pwc-archive/papers-with-abstracts huggingface dataset:
Morshed: Guiding Behavioral Decision-Makers towards Better Security Investment in Interdependent Systems
Authors: Mustafa Abdallah, Daniel Woods, Parinaz Naghizadeh, Issa Khalil, Timothy Cason, Shreyas Sundaram, Saurabh Bagchi
Date: 2020-11-12 00:00:00
Tasks: Decision Making

We model the behavioral biases of human decision-making in securing interdependent systems and show that such behavioral decision-making leads to a suboptimal pattern of resource allocation compared to non-behavioral (rational) decision-making. We provide empirical evidence for the existence of such behavioral bias model through a controlled subject study with 145 participants. We then propose three learning techniques for enhancing decision-making in multi-round setups. We illustrate the benefits of our decision-making model through multiple interdependent real-world systems and quantify the level of gain compared to the case in which the defenders are behavioral. We also show the benefit of our learning techniques against different attack models. We identify the effects of different system parameters on the degree of suboptimality of security outcomes due to behavioral decision-making.
Robotic self-representation improves manipulation skills and transfer learning
Authors: Phuong D. H. Nguyen, Manfred Eppe, Stefan Wermter
Date: 2020-11-13 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning (RL), Transfer Learning

Cognitive science suggests that the self-representation is critical for learning and problem-solving. However, there is a lack of computational methods that relate this claim to cognitively plausible robots and reinforcement learning. In this paper, we bridge this gap by developing a model that learns bidirectional action-effect associations to encode the representations of body schema and the peripersonal space from multisensory information, which is named multimodal BidAL. Through three different robotic experiments, we demonstrate that this approach significantly stabilizes the learning-based problem-solving under noisy conditions and that it improves transfer learning of robotic manipulation skills.
Monitoring and Diagnosability of Perception Systems
Authors: Pasquale Antonante, David I. Spivak, Luca Carlone
Date: 2020-11-11 00:00:00
Tasks: CPU, Diagnostic, Fault Detection

Perception is a critical component of high-integrity applications of robotics and autonomous systems, such as self-driving vehicles. In these applications, failure of perception systems may put human life at risk, and a broad adoption of these technologies requires the development of methodologies to guarantee and monitor safe operation. Despite the paramount importance of perception systems, currently there is no formal approach for system-level monitoring. In this work, we propose a mathematical model for runtime monitoring and fault detection and identification in perception systems. Towards this goal, we draw connections with the literature on diagnosability in multiprocessor systems, and generalize it to account for modules with heterogeneous outputs that interact over time. The resulting temporal diagnostic graphs (i) provide a framework to reason over the consistency of perception outputs -- across modules and over time -- thus enabling fault detection, (ii) allow us to establish formal guarantees on the maximum number of faults that can be uniquely identified in a given perception system, and (iii) enable the design of efficient algorithms for fault identification. We demonstrate our monitoring system, dubbed PerSyS, in realistic simulations using the LGSVL self-driving simulator and the Apollo Auto autonomy software stack, and show that PerSyS is able to detect failures in challenging scenarios (including scenarios that have caused self-driving car accidents in recent years), and is able to correctly identify faults while entailing a minimal computation overhead (< 5 ms on a single-core CPU).
Enabling the Sense of Self in a Dual-Arm Robot
Authors: Ali AlQallaf, Gerardo Aragon-Camarasa
Date: 2020-11-13 00:00:00

While humans are aware of their body and capabilities, robots are not. To address this, we present in this paper a neural network architecture that enables a dual-arm robot to get a sense of itself in an environment. Our approach is inspired by human self-awareness developmental levels and serves as the underlying building block for a robot to achieve awareness of itself while carrying out tasks in an environment. We assume that a robot has to know itself before interacting with the environment in order to be able to support different robotic tasks. Hence, we implemented a neural network architecture to enable a robot to differentiate its limbs from the environment using visual and proprioception sensory inputs. We demonstrate experimentally that a robot can distinguish itself with an accuracy of 88.7% on average in cluttered environmental settings and under confounding input signals.
Migration-Related Semantic Concepts for the Retrieval of Relevant Video Content
Authors: Elejalde Erick, Galanopoulos Damianos, Niederee Claudia, Mezaris Vasileios
Date: 2020-11-13 00:00:00
Tasks: Retrieval

