GenAI4ELab/papercli-papers huggingface dataset:
Learning Object Permanence from Video
Authors: Aviv Shamsian; Ofri Kleinfeld; Amir Globerson; Gal Chechik
Venue: ECCV 2020 (main)

Abstract: Object Permanence allows people to reason about the location of non-visible objects, by understanding that they continue to exist even when not perceived directly. Object Permanence is critical for building a model of the world, since objects in natural visual scenes dynamically occlude and contain each-other. Intensive studies in developmental psychology, suggesting that object permanence is a challenging task that is learned through extensive experience.Here we introduce the setup of learning Object Permanence from data. We explain why the learning problem should be dissected into four components, where objects are (1) visible, (2) occluded, (3) contained by another object and (4) carried by a containing object. The fourth subtask, where a target object is carried by a containing object, is particularly challenging because it requires a system to reason about a moving location of an invisible object. We then present a unified deep architecture that learns to predict object location under these four scenarios. We evaluate the architecture and system on a new dataset based on CATER, and find that it outperforms previous methods and various baselines. "
Adaptive Text Recognition through Visual Matching
Authors: Chuhan Zhang; Ankush Gupta; Andrew Zisserman
Venue: ECCV 2020 (main)

Abstract: This work addresses the problems of generalization and flexibility for text recognition in documents. We introduce a new model that exploits the repetitive nature of characters in languages, and decouples the visual decoding and linguistic modelling stages through intermediate representations in the form of similarity maps. By doing this, we turn text recognition into a visual matching problem, thereby achieving generalization in appearance and flexibility in classes. We evaluate the model on both synthetic and real datasets across different languages and alphabets, and show that it can handle challenges that traditional architectures are unable to solve without expensive re-training, including: (i) it can change the number of classes simply by changing the exemplars; and (ii) it can generalize to novel languages and characters (not in the training data) simply by providing a new glyph exemplar set. In essence, it is able to carry out one-shot sequence recognition. We also demonstrate that the model can generalize to unseen fonts without requiring new exemplars from them."
Actions as Moving Points
Authors: Yixuan Li; Zixu Wang; Limin Wang; Gangshan Wu
Venue: ECCV 2020 (main)

Abstract: The existing action tubelet detectors often depend on heuristic anchor design and placement, which might be computationally expensive and sub-optimal for precise localization. In this paper, we present a conceptually simple, computationally efficient, and more precise action tubelet detection framework, termed as MovingCenter Detector (MOC-detector), by treating an action instance as a trajectory of moving points. Based on the insight that movement information could simplify and assist action tubelet detection, our MOC-detector is composed of three crucial head branches: (1) Center Branch for instance center detection and action recognition, (2) Movement Branch for movement estimation at adjacent frames to form trajectories of moving points, (3) Box Branch for spatial extent detection by directly regressing bounding box size at each estimated center. These three branches work together to generate the tubelet detection results, which could be further linked to yield video-level tubes with a matching strategy. Our MOC-detector outperforms the existing state-of-the-art methods for both metrics of frame-mAP and video-mAP on the JHMDB and UCF101-24 datasets. The performance gap is more evident for higher video IoU, demonstrating that our MOC-detector is particularly effective for more precise action detection. We provide the code at https://github.com/MCG-NJU/MOC-Detector . "
Learning to Exploit Multiple Vision Modalities by Using Grafted Networks
Authors: Yuhuang Hu; Tobi Delbruck; Shih-Chii Liu
Venue: ECCV 2020 (main)

