pwc-archive/papers-with-abstracts huggingface dataset:
Provably Efficient Reinforcement Learning with Aggregated States
Authors: Shi Dong, Benjamin Van Roy, Zhengyuan Zhou
Date: 2019-12-13 00:00:00
Tasks: Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We establish that an optimistic variant of Q-learning applied to a fixed-horizon episodic Markov decision process with an aggregated state representation incurs regret $\tilde{\mathcal{O}}(\sqrt{H^5 M K} + \epsilon HK)$, where $H$ is the horizon, $M$ is the number of aggregate states, $K$ is the number of episodes, and $\epsilon$ is the largest difference between any pair of optimal state-action values associated with a common aggregate state. Notably, this regret bound does not depend on the number of states or actions and indicates that asymptotic per-period regret is no greater than $\epsilon$, independent of horizon. To our knowledge, this is the first such result that applies to reinforcement learning with nontrivial value function approximation without any restrictions on transition probabilities.
Fast Image Caption Generation with Position Alignment
Authors: Zheng-cong Fei
Date: 2019-12-13 00:00:00
Tasks: Caption Generation, Decoder, Image Captioning, Position, Sentence

Recent neural network models for image captioning usually employ an encoder-decoder architecture, where the decoder adopts a recursive sequence decoding way. However, such autoregressive decoding may result in sequential error accumulation and slow generation which limit the applications in practice. Non-autoregressive (NA) decoding has been proposed to cover these issues but suffers from language quality problem due to the indirect modeling of the target distribution. Towards that end, we propose an improved NA prediction framework to accelerate image captioning. Our decoding part consists of a position alignment to order the words that describe the content detected in the given image, and a fine non-autoregressive decoder to generate elegant descriptions. Furthermore, we introduce an inference strategy that regards position information as a latent variable to guide the further sentence generation. The Experimental results on public datasets show that our proposed model achieves better performance compared to general NA captioning models, while achieves comparable performance as autoregressive image captioning models with a significant speedup.
Bonn Activity Maps: Dataset Description
Authors: Julian Tanke, Oh-Hun Kwon, Patrick Stotko, Radu Alexandru Rosu, Michael Weinmann, Hassan Errami, Sven Behnke, Maren Bennewitz, Reinhard Klein, Andreas Weber, Angela Yao, Juergen Gall
Date: 2019-12-13 00:00:00
Tasks: Activity Recognition

The key prerequisite for accessing the huge potential of current machine learning techniques is the availability of large databases that capture the complex relations of interest. Previous datasets are focused on either 3D scene representations with semantic information, tracking of multiple persons and recognition of their actions, or activity recognition of a single person in captured 3D environments. We present Bonn Activity Maps, a large-scale dataset for human tracking, activity recognition and anticipation of multiple persons. Our dataset comprises four different scenes that have been recorded by time-synchronized cameras each only capturing the scene partially, the reconstructed 3D models with semantic annotations, motion trajectories for individual people including 3D human poses as well as human activity annotations. We utilize the annotations to generate activity likelihoods on the 3D models called activity maps.
Elastic registration based on compliance analysis and biomechanical graph matching
Authors: Jaime Garcia Guevara, Igor Peterlik, Marie-Odile Berger, Stéphane Cotin
Date: 2019-12-13 00:00:00
Tasks: Graph Matching