Migration, and especially irregular migration, is a critical issue for border agencies and society in general. Migration-related situations and decisions are influenced by various factors, including the perceptions about migration routes and target countries. An improved understanding of such factors can be achieved by systematic automated analyses of media and social media channels, and the videos and images published in them. However, the multifaceted nature of migration and the variety of ways migration-related aspects are expressed in images and videos make the finding and automated analysis of migration-related multimedia content a challenging task. We propose a novel approach that effectively bridges the gap between a substantiated domain understanding - encapsulated into a set of Migration-related semantic concepts - and the expression of such concepts in a video, by introducing an advanced video analysis and retrieval method for this purpose.
Trajectory Prediction in Autonomous Driving with a Lane Heading Auxiliary Loss
Authors: Ross Greer, Nachiket Deo, Mohan Trivedi
Date: 2020-11-12 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles, Prediction, Trajectory Prediction

Predicting a vehicle's trajectory is an essential ability for autonomous vehicles navigating through complex urban traffic scenes. Bird's-eye-view roadmap information provides valuable information for making trajectory predictions, and while state-of-the-art models extract this information via image convolution, auxiliary loss functions can augment patterns inferred from deep learning by further encoding common knowledge of social and legal driving behaviors. Since human driving behavior is inherently multimodal, models which allow for multimodal output tend to outperform single-prediction models on standard metrics. We propose a loss function which enhances such models by enforcing expected driving rules on all predicted modes. Our contribution to trajectory prediction is twofold; we propose a new metric which addresses failure cases of the off-road rate metric by penalizing trajectories that oppose the ascribed heading (flow direction) of a driving lane, and we show this metric to be differentiable and therefore suitable as an auxiliary loss function. We then use this auxiliary loss to extend the the standard multiple trajectory prediction (MTP) and MultiPath models, achieving improved results on the nuScenes prediction benchmark by predicting trajectories which better conform to the lane-following rules of the road.
Filter Pre-Pruning for Improved Fine-tuning of Quantized Deep Neural Networks
Authors: Jun Nishikawa, Ryoji Ikegaya
Date: 2020-11-13 00:00:00
Tasks: Quantization

Deep Neural Networks(DNNs) have many parameters and activation data, and these both are expensive to implement. One method to reduce the size of the DNN is to quantize the pre-trained model by using a low-bit expression for weights and activations, using fine-tuning to recover the drop in accuracy. However, it is generally difficult to train neural networks which use low-bit expressions. One reason is that the weights in the middle layer of the DNN have a wide dynamic range and so when quantizing the wide dynamic range into a few bits, the step size becomes large, which leads to a large quantization error and finally a large degradation in accuracy. To solve this problem, this paper makes the following three contributions without using any additional learning parameters and hyper-parameters. First, we analyze how batch normalization, which causes the aforementioned problem, disturbs the fine-tuning of the quantized DNN. Second, based on these results, we propose a new pruning method called Pruning for Quantization (PfQ) which removes the filters that disturb the fine-tuning of the DNN while not affecting the inferred result as far as possible. Third, we propose a workflow of fine-tuning for quantized DNNs using the proposed pruning method(PfQ). Experiments using well-known models and datasets confirmed that the proposed method achieves higher performance with a similar model size than conventional quantization methods including fine-tuning.
Lightweight Single-Image Super-Resolution Network with Attentive Auxiliary Feature Learning
Authors: Xuehui Wang, Qing Wang, Yuzhi Zhao, Junchi Yan, Lei Fan, Long Chen
Date: 2020-11-13 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