Abstract: Novel vision sensors such as thermal, hyperspectral, polarization, and event cameras provide information that is not available from conventional intensity cameras. An obstacle to using these sensors with current powerful deep neural networks is the lack of large labeled training datasets. This paper proposes a Network Grafting Algorithm (NGA), where a new front end network driven by unconventional visual inputs replaces the front end network of a pretrained deep network that processes intensity frames. The self-supervised training uses only synchronously-recorded intensity frames and novel sensor data to maximize feature similarity between the pretrained network and the grafted network. We show that the enhanced grafted network reaches competitive average precision (AP50) scores to the pretrained network on an object detection task using thermal and event camera datasets, with no increase in inference costs. Particularly, the grafted network driven by thermal frames showed a relative improvement of 49.11% over the use of intensity frames. The grafted front end has only 5--8% of the total parameters and can be trained in a few hours on a single GPU equivalent to 5% of the time that would be needed to train the entire object detector from labeled data. NGA allows new vision sensors to capitalize on previously pretrained powerful deep models, saving on training cost and widening a range of applications for novel sensors."
Geometric Correspondence Fields: Learned Differentiable Rendering for 3D Pose Refinement in the Wild
Authors: Alexander Grabner; Yaming Wang; Peizhao Zhang; Peihong Guo; Tong Xiao; Peter Vajda; Peter M. Roth; Vincent Lepetit
Venue: ECCV 2020 (main)

Abstract: We present a novel 3D pose refinement approach based on differentiable rendering for objects of arbitrary categories in the wild. In contrast to previous methods, we make two main contributions: First, instead of comparing real-world images and synthetic renderings in the RGB or mask space, we compare them in a feature space optimized for 3D pose refinement. Second, we introduce a novel differentiable renderer that learns to approximate the rasterization backward pass from data instead of relying on a hand-crafted algorithm. For this purpose, we predict deep cross-domain correspondences between RGB images and 3D model renderings in the form of what we call geometric correspondence fields. These correspondence fields serve as pixel-level gradients which are analytically propagated backward through the rendering pipeline to perform a gradient-based optimization directly on the 3D pose. In this way, we precisely align 3D models to objects in RGB images which results in significantly improved 3D pose estimates. We evaluate our approach on the challenging Pix3D dataset and achieve up to 55% relative improvement compared to state-of-the-art refinement methods in multiple metrics."
3D Fluid Flow Reconstruction Using Compact Light Field PIV
Authors: Zhong Li; Yu Ji; Jingyi Yu; Jinwei Ye
Venue: ECCV 2020 (main)

Abstract: Particle Imaging Velocimetry (PIV) estimates the fluid flow by analyzing the motion of injected particles. The problem is challenging as the particles lie at different depths but have similar appearances. Tracking a large number of moving particles is particularly difficult due to the heavy occlusion. In this paper, we present a PIV solution that uses a compact lenslet-based light field camera to track dense particles floating in the fluid and reconstruct the 3D fluid flow. We exploit the focal symmetry property in the light field focal stacks for recovering the depths of similar-looking particles. We further develop a motion-constrained optical flow estimation algorithm by enforcing the local motion rigidity and the Navier-Stoke fluid constraint. Finally, the estimated particle motion trajectory is used to visualize the 3D fluid flow. Comprehensive experiments on both synthetic and real data show that using a compact light field camera, our technique can recover dense and accurate 3D fluid flow. "
Contextual Diversity for Active Learning
Authors: Sharat Agarwal; Himanshu Arora; Saket Anand; Chetan Arora
Venue: ECCV 2020 (main)

Abstract: Requirement of large annotated datasets restricts the use of deep convolutional neural networks (CNNs) for many practical applications. The problem can be mitigated by using active learning (AL) techniques which, under a given annotation budget, allow to select a subset of data that yields maximum accuracy upon fine tuning. State of the art AL approaches typically relies on measures of visual diversity or prediction uncertainty, which are unable to effectively capture the variations in the spatial context. On the other hand, modern CNN architectures make heavy use of spatial context for achieving highly accurate predictions. Since the context is difficult to evaluate in the absence of ground-truth labels, we introduce the notion of contextual diversity that captures the confusion associated with spatially co-occurring classes. Contextual Diversity (CD) hinges on a crucial observation that the probability vector predicted by a CNN for a region of interest typically contains information from a larger receptive field. Exploiting this observation, we use the proposed CD measure within two AL frameworks: (1) a core-set based strategy and (2) a reinforcement learning based policy, for the active frame selection. Our extensive empirical evaluation establishes state of the art results for active learning on benchmark datasets of Semantic Segmentation, Object Detection and Image classification. Our ablation studies show clear advantages of using contextual diversity for active learning."
Temporal Aggregate Representations for Long-Range Video Understanding
Authors: Fadime Sener; Dipika Singhania; Angela Yao
Venue: ECCV 2020 (main)