An automatic elastic registration method suited for vascularized organs is proposed. The vasculature in both the preoperative and intra-operative images is represented as a graph. A typical application of this method is the fusion of pre-operative information onto the organ during surgery, to compensate for the limited details provided by the intra-operative imaging modality (e.g. CBCT) and to cope with changes in the shape of the organ. Due to image modalities differences and organ deformation, each graph has a different topology and shape. The Adaptive Compliance Graph Matching (ACGM) method presented does not require any manual initialization, handles intra-operative nonrigid deformations of up to 65 mm and computes a complete displacement field over the organ from only the matched vasculature. ACGM is better than the previous Biomechanical Graph Matching method 3 (BGM) because it uses an efficient biomechanical vascularized liver model to compute the organ's transformation and the vessels bifurcations compliance. This allows to efficiently find the best graph matches with a novel compliance-based adaptive search. These contributions are evaluated on ten realistic synthetic and two real porcine automatically segmented datasets. ACGM obtains better target registration error (TRE) than BGM, with an average TRE in the real datasets of 4.2 mm compared to 6.5 mm, respectively. It also is up to one order of magnitude faster, less dependent on the parameters used and more robust to noise.
Learned Video Compression via Joint Spatial-Temporal Correlation Exploration
Authors: Haojie Liu, Han Shen, Lichao Huang, Ming Lu, Tong Chen, Zhan Ma
Date: 2019-12-13 00:00:00
Tasks: Optical Flow Estimation, Video Compression

Traditional video compression technologies have been developed over decades in pursuit of higher coding efficiency. Efficient temporal information representation plays a key role in video coding. Thus, in this paper, we propose to exploit the temporal correlation using both first-order optical flow and second-order flow prediction. We suggest an one-stage learning approach to encapsulate flow as quantized features from consecutive frames which is then entropy coded with adaptive contexts conditioned on joint spatial-temporal priors to exploit second-order correlations. Joint priors are embedded in autoregressive spatial neighbors, co-located hyper elements and temporal neighbors using ConvLSTM recurrently. We evaluate our approach for the low-delay scenario with High-Efficiency Video Coding (H.265/HEVC), H.264/AVC and another learned video compression method, following the common test settings. Our work offers the state-of-the-art performance, with consistent gains across all popular test sequences.
A Method for Arbitrary Instance Style Transfer
Authors: Zhifeng Yu, Yusheng Wu, Tianyou Wang
Date: 2019-12-13 00:00:00
Tasks: Style Transfer

The ability to synthesize style and content of different images to form a visually coherent image holds great promise in various applications such as stylistic painting, design prototyping, image editing, and augmented reality. However, the majority of works in image style transfer have focused on transferring the style of an image to the entirety of another image, and only a very small number of works have experimented on methods to transfer style to an instance of another image. Researchers have proposed methods to circumvent the difficulty of transferring style to an instance in an arbitrary shape. In this paper, we propose a topologically inspired algorithm called Forward Stretching to tackle this problem by transforming an instance into a tensor representation, which allows us to transfer style to this instance itself directly. Forward Stretching maps pixels to specific positions and interpolate values between pixels to transform an instance to a tensor. This algorithm allows us to introduce a method to transfer arbitrary style to an instance in an arbitrary shape. We showcase the results of our method in this paper.
MM Algorithms for Distance Covariance based Sufficient Dimension Reduction and Sufficient Variable Selection
Authors: Runxiong Wu, Xin Chen
Date: 2019-12-13 00:00:00
Tasks: Dimensionality Reduction, Variable Selection

Sufficient dimension reduction (SDR) using distance covariance (DCOV) was recently proposed as an approach to dimension-reduction problems. Compared with other SDR methods, it is model-free without estimating link function and does not require any particular distributions on predictors (see Sheng and Yin, 2013, 2016). However, the DCOV-based SDR method involves optimizing a nonsmooth and nonconvex objective function over the Stiefel manifold. To tackle the numerical challenge, we novelly formulate the original objective function equivalently into a DC (Difference of Convex functions) program and construct an iterative algorithm based on the majorization-minimization (MM) principle. At each step of the MM algorithm, we inexactly solve the quadratic subproblem on the Stiefel manifold by taking one iteration of Riemannian Newton's method. The algorithm can also be readily extended to sufficient variable selection (SVS) using distance covariance. We establish the convergence property of the proposed algorithm under some regularity conditions. Simulation studies show our algorithm drastically improves the computation efficiency and is robust across various settings compared with the existing method. Supplemental materials for this article are available.
TopoAct: Visually Exploring the Shape of Activations in Deep Learning
Authors: Archit Rathore, Nithin Chalapathi, Sourabh Palande, Bei Wang
Date: 2019-12-13 00:00:00
Tasks: Anomaly Detection, Deep Learning, Image Classification, text-classification, Text Classification, Topological Data Analysis

