pwc-archive/papers-with-abstracts huggingface dataset:
Unveiling the Potential of Structure Preserving for Weakly Supervised Object Localization
Authors: Xingjia Pan, Yingguo Gao, Zhiwen Lin, Fan Tang, WeiMing Dong, Haolei Yuan, Feiyue Huang, Changsheng Xu
Date: 2021-03-08 00:00:00
Proceeding: CVPR 2021 1
Tasks: Classification, General Classification, Object, Object Localization, Single Particle Analysis, Weakly-Supervised Object Localization

Weakly supervised object localization(WSOL) remains an open problem given the deficiency of finding object extent information using a classification network. Although prior works struggled to localize objects through various spatial regularization strategies, we argue that how to extract object structural information from the trained classification network is neglected. In this paper, we propose a two-stage approach, termed structure-preserving activation (SPA), toward fully leveraging the structure information incorporated in convolutional features for WSOL. First, a restricted activation module (RAM) is designed to alleviate the structure-missing issue caused by the classification network on the basis of the observation that the unbounded classification map and global average pooling layer drive the network to focus only on object parts. Second, we designed a post-process approach, termed self-correlation map generating (SCG) module to obtain structure-preserving localization maps on the basis of the activation maps acquired from the first stage. Specifically, we utilize the high-order self-correlation (HSC) to extract the inherent structural information retained in the learned model and then aggregate HSC of multiple points for precise object localization. Extensive experiments on two publicly available benchmarks including CUB-200-2011 and ILSVRC show that the proposed SPA achieves substantial and consistent performance gains compared with baseline approaches.Code and models are available at https://github.com/Panxjia/SPA_CVPR2021
"Sharks are not the threat humans are": Argument Component Segmentation in School Student Essays
Authors: Tariq Alhindi, Debanjan Ghosh
Date: 2021-03-08 00:00:00
Tasks: Argument Mining, Classification, General Classification, Multi-Task Learning, Sentence

Argument mining is often addressed by a pipeline method where segmentation of text into argumentative units is conducted first and proceeded by an argument component identification task. In this research, we apply a token-level classification to identify claim and premise tokens from a new corpus of argumentative essays written by middle school students. To this end, we compare a variety of state-of-the-art models such as discrete features and deep learning architectures (e.g., BiLSTM networks and BERT-based architectures) to identify the argument components. We demonstrate that a BERT-based multi-task learning architecture (i.e., token and sentence level classification) adaptively pretrained on a relevant unlabeled dataset obtains the best results
Loosely Synchronized Search for Multi-agent Path Finding with Asynchronous Actions
Authors: Zhongqiang Ren, Sivakumar Rathinam, Howie Choset
Date: 2021-03-08 00:00:00
Tasks: Computational Efficiency, Multi-Agent Path Finding

Multi-agent path finding (MAPF) determines an ensemble of collision-free paths for multiple agents between their respective start and goal locations. Among the available MAPF planners for workspace modeled as a graph, A*-based approaches have been widely investigated due to their guarantees on completeness and solution optimality, and have demonstrated their efficiency in many scenarios. However, almost all of these A*-based methods assume that each agent executes an action concurrently in that all agents start and stop together. This article presents a natural generalization of MAPF with asynchronous actions (MAPF-AA) where agents do not necessarily start and stop concurrently. The main contribution of the work is a proposed approach called Loosely Synchronized Search (LSS) that extends A*-based MAPF planners to handle asynchronous actions. We show LSS is complete and finds an optimal solution if one exists. We also combine LSS with other existing MAPF methods that aims to trade-off optimality for computational efficiency. Numerical results are presented to corroborate the performance of LSS and the applicability of the proposed method is verified in the Robotarium, a remotely accessible swarm robotics research platform.
Nondeterminism and Instability in Neural Network Optimization
Authors: Cecilia Summers, Michael J. Dinneen
Date: 2021-03-08 00:00:00
Tasks: Diversity

