GenAI4ELab/papercli-papers huggingface dataset:
Measuring Generalisation to Unseen Viewpoints, Articulations, Shapes and Objects for 3D Hand Pose Estimation under Hand-Object Interaction
Authors: Anil Armagan; Guillermo Garcia-Hernando; Seungryul Baek; Shreyas Hampali; Mahdi Rad; Zhaohui Zhang; Shipeng Xie; MingXiu Chen; Boshen Zhang; Fu Xiong; Yang Xiao; Zhiguo Cao; Junsong Yuan; Pengfei Ren⁸; Weiting Huang⁸; Haifeng Sun⁸; Marek Hrúz⁹; Jakub Kanis⁹; Zdeněk Krňoul⁹; Qingfu Wan; Shile Li; Linlin Yang; Dongheui Lee; Angela Yao; Weiguo Zhou; Sijia Mei; Yunhui Liu; Adrian Spurr; Umar Iqbal; Pavlo Molchanov; Philippe Weinzaepfel; Romain Brégier; Grégory Rogez; Vincent Lepetit; Tae-Kyun Kim
Venue: ECCV 2020 (main)

Abstract: Articulations, Shapes and Objects for 3D Hand Pose Estimation under Hand-Object Interaction","We study how well different types of approaches generalise in the task of 3D hand pose estimation under single hand scenarios and hand-object interaction. We show that the accuracy of state-of-the-art methods can drop, and that they fail mostly on poses absent from the training set. Unfortunately, since the space of hand poses is highly dimensional, it is inherently not feasible to cover the whole space densely, despite recent efforts in collecting large-scale training datasets. This sampling problem is even more severe when hands are interacting with objects and/or inputs are RGB rather than depth images, as RGB images also vary with lighting conditions and colors. To address these issues, we designed a public challenge (HANDS'19) to evaluate the abilities of current 3D hand pose estimators~(HPEs) to interpolate and extrapolate the poses of a training set. More exactly, HANDS'19 is designed (a) to evaluate the influence of both depth and color modalities on 3D hand pose estimation, under the presence or absence of objects; (b) to assess the generalisation abilities w.r.t. four main axes: shapes, articulations, viewpoints, and objects; (c) to explore the use of a synthetic hand models to fill the gaps of current datasets. Through the challenge, the overall accuracy has dramatically improved over the baseline, especially on extrapolation tasks, from 27mm to 13mm mean joint error. Our analyses highlight the impacts of: ensemble approaches, the use of a parametric 3D hand model (MANO), and different HPE methods/backbones."
Disentangling Multiple Features in Video Sequences using Gaussian Processes in Variational Autoencoders
Authors: Sarthak Bhagat; Shagun Uppal; Zhuyun Yin; Nengli Lim
Venue: ECCV 2020 (main)

Abstract: We introduce MGP-VAE (Multi-disentangled-features Gaussian Processes Variational AutoEncoder), a variational autoencoder which uses Gaussian processes (GP) to model the latent space for the unsupervised learning of disentangled representations in video sequences. We improve upon previous work by establishing a framework by which multiple features, static or dynamic, can be disentangled. Specifically we use fractional Brownian motions (fBM) and Brownian bridges (BB) to enforce an inter-frame correlation structure in each independent channel, and show that varying this structure enables one to capture different factors of variation in the data. We demonstrate the quality of our representations with experiments on three publicly available datasets, and also quantify the improvement using a video prediction task. Moreover, we introduce a novel geodesic loss function which takes into account the curvature of the data manifold to improve learning. Our experiments show that the combination of the improved representations with the novel loss function enable MGP-VAE to outperform the baselines in video prediction."
SEN: A Novel Feature Normalization Dissimilarity Measure for Prototypical Few-Shot Learning Networks
Authors: Van Nhan Nguyen; Sigurd Løkse; Kristoffer Wickstrøm; Michael Kampffmeyer; Davide Roverso; Robert Jenssen
Venue: ECCV 2020 (main)