Deep neural networks such as GoogLeNet, ResNet, and BERT have achieved impressive performance in tasks such as image and text classification. To understand how such performance is achieved, we probe a trained deep neural network by studying neuron activations, i.e., combinations of neuron firings, at various layers of the network in response to a particular input. With a large number of inputs, we aim to obtain a global view of what neurons detect by studying their activations. In particular, we develop visualizations that show the shape of the activation space, the organizational principle behind neuron activations, and the relationships of these activations within a layer. Applying tools from topological data analysis, we present TopoAct, a visual exploration system to study topological summaries of activation vectors. We present exploration scenarios using TopoAct that provide valuable insights into learned representations of neural networks. We expect TopoAct to give a topological perspective that enriches the current toolbox of neural network analysis, and to provide a basis for network architecture diagnosis and data anomaly detection.
Toward Automatic Threat Recognition for Airport X-ray Baggage Screening with Deep Convolutional Object Detection
Authors: Kevin J Liang, John B. Sigman, Gregory P. Spell, Dan Strellis, William Chang, Felix Liu, Tejas Mehta, Lawrence Carin
Date: 2019-12-13 00:00:00
Tasks: object-detection, Object Detection

For the safety of the traveling public, the Transportation Security Administration (TSA) operates security checkpoints at airports in the United States, seeking to keep dangerous items off airplanes. At these checkpoints, the TSA employs a fleet of X-ray scanners, such as the Rapiscan 620DV, so Transportation Security Officers (TSOs) can inspect the contents of carry-on possessions. However, identifying and locating all potential threats can be a challenging task. As a result, the TSA has taken a recent interest in deep learning-based automated detection algorithms that can assist TSOs. In a collaboration funded by the TSA, we collected a sizable new dataset of X-ray scans with a diverse set of threats in a wide array of contexts, trained several deep convolutional object detection models, and integrated such models into the Rapiscan 620DV, resulting in functional prototypes capable of operating in real time. We show performance of our models on held-out evaluation sets, analyze several design parameters, and demonstrate the potential of such systems for automated detection of threats that can be found in airports.
Down to the Last Detail: Virtual Try-on with Detail Carving
Authors: Jiahang Wang, Wei zhang, Weizhong Liu, Tao Mei
Date: 2019-12-13 00:00:00
Tasks: Virtual Try-on

Virtual try-on under arbitrary poses has attracted lots of research attention due to its huge potential applications. However, existing methods can hardly preserve the details in clothing texture and facial identity (face, hair) while fitting novel clothes and poses onto a person. In this paper, we propose a novel multi-stage framework to synthesize person images, where rich details in salient regions can be well preserved. Specifically, a multi-stage framework is proposed to decompose the generation into spatial alignment followed by a coarse-to-fine generation. To better preserve the details in salient areas such as clothing and facial areas, we propose a Tree-Block (tree dilated fusion block) to harness multi-scale features in the generator networks. With end-to-end training of multiple stages, the whole framework can be jointly optimized for results with significantly better visual fidelity and richer details. Extensive experiments on standard datasets demonstrate that our proposed framework achieves the state-of-the-art performance, especially in preserving the visual details in clothing texture and facial identity. Our implementation will be publicly available soon.
Queueing Analysis of GPU-Based Inference Servers with Dynamic Batching: A Closed-Form Characterization
Authors: Yoshiaki Inoue
Date: 2019-12-13 00:00:00
Tasks: Computational Efficiency, Form, GPU