Nondeterminism in neural network optimization produces uncertainty in performance, making small improvements difficult to discern from run-to-run variability. While uncertainty can be reduced by training multiple model copies, doing so is time-consuming, costly, and harms reproducibility. In this work, we establish an experimental protocol for understanding the effect of optimization nondeterminism on model diversity, allowing us to isolate the effects of a variety of sources of nondeterminism. Surprisingly, we find that all sources of nondeterminism have similar effects on measures of model diversity. To explain this intriguing fact, we identify the instability of model training, taken as an end-to-end procedure, as the key determinant. We show that even one-bit changes in initial parameters result in models converging to vastly different values. Last, we propose two approaches for reducing the effects of instability on run-to-run variability.
Improving Global Adversarial Robustness Generalization With Adversarially Trained GAN
Authors: DeSheng Wang, Weidong Jin, Yunpu Wu, Aamir Khan
Date: 2021-03-08 00:00:00
Tasks: Adversarial Defense, Adversarial Robustness, Data Augmentation, image-classification, Image Classification

Convolutional neural networks (CNNs) have achieved beyond human-level accuracy in the image classification task and are widely deployed in real-world environments. However, CNNs show vulnerability to adversarial perturbations that are well-designed noises aiming to mislead the classification models. In order to defend against the adversarial perturbations, adversarially trained GAN (ATGAN) is proposed to improve the adversarial robustness generalization of the state-of-the-art CNNs trained by adversarial training. ATGAN incorporates adversarial training into standard GAN training procedure to remove obfuscated gradients which can lead to a false sense in defending against the adversarial perturbations and are commonly observed in existing GANs-based adversarial defense methods. Moreover, ATGAN adopts the image-to-image generator as data augmentation to increase the sample complexity needed for adversarial robustness generalization in adversarial training. Experimental results in MNIST SVHN and CIFAR-10 datasets show that the proposed method doesn't rely on obfuscated gradients and achieves better global adversarial robustness generalization performance than the adversarially trained state-of-the-art CNNs.
An Energy-Saving Snake Locomotion Gait Policy Obtained Using Deep Reinforcement Learning
Authors: Yilang Liu, Amir Barati Farimani
Date: 2021-03-08 00:00:00
Tasks: Deep Reinforcement Learning, Navigate, reinforcement-learning, Reinforcement Learning (RL)

Snake robots, comprised of sequentially connected joint actuators, have recently gained increasing attention in the industrial field, like life detection in narrow space. Such robots can navigate through the complex environment via the cooperation of multiple motors located on the backbone. However, controlling the robots in an unknown environment is challenging, and conventional control strategies can be energy inefficient or even fail to navigate to the destination. In this work, a snake locomotion gait policy is developed via deep reinforcement learning (DRL) for energy-efficient control. We apply proximal policy optimization (PPO) to each joint motor parameterized by angular velocity and the DRL agent learns the standard serpenoid curve at each timestep. The robot simulator and task environment are built upon PyBullet. Comparing to conventional control strategies, the snake robots controlled by the trained PPO agent can achieve faster movement and more energy-efficient locomotion gait. This work demonstrates that DRL provides an energy-efficient solution for robot control.
Predictive Visual Tracking: A New Benchmark and Baseline Approach
Authors: Bowen Li, Yiming Li, Junjie Ye, Changhong Fu, Hang Zhao
Date: 2021-03-08 00:00:00
Tasks: Visual Tracking

As a crucial robotic perception capability, visual tracking has been intensively studied recently. In the real-world scenarios, the onboard processing time of the image streams inevitably leads to a discrepancy between the tracking results and the real-world states. However, existing visual tracking benchmarks commonly run the trackers offline and ignore such latency in the evaluation. In this work, we aim to deal with a more realistic problem of latency-aware tracking. The state-of-the-art trackers are evaluated in the aerial scenarios with new metrics jointly assessing the tracking accuracy and efficiency. Moreover, a new predictive visual tracking baseline is developed to compensate for the latency stemming from the onboard computation. Our latency-aware benchmark can provide a more realistic evaluation of the trackers for the robotic applications. Besides, exhaustive experiments have proven the effectiveness of the proposed predictive visual tracking baseline approach.
OPANAS: One-Shot Path Aggregation Network Architecture Search for Object Detection
Authors: TingTing Liang, Yongtao Wang, Zhi Tang, Guosheng Hu, Haibin Ling
Date: 2021-03-08 00:00:00
Proceeding: CVPR 2021 1
Tasks: GPU, Neural Architecture Search, object-detection, Object Detection