Abstract: In this paper, we equip Prototypical Networks (PNs) with a novel dissimilarity measure to enable discriminative feature normalization for few-shot learning. The embedding onto the hypersphere requires no direct normalization and is easy to optimize. Our theoretical analysis shows that the proposed dissimilarity measure, denoted the Squared root of the Euclidean distance and the Norm distance (SEN), forces embedding points to be attracted to its correct prototype, while being repelled from all other prototypes, keeping the norm of all points the same. The resulting SEN PN outperforms the regular PN with a considerable margin, with no additional parameters as well as with negligible computational overhead."
Kinematic 3D Object Detection in Monocular Video
Authors: Garrick Brazil; Gerard Pons-Moll; Xiaoming Liu; Bernt Schiele
Venue: ECCV 2020 (main)

Abstract: Perceiving the physical world in 3D is fundamental for self-driving applications. Although temporal motion is an invaluable resource to human vision for detection, tracking, and depth perception, such features have not been thoroughly utilized in modern 3D object detectors. In this work, we propose a novel method for monocular video-based 3D object detection which carefully leverages kinematic motion to improve precision of 3D localization. Specifically, we first propose a novel decomposition of object orientation as well as a self-balancing 3D confidence. We show that both components are critical to enable our kinematic model to work effectively. Collectively, using only a single model, we efficiently leverage 3D kinematics from monocular videos to improve the overall localization precision in 3D object detection while also producing useful by-products of scene dynamics (ego-motion and per-object velocity). We achieve state-of-the-art performance on monocular 3D object detection and the Bird's Eye View tasks within the KITTI self-driving dataset. "
Describing Unseen Videos via Multi-Modal Cooperative Dialog Agents
Authors: Ye Zhu; Yu Wu; Yi Yang; Yan Yan
Venue: ECCV 2020 (main)

Abstract: With the arising concerns for the AI systems provided with direct access to abundant sensitive information, researchers seek to develop more reliable AI with implicit information sources. To this end, in this paper, we introduce a new task called video description via two multi-modal cooperative dialog agents, whose ultimate goal is for one conversational agent to describe an unseen video based on the dialog and two static frames. Specifically, one of the intelligent agents - Q-BOT - is given two static frames from the beginning and the end of the video, as well as a finite number of opportunities to ask relevant natural language questions before describing the unseen video. A-BOT, the other agent who has already seen the entire video, assists Q-BOT to accomplish the goal by providing answers to those questions. We propose a QA-Cooperative Network with a dynamic dialog history update learning mechanism to transfer knowledge from A-BOT to Q-BOT, thus helping Q-BOT to better describe the video. Extensive experiments demonstrate that Q-BOT can effectively learn to describe an unseen video by the proposed model and the cooperative learning method, achieving the promising performance where Q-BOT is given the full ground truth history dialog."
SACA Net: Cybersickness Assessment of Individual Viewers for VR Content via Graph-based Symptom Relation Embedding
Authors: Sangmin Lee; Jung Uk Kim; Hak Gu Kim; Seongyeop Kim; Yong Man Ro
Venue: ECCV 2020 (main)

Abstract: Recently, cybersickness assessment for VR content is required to deal with viewing safety issues. Assessing physical symptoms of individual viewers is challenging but important to provide detailed and personalized guides for viewing safety. In this paper, we propose a novel symptom-aware cybersickness assessment network (SACA Net) that quantifies physical symptom levels for assessing cybersickness of individual viewers. The SACA Net is designed to utilize the relational characteristics of symptoms for complementary effects among relevant symptoms. The proposed network consists of three main parts: a stimulus symptom context guider, a physiological symptom guider, and a symptom relation embedder. The stimulus symptom context guider and the physiological symptom guider extract symptom features from VR content and human physiology, respectively. The symptom relation embedder refines the stimulus-response symptom features to effectively predict cybersickness by embedding relational characteristics with graph formulation. For validation, we utilize two public 360-degree video datasets that contain cybersickness scores and physiological signals. Experimental results show that the proposed method is effective in predicting human cybersickness with physical symptoms. Further, latent relations among symptoms are interpretable by analyzing relational weights in the proposed network."
End-to-End Low Cost Compressive Spectral Imaging with Spatial-Spectral Self-Attention
Authors: Ziyi Meng; Jiawei Ma; Xin Yuan
Venue: ECCV 2020 (main)