Despite convolutional network-based methods have boosted the performance of single image super-resolution (SISR), the huge computation costs restrict their practical applicability. In this paper, we develop a computation efficient yet accurate network based on the proposed attentive auxiliary features (A$^2$F) for SISR. Firstly, to explore the features from the bottom layers, the auxiliary feature from all the previous layers are projected into a common space. Then, to better utilize these projected auxiliary features and filter the redundant information, the channel attention is employed to select the most important common feature based on current layer feature. We incorporate these two modules into a block and implement it with a lightweight network. Experimental results on large-scale dataset demonstrate the effectiveness of the proposed model against the state-of-the-art (SOTA) SR methods. Notably, when parameters are less than 320k, A$^2$F outperforms SOTA methods for all scales, which proves its ability to better utilize the auxiliary features. Codes are available at https://github.com/wxxxxxxh/A2F-SR.
Adaptive Future Frame Prediction with Ensemble Network
Authors: Wonjik Kim, Masayuki Tanaka, Masatoshi Okutomi, Yoko SASAKI
Date: 2020-11-13 00:00:00
Tasks: Prediction

Future frame prediction in videos is a challenging problem because videos include complicated movements and large appearance changes. Learning-based future frame prediction approaches have been proposed in kinds of literature. A common limitation of the existing learning-based approaches is a mismatch of training data and test data. In the future frame prediction task, we can obtain the ground truth data by just waiting for a few frames. It means we can update the prediction model online in the test phase. Then, we propose an adaptive update framework for the future frame prediction task. The proposed adaptive updating framework consists of a pre-trained prediction network, a continuous-updating prediction network, and a weight estimation network. We also show that our pre-trained prediction model achieves comparable performance to the existing state-of-the-art approaches. We demonstrate that our approach outperforms existing methods especially for dynamically changing scenes.
SHAD3S: A model to Sketch, Shade and Shadow
Authors: Raghav B. Venkataramaiyer, Abhishek Joshi, Saisha Narang, Vinay P. Namboodiri
Date: 2020-11-13 00:00:00
Tasks: Generative Adversarial Network

Hatching is a common method used by artists to accentuate the third dimension of a sketch, and to illuminate the scene. Our system SHAD3S attempts to compete with a human at hatching generic three-dimensional (3D) shapes, and also tries to assist her in a form exploration exercise. The novelty of our approach lies in the fact that we make no assumptions about the input other than that it represents a 3D shape, and yet, given a contextual information of illumination and texture, we synthesise an accurate hatch pattern over the sketch, without access to 3D or pseudo 3D. In the process, we contribute towards a) a cheap yet effective method to synthesise a sufficiently large high fidelity dataset, pertinent to task; b) creating a pipeline with conditional generative adversarial network (CGAN); and c) creating an interactive utility with GIMP, that is a tool for artists to engage with automated hatching or a form-exploration exercise. User evaluation of the tool suggests that the model performance does generalise satisfactorily over diverse input, both in terms of style as well as shape. A simple comparison of inception scores suggest that the generated distribution is as diverse as the ground truth.
Discriminative Feature Representation with Spatio-temporal Cues for Vehicle Re-identification
Authors: J. Tu, C. Chen, X. Huang, J. He, X. Guan
Date: 2020-11-13 00:00:00
Tasks: Management, Vehicle Re-Identification