Recently, neural architecture search (NAS) has been exploited to design feature pyramid networks (FPNs) and achieved promising results for visual object detection. Encouraged by the success, we propose a novel One-Shot Path Aggregation Network Architecture Search (OPANAS) algorithm, which significantly improves both searching efficiency and detection accuracy. Specifically, we first introduce six heterogeneous information paths to build our search space, namely top-down, bottom-up, fusing-splitting, scale-equalizing, skip-connect and none. Second, we propose a novel search space of FPNs, in which each FPN candidate is represented by a densely-connected directed acyclic graph (each node is a feature pyramid and each edge is one of the six heterogeneous information paths). Third, we propose an efficient one-shot search method to find the optimal path aggregation architecture, that is, we first train a super-net and then find the optimal candidate with an evolutionary algorithm. Experimental results demonstrate the efficacy of the proposed OPANAS for object detection: (1) OPANAS is more efficient than state-of-the-art methods (e.g., NAS-FPN and Auto-FPN), at significantly smaller searching cost (e.g., only 4 GPU days on MS-COCO); (2) the optimal architecture found by OPANAS significantly improves main-stream detectors including RetinaNet, Faster R-CNN and Cascade R-CNN, by 2.3-3.2 % mAP comparing to their FPN counterparts; and (3) a new state-of-the-art accuracy-speed trade-off (52.2 % mAP at 7.6 FPS) at smaller training costs than comparable state-of-the-arts. Code will be released at https://github.com/VDIGPKU/OPANAS.
Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges
Authors: Yoshitomo Matsubara, Marco Levorato, Francesco Restuccia
Date: 2021-03-08 00:00:00
Tasks: Autonomous Vehicles, image-classification, Image Classification, Object Detection, Semantic Segmentation, speech-recognition, Speech Recognition

Mobile devices such as smartphones and autonomous vehicles increasingly rely on deep neural networks (DNNs) to execute complex inference tasks such as image classification and speech recognition, among others. However, continuously executing the entire DNN on mobile devices can quickly deplete their battery. Although task offloading to cloud/edge servers may decrease the mobile device's computational burden, erratic patterns in channel quality, network, and edge server load can lead to a significant delay in task execution. Recently, approaches based on split computing (SC) have been proposed, where the DNN is split into a head and a tail model, executed respectively on the mobile device and on the edge server. Ultimately, this may reduce bandwidth usage as well as energy consumption. Another approach, called early exiting (EE), trains models to embed multiple "exits" earlier in the architecture, each providing increasingly higher target accuracy. Therefore, the trade-off between accuracy and delay can be tuned according to the current conditions or application demands. In this paper, we provide a comprehensive survey of the state of the art in SC and EE strategies by presenting a comparison of the most relevant approaches. We conclude the paper by providing a set of compelling research challenges.
End-to-End Human Object Interaction Detection with HOI Transformer
Authors: Cheng Zou, Bohan Wang, Yue Hu, Junqi Liu, Qian Wu, Yu Zhao, Boxun Li, Chenguang Zhang, Chi Zhang, Yichen Wei, Jian Sun
Date: 2021-03-08 00:00:00
Proceeding: CVPR 2021 1
Tasks: Human-Object Interaction Detection, object-detection, Object Detection

We propose HOI Transformer to tackle human object interaction (HOI) detection in an end-to-end manner. Current approaches either decouple HOI task into separated stages of object detection and interaction classification or introduce surrogate interaction problem. In contrast, our method, named HOI Transformer, streamlines the HOI pipeline by eliminating the need for many hand-designed components. HOI Transformer reasons about the relations of objects and humans from global image context and directly predicts HOI instances in parallel. A quintuple matching loss is introduced to force HOI predictions in a unified way. Our method is conceptually much simpler and demonstrates improved accuracy. Without bells and whistles, HOI Transformer achieves $26.61\% $ $ AP $ on HICO-DET and $52.9\%$ $AP_{role}$ on V-COCO, surpassing previous methods with the advantage of being much simpler. We hope our approach will serve as a simple and effective alternative for HOI tasks. Code is available at https://github.com/bbepoch/HoiTransformer .
Let's be friends! A rapport-building 3D embodied conversational agent for the Human Support Robot
Authors: Katarzyna Pasternak, Zishi Wu, Ubbo Visser, Christine Lisetti
Date: 2021-03-08 00:00:00