Abstract: Future prediction, especially in long-range videos, requires reasoning from current and past observations. In this work, we address questions of temporal extent, scaling, and level of semantic abstraction with a flexible multi-granular temporal aggregation framework. We show that it is possible to achieve state of the art in both next action and dense anticipation with simple techniques such as max-pooling and attention. To demonstrate the anticipation capabilities of our model, we conduct experiments on Breakfast, 50Salads, and EPIC-Kitchens datasets, where we achieve state-of-the-art results. With minimal modifications, our model can also be extended for video segmentation and action recognition."
Stochastic Fine-grained Labeling of Multi-state Sign Glosses for Continuous Sign Language Recognition
Authors: Zhe Niu; Brian Mak
Venue: ECCV 2020 (main)

Abstract: In this paper, we propose novel stochastic modeling of various components of a continuous sign language recognition (CSLR) system that is based on the transformer encoder and connectionist temporal classification (CTC). Most importantly, We model each sign gloss with multiple states, and the number of states is a categorical random variable that follows a learned probability distribution, providing stochastic fine-grained labels for training the CTC decoder. We further propose a stochastic frame dropping mechanism and a gradient stopping method to deal with the severe overfitting problem in training the transformer model with CTC loss. These two methods also help reduce the training computation, both in terms of time and space, significantly. We evaluated our model on popular CSLR datasets, and show its effectiveness compared to the state-of-the-art methods."
General 3D Room Layout from a Single View by Render-and-Compare
Authors: Sinisa Stekovic; Shreyas Hampali; Mahdi Rad; Sayan Deb Sarkar; Friedrich Fraundorfer; Vincent Lepetit
Venue: ECCV 2020 (main)

Abstract: We present a novel method to reconstruct the 3D layout of a room—walls, ﬂoors, ceilings—from a single perspective view in challenging conditions, by contrast with previous single-view methods restricted to cuboid-shaped layouts. This input view can consist of a color image only, but considering a depth map results in a more accurate reconstruction. Our approach is formalized as solving a constrained discrete optimization problem to ﬁnd the set of 3D polygons that constitute the layout. In order to deal with occlusions between components of the layout, which is a problem ignored by previous works, we introduce an analysis-by-synthesis method to iteratively reﬁne the 3D layout estimate. As no dataset was available to evaluate our method quantitatively, we created one together with several appropriate metrics. Our dataset consists of 293 images from ScanNet, which we annotated with precise 3D layouts. It oﬀers three times more samples than the popular NYUv2 303 benchmark, and a much larger variety of layouts."
Neural Dense Non-Rigid Structure from Motion with Latent Space Constraints
Authors: Vikramjit Sidhu; Edgar Tretschk; Vladislav Golyanik; Antonio Agudo; Christian Theobalt
Venue: ECCV 2020 (main)

Abstract: We introduce the first dense neural non-rigid structure from motion (N-NRSfM) approach, which can be trained end-to-end in an unsupervised manner from 2D point tracks. Compared to the competing methods, our combination of loss functions is fully-differentiable and can be readily integrated into deep-learning systems. We formulate the deformation model by an auto-decoder and impose subspace constraints on the recovered latent space function in a frequency domain. Thanks to the state recurrence cue, we classify the reconstructed non-rigid surfaces based on their similarity and recover the period of the input sequence. Our N-NRSfM approach achieves competitive accuracy on widely-used benchmark sequences and high visual quality on various real videos. Apart from being a standalone technique, our method enables multiple applications including shape compression, completion and interpolation, among others. Combined with an encoder trained directly on 2D images, we perform scenario-specific monocular 3D shape reconstruction at interactive frame rates. To facilitate the reproducibility of the results and boost the new research direction, we open-source our code and provide trained models for research purposes. "
Multimodal Memorability: Modeling Effects of Semantics and Decay on Video Memorability
Authors: Anelise Newman; Camilo Fosco; Vincent Casser; Allen Lee; Barry McNamara; Aude Oliva
Venue: ECCV 2020 (main)