Vehicle re-identification (re-ID) aims to discover and match the target vehicles from a gallery image set taken by different cameras on a wide range of road networks. It is crucial for lots of applications such as security surveillance and traffic management. The remarkably similar appearances of distinct vehicles and the significant changes of viewpoints and illumination conditions take grand challenges to vehicle re-ID. Conventional solutions focus on designing global visual appearances without sufficient consideration of vehicles' spatiotamporal relationships in different images. In this paper, we propose a novel discriminative feature representation with spatiotemporal clues (DFR-ST) for vehicle re-ID. It is capable of building robust features in the embedding space by involving appearance and spatio-temporal information. Based on this multi-modal information, the proposed DFR-ST constructs an appearance model for a multi-grained visual representation by a two-stream architecture and a spatio-temporal metric to provide complementary information. Experimental results on two public datasets demonstrate DFR-ST outperforms the state-of-the-art methods, which validate the effectiveness of the proposed method.
Image Animation with Perturbed Masks
Authors: Yoav Shalev, Lior Wolf
Date: 2020-11-13 00:00:00
Proceeding: CVPR 2022 1
Tasks: Image Animation

We present a novel approach for image-animation of a source image by a driving video, both depicting the same type of object. We do not assume the existence of pose models and our method is able to animate arbitrary objects without the knowledge of the object's structure. Furthermore, both, the driving video and the source image are only seen during test-time. Our method is based on a shared mask generator, which separates the foreground object from its background, and captures the object's general pose and shape. To control the source of the identity of the output frame, we employ perturbations to interrupt the unwanted identity information on the driver's mask. A mask-refinement module then replaces the identity of the driver with the identity of the source. Conditioned on the source image, the transformed mask is then decoded by a multi-scale generator that renders a realistic image, in which the content of the source frame is animated by the pose in the driving video. Due to the lack of fully supervised data, we train on the task of reconstructing frames from the same video the source image is taken from. Our method is shown to greatly outperform the state-of-the-art methods on multiple benchmarks. Our code and samples are available at https://github.com/itsyoavshalev/Image-Animation-with-Perturbed-Masks.
Transformer-Encoder Detector Module: Using Context to Improve Robustness to Adversarial Attacks on Object Detection
Authors: Faisal Alamri, Sinan Kalkan, Nicolas Pugeault
Date: 2020-11-13 00:00:00
Tasks: Object, object-detection, Object Detection, Object Recognition

Deep neural network approaches have demonstrated high performance in object recognition (CNN) and detection (Faster-RCNN) tasks, but experiments have shown that such architectures are vulnerable to adversarial attacks (FFF, UAP): low amplitude perturbations, barely perceptible by the human eye, can lead to a drastic reduction in labeling performance. This article proposes a new context module, called \textit{Transformer-Encoder Detector Module}, that can be applied to an object detector to (i) improve the labeling of object instances; and (ii) improve the detector's robustness to adversarial attacks. The proposed model achieves higher mAP, F1 scores and AUC average score of up to 13\% compared to the baseline Faster-RCNN detector, and an mAP score 8 points higher on images subjected to FFF or UAP attacks due to the inclusion of both contextual and visual features extracted from scene and encoded into the model. The result demonstrates that a simple ad-hoc context module can improve the reliability of object detectors significantly.
Using Graph Neural Networks to Reconstruct Ancient Documents
Authors: Cecilia Ostertag, Marie Beurton-Aimar
Date: 2020-11-13 00:00:00
Tasks: Graph Neural Network

In recent years, machine learning and deep learning approaches such as artificial neural networks have gained in popularity for the resolution of automatic puzzle resolution problems. Indeed, these methods are able to extract high-level representations from images, and then can be trained to separate matching image pieces from non-matching ones. These applications have many similarities to the problem of ancient document reconstruction from partially recovered fragments. In this work we present a solution based on a Graph Neural Network, using pairwise patch information to assign labels to edges representing the spatial relationships between pairs. This network classifies the relationship between a source and a target patch as being one of Up, Down, Left, Right or None. By doing so for all edges, our model outputs a new graph representing a reconstruction proposal. Finally, we show that our model is not only able to provide correct classifications at the edge-level, but also to generate partial or full reconstruction graphs from a set of patches.
Disassemblable Fieldwork CT Scanner Using a 3D-printed Calibration Phantom
Authors: Florian Schiffers, Thomas Bochynek, Andre Aichert, Tobias Würfl, Michael Rubenstein, Oliver Cossairt
Date: 2020-11-12 00:00:00
Tasks: Computed Tomography (CT), Tomographic Reconstructions