Abstract: Coded aperture snapshot spectral imaging (CASSI) is an effective tool to capture real-world 3D hyperspectral images. While a number of existing work has been conducted for hardware and algorithm design, we make a step towards the low-cost solution that enjoys video-rate high-quality reconstruction. To make solid progress on this challenging yet under-investigated task, we reproduce a stable single disperser (SD) CASSI system to gather large-scale real-world CASSI data and propose a novel deep convolutional network to carry out the real-time reconstruction by using self-attention. In order to jointly capture the self-attention across different dimensions in hyperspectral images (i.e., channel-wise spectral correlation and non-local spatial regions), we propose Spatial-Spectral Self-Attention (TSA) to process each dimension sequentially, yet in an order-independent manner. We employ TSA in an encoder-decoder network, dubbed TSA-Net, to reconstruct the desired 3D cube. Furthermore, we investigate how noise affects the results and propose to add shot noise in model training, which improves the real data results significantly. We hope our large-scale CASSI data serve as a benchmark in future research and our TSA model as a baseline in deep learning based reconstruction algorithms."
Know Your Surroundings: Exploiting Scene Information for Object Tracking
Authors: Goutam Bhat; Martin Danelljan; Luc Van Gool; Radu Timofte
Venue: ECCV 2020 (main)

Abstract: Current state-of-the-art trackers rely only on a target appearance model in order to localize the object in each frame. Such approaches are however prone to fail in case of e.g. fast appearance changes or presence of distractor objects, where a target appearance model alone is insufficient for robust tracking. Having the knowledge about the presence and locations of other objects in the surrounding scene can be highly beneficial in such cases. This scene information can be propagated through the sequence and used to, for instance, explicitly avoid distractor objects and eliminate target candidate regions.In this work, we propose a novel tracking architecture which can utilize scene information for tracking. Our tracker represents such information as dense localized state vectors, which can encode, for example, if a local region is target, background, or distractor. These state vectors are propagated through the sequence and combined with the appearance model output to localize the target. Our network is learned to effectively utilize the scene information by directly maximizing tracking performance on video segments. The proposed approach sets a new state-of-the-art on 3 tracking benchmarks, achieving an AO score of 63.6% on the recent GOT-10k dataset."
Practical Detection of Trojan Neural Networks: Data-Limited and Data-Free Cases
Authors: Ren Wang; Gaoyuan Zhang; Sijia Liu; Pin-Yu Chen; Jinjun Xiong; Meng Wang
Venue: ECCV 2020 (main)

Abstract: When the training data are maliciously tampered, the predictions of the acquired deep neural network (DNN) can be manipulated by an adversary known as the Trojan attack (or poisoning backdoor attack). The lack of robustness of DNNs against Trojan attacks could significantly harm real-life machine learning (ML) systems in downstream applications, therefore posing widespread concern to their trustworthiness. In this paper, we study the problem of the Trojan network (TrojanNet) detection in the data-scarce regime, where only the weights of a trained DNN are accessed by the detector. We first propose a data-limited TrojanNet detector (TND), when only a few data samples are available for TrojanNet detection. We show that an effective data-limited TND can be established by exploring connections between Trojan attack and prediction-evasion adversarial attacks including per-sample attack as well as all-sample universal attack. In addition, we propose a data-free TND, which can detect a TrojanNet without accessing any data samples. We show that such a TND can be built by leveraging the internal response of hidden neurons, which exhibits the Trojan behavior even at random noise inputs. The effectiveness of our proposals is evaluated by extensive experiments under different model architectures and datasets including CIFAR-10, GTSRB, and ImageNet."
Anatomy-Aware Siamese Network: Exploiting Semantic Asymmetry for Accurate Pelvic Fracture Detection in X-ray Images
Authors: Haomin Chen; Yirui Wang; Kang Zheng; Weijian Li; Chi-Tung Chang; Adam P. Harrison; Jing Xiao; Gregory D. Hager; Le Lu; Chien-Hung Liao; Shun Miao
Venue: ECCV 2020 (main)