Abstract: A key capability of an intelligent system is deciding when events from past experience must be remembered and when they can be forgotten. Towards this goal, we develop a predictive model of human visual event memory and how those memories decay over time. We introduce Memento10k, a new, dynamic video memorability dataset containing human annotations at different viewing delays. Based on our findings, we propose a new mathematical formulation of memorability decay, resulting in a model that is able to produce the first quantitative estimation of how a video decays in memory over time. In contrast with previous work, our model can predict the probability that a video will be remembered at an arbitrary delay. Importantly, our approach combines visual and semantic information (in the form of textual captions) to fully represent the meaning of events. Our experiments on two video memorability benchmarks, including Memento10k, show that our model significantly improves upon the best prior approach (by 12% on average). "
Yet Another Intermediate-Level Attack
Authors: Qizhang Li; Yiwen Guo; Hao Chen
Venue: ECCV 2020 (main)

Abstract: The transferability of adversarial examples across deep neural network (DNN) models is the crux of a spectrum of black-box attacks. In this paper, we propose a novel method to enhance the black-box transferability of baseline adversarial examples. By establishing a linear mapping of the intermediate-level discrepancies (between a set of adversarial inputs and their benign counterparts) for predicting their evoked adversarial loss, we manage to take full advantage of the optimization procedure of baseline attacks. We conduct extensive experiments to verify the effectiveness of our method on CIFAR-100 and ImageNet. Experimental results demonstrate that it outperforms previous state-of-the-arts by large margins. Our code will be made publicly available."
Topology-Change-Aware Volumetric Fusion for Dynamic Scene Reconstruction
Authors: Chao Li; Xiaohu Guo
Venue: ECCV 2020 (main)

Abstract: Topology change is a challenging problem for 4D reconstruction of dynamic scenes. In the classic volumetric fusion-based framework, a mesh is usually extracted from the TSDF volume as the canonical surface representation to help estimating deformation field. However, the surface and Embedded Deformation Graph (EDG) representations bring conflicts under topology changes since the surface mesh has fixed-connectivity but the deformation field can be discontinuous. In this paper, the classic framework is re-designed to enable 4D reconstruction of dynamic scene under topology changes, by introducing a novel structure of Non-manifold Volumetric Grid to the re-design of both TSDF and EDG, which allows connectivity updates by cell splitting and replication. Experiments show convincing reconstruction results for dynamic scenes of topology changes, as compared to the state-of-the-art methods."
Early Exit Or Not: Resource-Efficient Blind Quality Enhancement for Compressed Images
Authors: Qunliang Xing; Mai Xu; Tianyi Li; Zhenyu Guan
Venue: ECCV 2020 (main)

Abstract: Lossy image compression is pervasively conducted to save communication bandwidth, resulting in undesirable compression artifacts. Recently, extensive approaches have been proposed to reduce image compression artifacts at the decoder side; however, they require a series of architecture-identical models to process images with different quality, which are inefficient and resource-consuming. Besides, it is common in practice that compressed images are with unknown quality and it is intractable for existing approaches to select a suitable model for blind quality enhancement. In this paper, we propose a resource-efficient blind quality enhancement (RBQE) approach for compressed images. Specifically, our approach blindly and progressively enhances the quality of compressed images through a dynamic deep neural network (DNN), in which an early-exit strategy is embedded. Then, our approach can automatically decide to terminate or continue enhancement according to the assessed quality of enhanced images. Consequently, slight artifacts can be removed in a simpler and faster process, while the severe artifacts can be further removed in a more elaborate process. Extensive experiments demonstrate that our RBQE approach achieves state-of-the-art performance in terms of both blind quality enhancement and resource efficiency."
PatchNets: Patch-Based Generalizable Deep Implicit 3D Shape Representations
Authors: Edgar Tretschk; Ayush Tewari; Vladislav Golyanik; Michael Zollhöfer; Carsten Stoll; Christian Theobalt
Venue: ECCV 2020 (main)