Partial subtle mirroring of nonverbal behaviors during conversations (also known as mimicking or parallel empathy), is essential for rapport building, which in turn is essential for optimal human-human communication outcomes. Mirroring has been studied in interactions between robots and humans, and in interactions between Embodied Conversational Agents (ECAs) and humans. However, very few studies examine interactions between humans and ECAs that are integrated with robots, and none of them examine the effect of mirroring nonverbal behaviors in such interactions. Our research question is whether integrating an ECA able to mirror its interlocutor's facial expressions and head movements (continuously or intermittently) with a human-service robot will improve the user's experience with the support robot that is able to perform useful mobile manipulative tasks (e.g. at home). Our contribution is the complex integration of an expressive ECA, able to track its interlocutor's face, and to mirror his/her facial expressions and head movements in real time, integrated with a human support robot such that the robot and the agent are fully aware of each others', and of the users', nonverbals cues. We also describe a pilot study we conducted towards answering our research question, which shows promising results for our forthcoming larger user study.
Sparsification for Fast Optimal Multi-Robot Path Planning in Lazy Compilation Schemes
Authors: Pavel Surynek
Date: 2021-03-08 00:00:00
Tasks: Navigate

Path planning for multiple robots (MRPP) represents a task of finding non-colliding paths for robots through which they can navigate from their initial positions to specified goal positions. The problem is usually modeled using undirected graphs where robots move between vertices across edges. Contemporary optimal solving algorithms include dedicated search-based methods, that solve the problem directly, and compilation-based algorithms that reduce MRPP to a different formalism for which an efficient solver exists, such as constraint programming (CP), mixed integer programming (MIP), or Boolean satisfiability (SAT). In this paper, we enhance existing SAT-based algorithm for MRPP via spartification of the set of candidate paths for each robot from which target Boolean encoding is derived. Suggested sparsification of the set of paths led to smaller target Boolean formulae that can be constructed and solved faster while optimality guarantees of the approach have been kept.
Fully Convolutional Geometric Features for Category-level Object Alignment
Authors: Qiaojun Feng, Nikolay Atanasov
Date: 2021-03-08 00:00:00
Tasks: Metric Learning, Object

This paper focuses on pose registration of different object instances from the same category. This is required in online object mapping because object instances detected at test time usually differ from the training instances. Our approach transforms instances of the same category to a normalized canonical coordinate frame and uses metric learning to train fully convolutional geometric features. The resulting model is able to generate pairs of matching points between the instances, allowing category-level registration. Evaluation on both synthetic and real-world data shows that our method provides robust features, leading to accurate alignment of instances with different shapes.
Localization and Mapping using Instance-specific Mesh Models
Authors: Qiaojun Feng, Yue Meng, Mo Shan, Nikolay Atanasov
Date: 2021-03-08 00:00:00
Tasks: Object

This paper focuses on building semantic maps, containing object poses and shapes, using a monocular camera. This is an important problem because robots need rich understanding of geometry and context if they are to shape the future of transportation, construction, and agriculture. Our contribution is an instance-specific mesh model of object shape that can be optimized online based on semantic information extracted from camera images. Multi-view constraints on the object shape are obtained by detecting objects and extracting category-specific keypoints and segmentation masks. We show that the errors between projections of the mesh model and the observed keypoints and masks can be differentiated in order to obtain accurate instance-specific object shapes. We evaluate the performance of the proposed approach in simulation and on the KITTI dataset by building maps of car poses and shapes.
Adaptive-Control-Oriented Meta-Learning for Nonlinear Systems
Authors: Spencer M. Richards, Navid Azizan, Jean-Jacques Slotine, Marco Pavone
Date: 2021-03-07 00:00:00
Tasks: Meta-Learning, regression