GPU-accelerated computing is a key technology to realize high-speed inference servers using deep neural networks (DNNs). An important characteristic of GPU-based inference is that the computational efficiency, in terms of the processing speed and energy consumption, drastically increases by processing multiple jobs together in a batch. In this paper, we formulate GPU-based inference servers as a batch service queueing model with batch-size dependent processing times. We first show that the energy efficiency of the server monotonically increases with the arrival rate of inference jobs, which suggests that it is energy-efficient to operate the inference server under a utilization level as high as possible within a latency requirement of inference jobs. We then derive a closed-form upper bound for the mean latency, which provides a simple characterization of the latency performance. Through simulation and numerical experiments, we show that the exact value of the mean latency is well approximated by this upper bound. We further compare this upper bound with the latency curve measured in real implementation of GPU-based inference servers and we show that the real performance curve is well explained by the derived simple formula.
Sim2Real Predictivity: Does Evaluation in Simulation Predict Real-World Performance?
Authors: Abhishek Kadian, Joanne Truong, Aaron Gokaslan, Alexander Clegg, Erik Wijmans, Stefan Lee, Manolis Savva, Sonia Chernova, Dhruv Batra
Date: 2019-12-13 00:00:00
Tasks: PointGoal Navigation, Visual Navigation

Does progress in simulation translate to progress on robots? If one method outperforms another in simulation, how likely is that trend to hold in reality on a robot? We examine this question for embodied PointGoal navigation, developing engineering tools and a research paradigm for evaluating a simulator by its sim2real predictivity. First, we develop Habitat-PyRobot Bridge (HaPy), a library for seamless execution of identical code on simulated agents and robots, transferring simulation-trained agents to a LoCoBot platform with a one-line code change. Second, we investigate the sim2real predictivity of Habitat-Sim for PointGoal navigation. We 3D-scan a physical lab space to create a virtualized replica, and run parallel tests of 9 different models in reality and simulation. We present a new metric called Sim-vs-Real Correlation Coefficient (SRCC) to quantify predictivity. We find that SRCC for Habitat as used for the CVPR19 challenge is low (0.18 for the success metric), suggesting that performance differences in this simulator-based challenge do not persist after physical deployment. This gap is largely due to AI agents learning to exploit simulator imperfections, abusing collision dynamics to 'slide' along walls, leading to shortcuts through otherwise non-navigable space. Naturally, such exploits do not work in the real world. Our experiments show that it is possible to tune simulation parameters to improve sim2real predictivity (e.g. improving $SRCC_{Succ}$ from 0.18 to 0.844), increasing confidence that in-simulation comparisons will translate to deployed systems in reality.
Small Object Detection using Context and Attention
Authors: Jeong-Seon Lim, Marcella Astrid, Hyun-Jin Yoon, Seung-Ik Lee
Date: 2019-12-13 00:00:00
Tasks: Object, object-detection, Object Detection, Small Object Detection

There are many limitations applying object detection algorithm on various environments. Especially detecting small objects is still challenging because they have low resolution and limited information. We propose an object detection method using context for improving accuracy of detecting small objects. The proposed method uses additional features from different layers as context by concatenating multi-scale features. We also propose object detection with attention mechanism which can focus on the object in image, and it can include contextual information from target layer. Experimental results shows that proposed method also has higher accuracy than conventional SSD on detecting small objects. Also, for 300$\times$300 input, we achieved 78.1% Mean Average Precision (mAP) on the PASCAL VOC2007 test set.
Grounding-Tracking-Integration
Authors: Zhengyuan Yang, Tushar Kumar, Tianlang Chen, Jinsong Su, Jiebo Luo
Date: 2019-12-13 00:00:00

