pwc-archive/papers-with-abstracts huggingface dataset:
Nonparametric Part Transfer for Fine-grained Recognition
Authors: Christoph Goring, Erik Rodner, Alexander Freytag, Joachim Denzler
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Fine-Grained Image Classification, Image Segmentation, Semantic Segmentation

In the following paper, we present an approach for finegrained recognition based on a new part detection method. In particular, we propose a nonparametric label transfer technique which transfers part constellations from objects with similar global shapes. The possibility for transferring part annotations to unseen images allows for coping with a high degree of pose and view variations in scenarios where traditional detection models (such as deformable part models) fail. Our approach is especially valuable for fine-grained recognition scenarios where intraclass variations are extremely high, and precisely localized features need to be extracted. Furthermore, we show the importance of carefully designed visual extraction strategies, such as combination of complementary feature types and iterative image segmentation, and the resulting impact on the recognition performance. In experiments, our simple yet powerful approach achieves 35.9% and 57.8% accuracy on the CUB-2010 and 2011 bird datasets, which is the current best performance for these benchmarks.
Estimation of Lower Limb Periodic Motions from sEMG Using Least Squares Support Vector Regression
Authors: Q. L. Li, Y. Song, Z. G. Hou
Date: 2014-10-26 00:00:00
Proceeding: 无 2014 10
Tasks: regression

In this paper, a new technique for predicting human lower limb periodic motionsfrom multi-channel surface ElectroMyoGram (s EMG) was proposed on the basis of least-squares support vector regression (LS-SVR). The sEMG signals were sampled from sevenhuman lower limb muscles. Two channels sEMG were selected and mapped to muscle acti-vation levels for angles estimation based on cross-correlation analysis. To deal with the timedelay introduced by low-pass ﬁltering of raw s EMG, a k-order dynamic model was derivedto represent the dynamic relationship between the joint angles and muscle activation levels.The dynamic model was built by data driven LS-SVR with radial basis function kernel. Theinputs of the LS-SVR are muscle activation levels, and the outputs are joint angles of the hipand knee. In experiments, 48 s EMG-angle datasets sampled from six healthy people wereutilized to verify the effectiveness of the proposed method. Result shows that the humanlower limb joint angles can be well estimated in different motion conditions.
Deep Isometric Learning for Visual Recognition
Authors: Haozhi Qi, Chong You, Xiaolong Wang, Yi Ma, Jitendra Malik
Date: 2020-06-30 00:00:00
Proceeding: ICML 2020 1

Initialization, normalization, and skip connections are believed to be three indispensable techniques for training very deep convolutional neural networks and obtaining state-of-the-art performance. This paper shows that deep vanilla ConvNets without normalization nor skip connections can also be trained to achieve surprisingly good performance on standard image recognition benchmarks. This is achieved by enforcing the convolution kernels to be near isometric during initialization and training, as well as by using a variant of ReLU that is shifted towards being isometric. Further experiments show that if combined with skip connections, such near isometric networks can achieve performances on par with (for ImageNet) and better than (for COCO) the standard ResNet, even without normalization at all. Our code is available at https://github.com/HaozhiQi/ISONet.
ChipNet: Real-Time LiDAR Processing for Drivable Region Segmentation on an FPGA
Authors: Yecheng Lyu, Lin Bai, Xinming Huang
Date: 2018-08-10 00:00:00
Tasks: 3D geometry, Autonomous Vehicles, Segmentation, Semantic Segmentation