Abstract: Trauma PXR are essential for instantaneous pelvic bone fracture detection. However, small, pathologically critical fractures can be missed, even by experienced clinicians, under the very limited diagnosis times allowed in urgent care. As a result, fracture CAD has very high demands to save time and assist physicians to detect (otherwise) missed fractures more accurately and reliably. In this work, we present a new approach to fracture detection that uses a Siamese network to take advantage of the anatomical symmetry of pelvic structures to improve fracture detection. We show that symmetric alignment at the network feature level makes Siamese learning more spatially accurate and also reduces the influence of imaging artifacts. Metric learning on Siamese deep features further improves CAD differentiation power with the anatomical symmetry cue. We evaluate our method on 2,359 PXR patients, reporting an area under the ROC curve value of 0.9771, the highest among state-of-the-art fracture detection methods. "
DeepLandscape: Adversarial Modeling of Landscape Videos
Authors: Elizaveta Logacheva; Roman Suvorov; Oleg Khomenko; Anton Mashikhin; Victor Lempitsky
Venue: ECCV 2020 (main)

Abstract: We build a new model of landscape videos that can be trained on a mixture of static landscape images as well as landscape animations. Our architecture extends StyleGAN model by augmenting it with parts that allow to model dynamic changes in a scene. Once trained, our model can be used to generate realistic time-lapse landscape videos with moving objects and time-of-the-day changes. Furthermore, by fitting the learned models to a static landscape image, the latter can be reenacted in a realistic way. We propose simple but necessary modifications to StyleGAN inversion procedure, which lead to in-domain latent codes and allow to manipulate real images. Quantitative comparisons and user studies suggest that our model produces more compelling animations of given photographs than previously proposed methods. The results of our approach including comparisons with prior art can be seen in supplementary materials and on the project page https://saic-mdal.github.io/deep-landscape/."
GANwriting: Content-Conditioned Generation of Styled Handwritten Word Images
Authors: Lei Kang; Pau Riba; Yaxing Wang; Marçal Rusiñol; Alicia Fornés; Mauricio Villegas
Venue: ECCV 2020 (main)

Abstract: Although current image generation methods have reached impressive quality levels, they are still unable to produce plausible yet diverse images of handwritten words. On the contrary, when writing by hand, a great variability is observed across different writers, and even when analyzing words scribbled by the same individual, involuntary variations are conspicuous. In this work, we take a step closer to producing realistic and varied artificially rendered handwritten words. We propose a novel method that is able to produce credible handwritten word images by conditioning the generative process with both calligraphic style features and textual content. Our generator is guided by three complementary learning objectives: to produce realistic images, to imitate a certain handwriting style and to convey a specific textual content. Our model is unconstrained to any predefined vocabulary, being able to render whatever input word. Given a sample writer, it is also able to mimic its calligraphic features in a few-shot setup. We significantly advance over prior art and demonstrate with qualitative, quantitative and human-based evaluations the realistic aspect of our synthetically produced images."
Spatial-Angular Interaction for Light Field Image Super-Resolution
Authors: Yingqian Wang; Longguang Wang; Jungang Yang; Wei An; Jingyi Yu; Yulan Guo
Venue: ECCV 2020 (main)

Abstract: Light field (LF) cameras record both intensity and directions of light rays, and capture scenes from a number of viewpoints. Both information within each perspective (i.e., spatial information) and among different perspectives (i.e., angular information) is beneficial to image super-resolution (SR). In this paper, we propose a spatial-angular interactive network (namely, LF-InterNet) for LF image SR. Specifically, spatial and angular features are first separately extracted from input LFs, and then repetitively interacted to progressively incorporate spatial and angular information. Finally, the interacted features are fused to superresolve each sub-aperture image. Experimental results demonstrate the superiority of LF-InterNet over the state-of-the-art methods, i.e., our method can achieve high PSNR and SSIM scores with low computational cost, and recover faithful details in the reconstructed images."
BATS: Binary ArchitecTure Search
Authors: Adrian Bulat; Brais Martinez; Georgios Tzimiropoulos
Venue: ECCV 2020 (main)