In this paper, we study Tracking by Language that localizes the target box sequence in a video based on a language query. We propose a framework called GTI that decomposes the problem into three sub-tasks: Grounding, Tracking, and Integration. The three sub-task modules operate simultaneously and predict the box sequence frame-by-frame. "Grounding" predicts the referred region directly from the language query. "Tracking" localizes the target based on the history of the grounded regions in previous frames. "Integration" generates final predictions by synergistically combining grounding and tracking. With the "integration" task as the key, we explore how to indicate the quality of the grounded regions in each frame and achieve the desired mutually beneficial combination. To this end, we propose an "RT-integration" method that defines and predicts two scores to guide the integration: 1) R-score represents the Region correctness whether the grounding prediction accurately covers the target, and 2) T-score represents the Template quality whether the region provides informative visual cues to improve tracking in future frames. We present our real-time GTI implementation with the proposed RT-integration, and benchmark the framework on LaSOT and Lingual OTB99 with highly promising results. Moreover, we produce a disambiguated version of LaSOT queries to facilitate future tracking by language studies.
Identity Preserve Transform: Understand What Activity Classification Models Have Learnt
Authors: Jialing Lyu, Weichao Qiu, Xinyue Wei, Yi Zhang, Alan Yuille, Zheng-Jun Zha
Date: 2019-12-13 00:00:00
Tasks: Classification, General Classification

Activity classification has observed great success recently. The performance on small dataset is almost saturated and people are moving towards larger datasets. What leads to the performance gain on the model and what the model has learnt? In this paper we propose identity preserve transform (IPT) to study this problem. IPT manipulates the nuisance factors (background, viewpoint, etc.) of the data while keeping those factors related to the task (human motion) unchanged. To our surprise, we found popular models are using highly correlated information (background, object) to achieve high classification accuracy, rather than using the essential information (human motion). This can explain why an activity classification model usually fails to generalize to datasets it is not trained on. We implement IPT in two forms, i.e. image-space transform and 3D transform, using synthetic images. The tool will be made open-source to help study model and dataset design.
Short-duration Speaker Verification (SdSV) Challenge 2021: the Challenge Evaluation Plan
Authors: Hossein Zeinali, Kong Aik Lee, Jahangir Alam, Lukas Burget
Date: 2019-12-13 00:00:00
Tasks: Speaker Recognition, Speaker Verification, Text-Dependent Speaker Verification, Text-Independent Speaker Verification

This document describes the Short-duration Speaker Verification (SdSV) Challenge 2021. The main goal of the challenge is to evaluate new technologies for text-dependent (TD) and text-independent (TI) speaker verification (SV) in a short duration scenario. The proposed challenge evaluates SdSV with varying degree of phonetic overlap between the enrollment and test utterances (cross-lingual). It is the first challenge with a broad focus on systematic benchmark and analysis on varying degrees of phonetic variability on short-duration speaker recognition. We expect that modern methods (deep neural networks in particular) will play a key role.
Recruitment-imitation Mechanism for Evolutionary Reinforcement Learning
Authors: Shuai Lü, Shuai Han, Wenbo Zhou, Junwei Zhang
Date: 2019-12-13 00:00:00
Tasks: continuous-control, Continuous Control, Efficient Exploration, Evolutionary Algorithms, Imitation Learning, MuJoCo, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement learning, evolutionary algorithms and imitation learning are three principal methods to deal with continuous control tasks. Reinforcement learning is sample efficient, yet sensitive to hyper-parameters setting and needs efficient exploration; Evolutionary algorithms are stable, but with low sample efficiency; Imitation learning is both sample efficient and stable, however it requires the guidance of expert data. In this paper, we propose Recruitment-imitation Mechanism (RIM) for evolutionary reinforcement learning, a scalable framework that combines advantages of the three methods mentioned above. The core of this framework is a dual-actors and single critic reinforcement learning agent. This agent can recruit high-fitness actors from the population of evolutionary algorithms, which instructs itself to learn from experience replay buffer. At the same time, low-fitness actors in the evolutionary population can imitate behavior patterns of the reinforcement learning agent and improve their adaptability. Reinforcement and imitation learners in this framework can be replaced with any off-policy actor-critic reinforcement learner or data-driven imitation learner. We evaluate RIM on a series of benchmarks for continuous control tasks in Mujoco. The experimental results show that RIM outperforms prior evolutionary or reinforcement learning methods. The performance of RIM's components is significantly better than components of previous evolutionary reinforcement learning algorithm, and the recruitment using soft update enables reinforcement learning agent to learn faster than that using hard update.