This paper presents a field-programmable gate array (FPGA) design of a segmentation algorithm based on convolutional neural network (CNN) that can process light detection and ranging (LiDAR) data in real-time. For autonomous vehicles, drivable region segmentation is an essential step that sets up the static constraints for planning tasks. Traditional drivable region segmentation algorithms are mostly developed on camera data, so their performance is susceptible to the light conditions and the qualities of road markings. LiDAR sensors can obtain the 3D geometry information of the vehicle surroundings with high precision. However, it is a computational challenge to process a large amount of LiDAR data in real-time. In this paper, a convolutional neural network model is proposed and trained to perform semantic segmentation using data from the LiDAR sensor. An efficient hardware architecture is proposed and implemented on an FPGA that can process each LiDAR scan in 17.59 ms, which is much faster than the previous works. Evaluated using Ford and KITTI road detection benchmarks, the proposed solution achieves both high accuracy in performance and real-time processing in speed.
The Covering-Assignment Problem for Swarm-powered Ad-hoc Clouds: A Distributed 3D Mapping Use-case
Authors: Leandro R. Costa, Daniel Aloise, Luca G. Gianoli, Andrea Lodi
Date: 2020-04-21 00:00:00
Tasks: 3D Reconstruction

The popularity of drones is rapidly increasing across the different sectors of the economy. Aerial capabilities and relatively low costs make drones the perfect solution to improve the efficiency of those operations that are typically carried out by humans (e.g., building inspection, photo collection). The potential of drone applications can be pushed even further when they are operated in fleets and in a fully autonomous manner, acting de facto as a drone swarm. Besides automating field operations, a drone swarm can serve as an ad-hoc cloud infrastructure built on top of computing and storage resources available across the swarm members and other connected elements. Even in the absence of Internet connectivity, this cloud can serve the workloads generated by the swarm members themselves, as well as by the field agents operating within the area of interest. By considering the practical example of a swarm-powered 3D reconstruction application, we present a new optimization problem for the efficient generation and execution, on top of swarm-powered ad-hoc cloud infrastructure, of multi-node computing workloads subject to data geolocation and clustering constraints. The objective is the minimization of the overall computing times, including both networking delays caused by the inter-drone data transmission and computation delays. We prove that the problem is NP-hard and present two combinatorial formulations to model it. Computational results on the solution of the formulations show that one of them can be used to solve, within the configured time-limit, more than 50% of the considered real-world instances involving up to two hundred images and six drones.
GANs in computer vision ebook
Authors: Nikolas Adaloglou, Sergios Karagianakos
Date: 2020-06-10 00:00:00
Proceeding: ebook 2020 6
Tasks: Articles, Conditional Image Generation, Image Generation, Image-to-Image Translation, Video Generation, Video-to-Video Synthesis

In this article-series we are reviewing the most fundamental works of Generative Adversarial Networks in Computer Vision. We start from the very beginning from concepts such as generative learning, adversarial learning. We provide some code and illustrations for educational purposes. The goal is to focus on the intuition of the models, by tackling the multiple problems that arise when training a GAN. We have thoroughly analyzed more than 20 papers in 6 different articles in a chronological order. We will continue to update the GAN series, based on the newer publications or older ones that we skipped. We do hope that this series will provide you a big overview of the field, so that you will not need to read all the literature by yourself, independent of your background on GANs.
Fixing Convergence of Gaussian Belief Propagation
Authors: Jason K. Johnson, Danny Bickson, Danny Dolev
Date: 2009-01-27 00:00:00

Gaussian belief propagation (GaBP) is an iterative message-passing algorithm for inference in Gaussian graphical models. It is known that when GaBP converges it converges to the correct MAP estimate of the Gaussian random vector and simple sufficient conditions for its convergence have been established. In this paper we develop a double-loop algorithm for forcing convergence of GaBP. Our method computes the correct MAP estimate even in cases where standard GaBP would not have converged. We further extend this construction to compute least-squares solutions of over-constrained linear systems. We believe that our construction has numerous applications, since the GaBP algorithm is linked to solution of linear systems of equations, which is a fundamental problem in computer science and engineering. As a case study, we discuss the linear detection problem. We show that using our new construction, we are able to force convergence of Montanari's linear detection algorithm, in cases where it would originally fail. As a consequence, we are able to increase significantly the number of users that can transmit concurrently.
Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over Modules
Authors: Sarthak Mittal, Alex Lamb, Anirudh Goyal, Vikram Voleti, Murray Shanahan, Guillaume Lajoie, Michael Mozer, Yoshua Bengio
Date: 2020-06-30 00:00:00
Proceeding: ICML 2020 1
Tasks: image-classification, Image Classification, Language Modeling, Language Modelling, Open-Ended Question Answering, Sequential Image Classification, Video Prediction