Abstract: This paper proposes Binary ArchitecTure Search (BATS), a framework that drastically reduces the accuracy gap between binary neural networks and their real-valued counterparts by means of Neural Architecture Search (NAS). We show that directly applying NAS to the binary domain provides very poor results. To alleviate this, we describe, to our knowledge, for the first time, the 3 key ingredients for successfully applying NAS to the binary domain. Specifically, we (1) introduce and design a novel binary-oriented search space, (2) propose a new mechanism for controlling and stabilising the resulting searched topologies, (3) propose and validate a series of new search strategies for binary networks that lead to faster convergence and lower search times. Experimental results demonstrate the effectiveness of the proposed approach and the necessity of searching in the binary space directly. Moreover, (4) we set a new state-of-the-art for binary neural networks on CIFAR10, CIFAR100 and ImageNet datasets. Code will be made available."
A Closer Look at Local Aggregation Operators in Point Cloud Analysis
Authors: Ze Liu(†); Han Hu; Yue Cao; Zheng Zhang; Xin Tong
Venue: ECCV 2020 (main)

Abstract: Recent advances of network architecture for point cloud processing are mainly driven by new designs of local aggregation operators. However, the impact of these operators to network performance is not carefully investigated due to different overall network architecture and implementation details in each solution. Meanwhile, most of operators are only applied in shallow architectures. In this paper, we revisit the representative local aggregation operators and study their performance using the same deep residual architecture. Our investigation reveals that depsite the different designs of these operators, all of these operators make surprisingly similar contributions to the network performance under the same network input and feature numbers and result in the state-of-the-art accuracy on standard benchmarks. This finding stimulate us to rethink the necessity of sophisticated design of local aggregation operator for point cloud processing. To this end, we propose a simple local aggregation operator without learnable weights, named Position Pooling (PosPool), which performs similarly or slightly better than existing sophisticated operators. In particular, a simple deep residual network with PosPool layers exhibits outstanding performance on all benchmarks, which outperforms the previous state-of-the methods on the challenging PartNet datasets by a large margin (6.4 mIoU). "
Look here! A parametric learning based approach to redirect visual attention
Authors: Youssef A. Mejjati; Celso F. Gomez; Kwang In Kim; Eli Shechtman; Zoya Bylinskii
Venue: ECCV 2020 (main)

Abstract: Across photography, marketing, and website design, being able to direct the viewer's attention is a powerful tool. Motivated by professional workflows, we introduce an automatic method to make an image region more attention-capturing via subtle image edits that maintain realism and fidelity to the original. From an input image and a user-provided mask, our GazeShiftNet model predicts a distinct set of global parametric transformations to be applied to the foreground and background image regions separately. We present the results of quantitative and qualitative experiments that demonstrate improvements over prior state-of-the-art. In contrast to existing attention shifting algorithms, our global parametric approach better preserves image semantics and avoids typical generative artifacts. Our edits enable inference at interactive rates on any image size, and easily generalize to videos. Extensions of our model allow for multi-style edits and the ability to both increase and attenuate attention in an image region. Furthermore, users can customize the edited images by dialing the edits up or down via interpolations in parameter space. This paper presents a practical tool that can simplify future image editing pipelines."
Variational Diffusion Autoencoders with Random Walk Sampling
Authors: Henry Li; Ofir Lindenbaum; Xiuyuan Cheng; Alexander Cloninger
Venue: ECCV 2020 (main)

Abstract: Variational autoencoders (VAEs) and generative adversarial networks (GANs) enjoy an intuitive connection to manifold learning: in training the decoder/generator is optimized to approximate a homeomorphism between the data distribution and the sampling space. This is a construction that strives to define the data manifold. A major obstacle to VAEs and GANs, however, is choosing a suitable prior that matches the data topology. Well-known consequences of poorly picked priors are posterior and mode collapse. To our knowledge, no existing method sidesteps this user choice. Conversely, extit{diffusion maps} automatically infer the data topology and enjoy a rigorous connection to manifold learning, but do not scale easily or provide the inverse homeomorphism (i.e. decoder/generator). We propose a method that combines these approaches into a generative model that inherits the asymptotic guarantees of extit{diffusion maps} while preserving the scalability of deep models. We prove approximation theoretic results for the dimension dependence of our proposed method. Finally, we demonstrate the effectiveness of our method with various real and synthetic datasets."