The use of computed tomography (CT) imaging has become of increasing interest to academic areas outside of the field of medical imaging and industrial inspection, e.g., to biology and cultural heritage research. The pecularities of these fields, however, sometimes require that objects need to be imaged on-site, e.g., in field-work conditions or in museum collections. Under these circumstances, it is often not possible to use a commercial device and a custom solution is the only viable option. In order to achieve high image quality under adverse conditions, reliable calibration and trajectory reproduction are usually key requirements for any custom CT scanning system. Here, we introduce the construction of a low-cost disassemblable CT scanner that allows calibration even when trajectory reproduction is not possible due to the limitations imposed by the project conditions. Using 3D-printed in-image calibration phantoms, we compute a projection matrix directly from each captured X-ray projection. We describe our method in detail and show successful tomographic reconstructions of several specimen as proof of concept.
FastTrack: an open-source software for tracking varying numbers of deformable objects
Authors: Benjamin Gallois, Raphaël Candelier
Date: 2020-11-13 00:00:00
Tasks: Cell Tracking

Analyzing the dynamical properties of mobile objects requires to extract trajectories from recordings, which is often done by tracking movies. We compiled a database of two-dimensional movies for very different biological and physical systems spanning a wide range of length scales and developed a general-purpose, optimized, open-source, cross-platform, easy to install and use, self-updating software called FastTrack. It can handle a changing number of deformable objects in a region of interest, and is particularly suitable for animal and cell tracking in two-dimensions. Furthermore, we introduce the probability of incursions as a new measure of a movie's trackability that doesn't require the knowledge of ground truth trajectories, since it is resilient to small amounts of errors and can be computed on the basis of an ad hoc tracking. We also leveraged the versatility and speed of FastTrack to implement an iterative algorithm determining a set of nearly-optimized tracking parameters -- yet further reducing the amount of human intervention -- and demonstrate that FastTrack can be used to explore the space of tracking parameters to optimize the number of swaps for a batch of similar movies. A benchmark shows that FastTrack is orders of magnitude faster than state-of-the-art tracking algorithms, with a comparable tracking accuracy. The source code is available under the GNU GPLv3 at https://github.com/FastTrackOrg/FastTrack and pre-compiled binaries for Windows, Mac and Linux are available at http://www.fasttrack.sh.
Unified Multi-Modal Landmark Tracking for Tightly Coupled Lidar-Visual-Inertial Odometry
Authors: David Wisth, Marco Camurri, Sandipan Das, Maurice Fallon
Date: 2020-11-13 00:00:00
Tasks: CPU, Landmark Tracking

We present an efficient multi-sensor odometry system for mobile platforms that jointly optimizes visual, lidar, and inertial information within a single integrated factor graph. This runs in real-time at full framerate using fixed lag smoothing. To perform such tight integration, a new method to extract 3D line and planar primitives from lidar point clouds is presented. This approach overcomes the suboptimality of typical frame-to-frame tracking methods by treating the primitives as landmarks and tracking them over multiple scans. True integration of lidar features with standard visual features and IMU is made possible using a subtle passive synchronization of lidar and camera frames. The lightweight formulation of the 3D features allows for real-time execution on a single CPU. Our proposed system has been tested on a variety of platforms and scenarios, including underground exploration with a legged robot and outdoor scanning with a dynamically moving handheld device, for a total duration of 96 min and 2.4 km traveled distance. In these test sequences, using only one exteroceptive sensor leads to failure due to either underconstrained geometry (affecting lidar) or textureless areas caused by aggressive lighting changes (affecting vision). In these conditions, our factor graph naturally uses the best information available from each sensor modality without any hard switches.