Robust perception relies on both bottom-up and top-down signals. Bottom-up signals consist of what's directly observed through sensation. Top-down signals consist of beliefs and expectations based on past experience and short-term memory, such as how the phrase `peanut butter and~...' will be completed. The optimal combination of bottom-up and top-down information remains an open question, but the manner of combination must be dynamic and both context and task dependent. To effectively utilize the wealth of potential top-down information available, and to prevent the cacophony of intermixed signals in a bidirectional architecture, mechanisms are needed to restrict information flow. We explore deep recurrent neural net architectures in which bottom-up and top-down signals are dynamically combined using attention. Modularity of the architecture further restricts the sharing and communication of information. Together, attention and modularity direct information flow, which leads to reliable performance improvements in perceptual and language tasks, and in particular improves robustness to distractions and noisy data. We demonstrate on a variety of benchmarks in language modeling, sequential image classification, video prediction and reinforcement learning that the \emph{bidirectional} information flow can improve results over strong baselines.
Biologically Inspired Mechanisms for Adversarial Robustness
Authors: Manish V. Reddy, Andrzej Banburski, Nishka Pant, Tomaso Poggio
Date: 2020-06-29 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: Adversarial Robustness

A convolutional neural network strongly robust to adversarial perturbations at reasonable computational and performance cost has not yet been demonstrated. The primate visual ventral stream seems to be robust to small perturbations in visual stimuli but the underlying mechanisms that give rise to this robust perception are not understood. In this work, we investigate the role of two biologically plausible mechanisms in adversarial robustness. We demonstrate that the non-uniform sampling performed by the primate retina and the presence of multiple receptive fields with a range of receptive field sizes at each eccentricity improve the robustness of neural networks to small adversarial perturbations. We verify that these two mechanisms do not suffer from gradient obfuscation and study their contribution to adversarial robustness through ablation studies.
Learning and Planning in Average-Reward Markov Decision Processes
Authors: Yi Wan, Abhishek Naik, Richard S. Sutton
Date: 2020-06-29 00:00:00

We introduce learning and planning algorithms for average-reward MDPs, including 1) the first general proven-convergent off-policy model-free control algorithm without reference states, 2) the first proven-convergent off-policy model-free prediction algorithm, and 3) the first off-policy learning algorithm that converges to the actual value function rather than to the value function plus an offset. All of our algorithms are based on using the temporal-difference error rather than the conventional error when updating the estimate of the average reward. Our proof techniques are a slight generalization of those by Abounadi, Bertsekas, and Borkar (2001). In experiments with an Access-Control Queuing Task, we show some of the difficulties that can arise when using methods that rely on reference states and argue that our new algorithms can be significantly easier to use.
Theory-Inspired Path-Regularized Differential Network Architecture Search
Authors: Pan Zhou, Caiming Xiong, Richard Socher, Steven C. H. Hoi
Date: 2020-06-30 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: image-classification, Image Classification

Despite its high search efficiency, differential architecture search (DARTS) often selects network architectures with dominated skip connections which lead to performance degradation. However, theoretical understandings on this issue remain absent, hindering the development of more advanced methods in a principled way. In this work, we solve this problem by theoretically analyzing the effects of various types of operations, e.g. convolution, skip connection and zero operation, to the network optimization. We prove that the architectures with more skip connections can converge faster than the other candidates, and thus are selected by DARTS. This result, for the first time, theoretically and explicitly reveals the impact of skip connections to fast network optimization and its competitive advantage over other types of operations in DARTS. Then we propose a theory-inspired path-regularized DARTS that consists of two key modules: (i) a differential group-structured sparse binary gate introduced for each operation to avoid unfair competition among operations, and (ii) a path-depth-wise regularization used to incite search exploration for deep architectures that often converge slower than shallow ones as shown in our theory and are not well explored during the search. Experimental results on image classification tasks validate its advantages.
Self-Supervised Learning of a Biologically-Inspired Visual Texture Model
Authors: Nikhil Parthasarathy, Eero P. Simoncelli
Date: 2020-06-30 00:00:00
Tasks: Self-Supervised Learning, Texture Classification

