the second stage, for each guided point, different visual feature is extracted by the localization subnet. The relationship between guided points is explored by the graph pose refinement module to get more accurate localization results. Experiments show that Graph-PCNN can be used in various backbones to boost the performance by a large margin. Without bells and whistles, our best model can achieve a new state-of-the-art 76.8% AP on COCO test-dev split. "