Abstract: Implicit surface representation combined with deep learning has led to impressive models which can represent detailed shapes of objects. Implicit surface representations, such as signed-distance functions, allow to represent shapes of arbitrary topologies. Since a continous function is learned, the reconstructions can also be extracted at any arbitrary resolution. However, large shape datasets such as Shapenet are required to train such models. In this paper, we present a mid-level patch-based surface representation. At the level of patches, objects across different categories share similarities, which leads to more generalizable models. We introduce a novel method to learn these patches in a canonical space, such that they are as object-agnostic as possible. We show that patches trained on one category of objects from ShapeNet can also represent detailed shapes from any other category as well. In addition, our patches can be trained using much fewer shapes, compared to existing approaches. We show several applications of our new representation, including shape interpolation and partial point cloud completion. Due to explicit control over patch extrinsics, our representation is also more controllable compared to object-level representations, which we demonstrate by non-rigidly deforming shapes. "
How does Lipschitz Regularization Influence GAN Training?
Authors: Yipeng Qin; Niloy Mitra; Peter Wonka
Venue: ECCV 2020 (main)

Abstract: Despite the success of Lipschitz regularization in stabilizing GAN training, the exact reason of its effectiveness remains poorly understood. The direct effect of $K$-Lipschitz regularization is to restrict the $L2$-norm of the neural network gradient to be smaller than a threshold $K$ (e.g., $K=1$) such that $\| Lipschitz regularization ensures that all loss functions effectively work in the same way. Empirically, we verify our proposition on the MNIST, CIFAR10 and CelebA datasets."
Infrastructure-based Multi-Camera Calibration using Radial Projections
Authors: Yukai Lin; Viktor Larsson; Marcel Geppert; Zuzana Kukelova; Marc Pollefeys; Torsten Sattler
Venue: ECCV 2020 (main)

Abstract: Multi-camera systems are an important sensor platform for intelligent systems such as self-driving cars. Pattern-based calibration techniques can be used to calibrate the intrinsics of the cameras individually. However, extrinsic calibration of systems with little to no visual overlap between the cameras is a challenge. Given the camera intrinsics, infrastucture-based calibration techniques are able to estimate the extrinsics using 3D maps pre-built via SLAM or Structure-from-Motion. In this paper, we propose to fully calibrate a multi-camera system from scratch using an infrastructure-based approach. Assuming that the distortion is mainly radial, we introduce a two-stage approach. We first estimate the camera-rig extrinsics up to a single unknown translation component per camera. Next, we solve for both the intrinsic parameters and the missing translation components. Extensive experiments on multiple indoor and outdoor scenes with multiple multi-camera systems show that our calibration method achieves high accuracy and robustness. In particular, our approach is more robust than the naive approach of first estimating intrinsic parameters and pose per camera before refining the extrinsic parameters of the system. The implementation is available at https://github.com/youkely/InfrasCal."
MotionSqueeze: Neural Motion Feature Learning for Video Understanding
Authors: Heeseung Kwon; Manjin Kim; Suha Kwak; Minsu Cho
Venue: ECCV 2020 (main)

Abstract: Motion plays a crucial role in understanding videos and most state-of-the-art neural models for video classification incorporate motion information typically using optical flows extracted by a separate off-the-shelf method. As the frame-by-frame optical flows require heavy computation, incorporating motion information has remained a major computational bottleneck for video understanding. In this work, we replace external and heavy computation of optical flows with internal and light-weight learning of motion features. We propose a trainable neural module, dubbed MotionSqueeze, for effective motion feature extraction. Inserted in the middle of any neural network, it learns to establish correspondences across frames and convert them into motion features, which are readily fed to the next downstream layer for better prediction. We demonstrate that the proposed method provides a significant gain on four standard benchmarks for action recognition with only a small amount of additional cost, outperforming the state of the art on Something-Something-V1&V2 datasets."