We develop a model for representing visual texture in a low-dimensional feature space, along with a novel self-supervised learning objective that is used to train it on an unlabeled database of texture images. Inspired by the architecture of primate visual cortex, the model uses a first stage of oriented linear filters (corresponding to cortical area V1), consisting of both rectified units (simple cells) and pooled phase-invariant units (complex cells). These responses are processed by a second stage (analogous to cortical area V2) consisting of convolutional filters followed by half-wave rectification and pooling to generate V2 'complex cell' responses. The second stage filters are trained on a set of unlabeled homogeneous texture images, using a novel contrastive objective that maximizes the distance between the distribution of V2 responses to individual images and the distribution of responses across all images. When evaluated on texture classification, the trained model achieves substantially greater data-efficiency than a variety of deep hierarchical model architectures. Moreover, we show that the learned model exhibits stronger representational similarity to texture responses of neural populations recorded in primate V2 than pre-trained deep CNNs.
Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation
Authors: Yingda Xia, Dong Yang, Zhiding Yu, Fengze Liu, Jinzheng Cai, Lequan Yu, Zhuotun Zhu, Daguang Xu, Alan Yuille, Holger Roth
Date: 2020-06-28 00:00:00
Tasks: Domain Adaptation, Image Segmentation, Medical Image Analysis, Medical Image Segmentation, Organ Segmentation, Pancreas Segmentation, Segmentation, Semantic Segmentation, Semi-supervised Medical Image Segmentation, Unsupervised Domain Adaptation, Volumetric Medical Image Segmentation

Although having achieved great success in medical image segmentation, deep learning-based approaches usually require large amounts of well-annotated data, which can be extremely expensive in the field of medical image analysis. Unlabeled data, on the other hand, is much easier to acquire. Semi-supervised learning and unsupervised domain adaptation both take the advantage of unlabeled data, and they are closely related to each other. In this paper, we propose uncertainty-aware multi-view co-training (UMCT), a unified framework that addresses these two tasks for volumetric medical image segmentation. Our framework is capable of efficiently utilizing unlabeled data for better performance. We firstly rotate and permute the 3D volumes into multiple views and train a 3D deep network on each view. We then apply co-training by enforcing multi-view consistency on unlabeled data, where an uncertainty estimation of each view is utilized to achieve accurate labeling. Experiments on the NIH pancreas segmentation dataset and a multi-organ segmentation dataset show state-of-the-art performance of the proposed framework on semi-supervised medical image segmentation. Under unsupervised domain adaptation settings, we validate the effectiveness of this work by adapting our multi-organ segmentation model to two pathological organs from the Medical Segmentation Decathlon Datasets. Additionally, we show that our UMCT-DA model can even effectively handle the challenging situation where labeled source data is inaccessible, demonstrating strong potentials for real-world applications.
TinyRadarNN: Combining Spatial and Temporal Convolutional Neural Networks for Embedded Gesture Recognition with Short Range Radars
Authors: Moritz Scherer, Michele Magno, Jonas Erb, Philipp Mayer, Manuel Eggimann, Luca Benini
Date: 2020-06-25 00:00:00
Tasks: Gesture Recognition, Hand Gesture Recognition, Hand-Gesture Recognition