Real-time adaptation is imperative to the control of robots operating in complex, dynamic environments. Adaptive control laws can endow even nonlinear systems with good trajectory tracking performance, provided that any uncertain dynamics terms are linearly parameterizable with known nonlinear features. However, it is often difficult to specify such features a priori, such as for aerodynamic disturbances on rotorcraft or interaction forces between a manipulator arm and various objects. In this paper, we turn to data-driven modeling with neural networks to learn, offline from past data, an adaptive controller with an internal parametric model of these nonlinear features. Our key insight is that we can better prepare the controller for deployment with control-oriented meta-learning of features in closed-loop simulation, rather than regression-oriented meta-learning of features to fit input-output data. Specifically, we meta-learn the adaptive controller with closed-loop tracking simulation as the base-learner and the average tracking error as the meta-objective. With a nonlinear planar rotorcraft subject to wind, we demonstrate that our adaptive controller outperforms other controllers trained with regression-oriented meta-learning when deployed in closed-loop for trajectory tracking control.
Convolutional Graph-Tensor Net for Graph Data Completion
Authors: Xiao-Yang Liu, Ming Zhu
Date: 2021-03-07 00:00:00
Tasks: Recommendation Systems

Graph data completion is a fundamentally important issue as data generally has a graph structure, e.g., social networks, recommendation systems, and the Internet of Things. We consider a graph where each node has a data matrix, represented as a \textit{graph-tensor} by stacking the data matrices in the third dimension. In this paper, we propose a \textit{Convolutional Graph-Tensor Net} (\textit{Conv GT-Net}) for the graph data completion problem, which uses deep neural networks to learn the general transform of graph-tensors. The experimental results on the ego-Facebook data sets show that the proposed \textit{Conv GT-Net} achieves significant improvements on both completion accuracy (50\% higher) and completion speed (3.6x $\sim$ 8.1x faster) over the existing algorithms.
Local word statistics affect reading times independently of surprisal
Authors: Adam Goodkind, Klinton Bicknell
Date: 2021-03-07 00:00:00
Tasks: Sentence

Surprisal theory has provided a unifying framework for understanding many phenomena in sentence processing (Hale, 2001; Levy, 2008a), positing that a word's conditional probability given all prior context fully determines processing difficulty. Problematically for this claim, one local statistic, word frequency, has also been shown to affect processing, even when conditional probability given context is held constant. Here, we ask whether other local statistics have a role in processing, or whether word frequency is a special case. We present the first clear evidence that more complex local statistics, word bigram and trigram probability, also affect processing independently of surprisal. These findings suggest a significant and independent role of local statistics in processing. Further, it motivates research into new generalizations of surprisal that can also explain why local statistical information should have an outsized effect.
Efficient Model Performance Estimation via Feature Histories
Authors: Shengcao Cao, Xiaofang Wang, Kris Kitani
Date: 2021-03-07 00:00:00
Tasks: image-classification, Image Classification, model, Neural Architecture Search

An important step in the task of neural network design, such as hyper-parameter optimization (HPO) or neural architecture search (NAS), is the evaluation of a candidate model's performance. Given fixed computational resources, one can either invest more time training each model to obtain more accurate estimates of final performance, or spend more time exploring a greater variety of models in the configuration space. In this work, we aim to optimize this exploration-exploitation trade-off in the context of HPO and NAS for image classification by accurately approximating a model's maximal performance early in the training process. In contrast to recent accelerated NAS methods customized for certain search spaces, e.g., requiring the search space to be differentiable, our method is flexible and imposes almost no constraints on the search space. Our method uses the evolution history of features of a network during the early stages of training to build a proxy classifier that matches the peak performance of the network under consideration. We show that our method can be combined with multiple search algorithms to find better solutions to a wide range of tasks in HPO and NAS. Using a sampling-based search algorithm and parallel computing, our method can find an architecture which is better than DARTS and with an 80% reduction in wall-clock search time.