This work proposes a low-power high-accuracy embedded hand-gesture recognition algorithm targeting battery-operated wearable devices using low power short-range RADAR sensors. A 2D Convolutional Neural Network (CNN) using range frequency Doppler features is combined with a Temporal Convolutional Neural Network (TCN) for time sequence prediction. The final algorithm has a model size of only 46 thousand parameters, yielding a memory footprint of only 92 KB. Two datasets containing 11 challenging hand gestures performed by 26 different people have been recorded containing a total of 20,210 gesture instances. On the 11 hand gesture dataset, accuracies of 86.6% (26 users) and 92.4% (single user) have been achieved, which are comparable to the state-of-the-art, which achieves 87% (10 users) and 94% (single user), while using a TCN-based network that is 7500x smaller than the state-of-the-art. Furthermore, the gesture recognition classifier has been implemented on a Parallel Ultra-Low Power Processor, demonstrating that real-time prediction is feasible with only 21 mW of power consumption for the full TCN sequence prediction network, while a system-level power consumption of less than 100 mW is achieved. We provide open-source access to all the code and data collected and used in this work on tinyradar.ethz.ch.
Learning to Read through Machine Teaching
Authors: Ayon Sen, Christopher R. Cox, Matthew Cooper Borkenhagen, Mark S. Seidenberg, Xiaojin Zhu
Date: 2020-06-30 00:00:00
Tasks: Combinatorial Optimization

Learning to read words aloud is a major step towards becoming a reader. Many children struggle with the task because of the inconsistencies of English spelling-sound correspondences. Curricula vary enormously in how these patterns are taught. Children are nonetheless expected to master the system in limited time (by grade 4). We used a cognitively interesting neural network architecture to examine whether the sequence of learning trials could be structured to facilitate learning. This is a hard combinatorial optimization problem even for a modest number of learning trials (e.g., 10K). We show how this sequence optimization problem can be posed as optimizing over a time varying distribution i.e., defining probability distributions over words at different steps in training. We then use stochastic gradient descent to find an optimal time-varying distribution and a corresponding optimal training sequence. We observed significant improvement on generalization accuracy compared to baseline conditions (random sequences; sequences biased by word frequency). These findings suggest an approach to improving learning outcomes in domains where performance depends on ability to generalize beyond limited training experience.
Bounded Rationality in Las Vegas: Probabilistic Finite Automata PlayMulti-Armed Bandits
Authors: Xinming Liu, Joseph Y. Halpern
Date: 2020-06-30 00:00:00

While traditional economics assumes that humans are fully rational agents who always maximize their expected utility, in practice, we constantly observe apparently irrational behavior. One explanation is that people have limited computational power, so that they are, quite rationally, making the best decisions they can, given their computational limitations. To test this hypothesis, we consider the multi-armed bandit (MAB) problem. We examine a simple strategy for playing an MAB that can be implemented easily by a probabilistic finite automaton (PFA). Roughly speaking, the PFA sets certain expectations, and plays an arm as long as it meets them. If the PFA has sufficiently many states, it performs near-optimally. Its performance degrades gracefully as the number of states decreases. Moreover, the PFA acts in a "human-like" way, exhibiting a number of standard human biases, like an optimism bias and a negativity bias.
MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning
Authors: Elise van der Pol, Daniel E. Worrall, Herke van Hoof, Frans A. Oliehoek, Max Welling
Date: 2020-06-30 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper introduces MDP homomorphic networks for deep reinforcement learning. MDP homomorphic networks are neural networks that are equivariant under symmetries in the joint state-action space of an MDP. Current approaches to deep reinforcement learning do not usually exploit knowledge about such structure. By building this prior knowledge into policy and value networks using an equivariance constraint, we can reduce the size of the solution space. We specifically focus on group-structured symmetries (invertible transformations). Additionally, we introduce an easy method for constructing equivariant network layers numerically, so the system designer need not solve the constraints by hand, as is typically done. We construct MDP homomorphic MLPs and CNNs that are equivariant under either a group of reflections or rotations. We show that such networks converge faster than unstructured baselines on CartPole, a grid world and Pong.