pwc-archive/papers-with-abstracts huggingface dataset:
Hallucinating IDT Descriptors and I3D Optical Flow Features for Action Recognition with CNNs
Authors: Lei Wang, Piotr Koniusz, Du. Q. Huynh
Date: 2019-06-13 00:00:00
Proceeding: ICCV 2019 10
Tasks: Action Classification, Action Recognition, Hallucination, Optical Flow Estimation, Scene Recognition

In this paper, we revive the use of old-fashioned handcrafted video representations for action recognition and put new life into these techniques via a CNN-based hallucination step. Despite of the use of RGB and optical flow frames, the I3D model (amongst others) thrives on combining its output with the Improved Dense Trajectory (IDT) and extracted with its low-level video descriptors encoded via Bag-of-Words (BoW) and Fisher Vectors (FV). Such a fusion of CNNs and handcrafted representations is time-consuming due to pre-processing, descriptor extraction, encoding and tuning parameters. Thus, we propose an end-to-end trainable network with streams which learn the IDT-based BoW/FV representations at the training stage and are simple to integrate with the I3D model. Specifically, each stream takes I3D feature maps ahead of the last 1D conv. layer and learns to `translate' these maps to BoW/FV representations. Thus, our model can hallucinate and use such synthesized BoW/FV representations at the testing stage. We show that even features of the entire I3D optical flow stream can be hallucinated thus simplifying the pipeline. Our model saves 20-55h of computations and yields state-of-the-art results on four publicly available datasets.
Stand-Alone Self-Attention in Vision Models
Authors: Prajit Ramachandran, Niki Parmar, Ashish Vaswani, Irwan Bello, Anselm Levskaya, Jonathon Shlens
Date: 2019-06-13 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: object-detection, Object Detection

Convolutions are a fundamental building block of modern computer vision systems. Recent approaches have argued for going beyond convolutions in order to capture long-range dependencies. These efforts focus on augmenting convolutional models with content-based interactions, such as self-attention and non-local means, to achieve gains on a number of vision tasks. The natural question that arises is whether attention can be a stand-alone primitive for vision models instead of serving as just an augmentation on top of convolutions. In developing and testing a pure self-attention vision model, we verify that self-attention can indeed be an effective stand-alone layer. A simple procedure of replacing all instances of spatial convolutions with a form of self-attention applied to ResNet model produces a fully self-attentional model that outperforms the baseline on ImageNet classification with 12% fewer FLOPS and 29% fewer parameters. On COCO object detection, a pure self-attention model matches the mAP of a baseline RetinaNet while having 39% fewer FLOPS and 34% fewer parameters. Detailed ablation studies demonstrate that self-attention is especially impactful when used in later layers. These results establish that stand-alone self-attention is an important addition to the vision practitioner's toolbox.
Meaning to Form: Measuring Systematicity as Information
Authors: Tiago Pimentel, Arya D. McCarthy, Damián E. Blasi, Brian Roark, Ryan Cotterell
Date: 2019-06-13 00:00:00
Proceeding: ACL 2019 7
Tasks: Form

A longstanding debate in semiotics centers on the relationship between linguistic signs and their corresponding semantics: is there an arbitrary relationship between a word form and its meaning, or does some systematic phenomenon pervade? For instance, does the character bigram \textit{gl} have any systematic relationship to the meaning of words like \textit{glisten}, \textit{gleam} and \textit{glow}? In this work, we offer a holistic quantification of the systematicity of the sign using mutual information and recurrent neural networks. We employ these in a data-driven and massively multilingual approach to the question, examining 106 languages. We find a statistically significant reduction in entropy when modeling a word form conditioned on its semantic representation. Encouragingly, we also recover well-attested English examples of systematic affixes. We conclude with the meta-point: Our approximate effect size (measured in bits) is quite small---despite some amount of systematicity between form and meaning, an arbitrary relationship and its resulting benefits dominate human language.
Dynamic PET cardiac and parametric image reconstruction: a fixed-point proximity gradient approach using patch-based DCT and tensor SVD regularization
Authors: Ida Häggström, Yizun Lin, Si Li, Andrzej Krol, Yuesheng Xu, C. Ross Schmidtlein
Date: 2019-06-13 00:00:00
Tasks: Image Reconstruction, SSIM

Our aim was to enhance visual quality and quantitative accuracy of dynamic positron emission tomography (PET)uptake images by improved image reconstruction, using sophisticated sparse penalty models that incorporate both 2D spatial+1D temporal (3DT) information. We developed two new 3DT PET reconstruction algorithms, incorporating different temporal and spatial penalties based on discrete cosine transform (DCT)w/ patches, and tensor nuclear norm (TNN) w/ patches, and compared to frame-by-frame methods; conventional 2D ordered subsets expectation maximization (OSEM) w/ post-filtering and 2D-DCT and 2D-TNN. A 3DT brain phantom with kinetic uptake (2-tissue model), and a moving 3DT cardiac/lung phantom was simulated and reconstructed. For the cardiac/lung phantom, an additional cardiac gated 2D-OSEM set was reconstructed. The structural similarity index (SSIM) and relative root mean squared error (rRMSE) relative ground truth was investigated. The image derived left ventricular (LV) volume for the cardiac/lung images was found by region growing and parametric images of the brain phantom were calculated. For the cardiac/lung phantom, 3DT-TNN yielded optimal images, and 3DT-DCT was best for the brain phantom. The optimal LV volume from the 3DT-TNN images was on average 11 and 55 percentage points closer to the true value compared to cardiac gated 2D-OSEM and 2D-OSEM respectively. Compared to 2D-OSEM, parametric images based on 3DT-DCT images generally had smaller bias and higher SSIM. Our novel methods that incorporate both 2D spatial and 1D temporal penalties produced dynamic PET images of higher quality than conventional 2D methods, w/o need for post-filtering. Breathing and cardiac motion were simultaneously captured w/o need for respiratory or cardiac gating. LV volumes were better recovered, and subsequently fitted parametric images were generally less biased and of higher quality.
Learning Instance Occlusion for Panoptic Segmentation
Authors: Justin Lazarow, Kwonjoon Lee, Kunyu Shi, Zhuowen Tu
Date: 2019-06-13 00:00:00
Proceeding: CVPR 2020 6
Tasks: Instance Segmentation, Panoptic Segmentation, Relation, Segmentation, Semantic Segmentation

Panoptic segmentation requires segments of both "things" (countable object instances) and "stuff" (uncountable and amorphous regions) within a single output. A common approach involves the fusion of instance segmentation (for "things") and semantic segmentation (for "stuff") into a non-overlapping placement of segments, and resolves overlaps. However, instance ordering with detection confidence do not correlate well with natural occlusion relationship. To resolve this issue, we propose a branch that is tasked with modeling how two instance masks should overlap one another as a binary relation. Our method, named OCFusion, is lightweight but particularly effective in the instance fusion process. OCFusion is trained with the ground truth relation derived automatically from the existing dataset annotations. We obtain state-of-the-art results on COCO and show competitive results on the Cityscapes panoptic segmentation benchmark.
Learning to Forget for Meta-Learning
Authors: Sungyong Baik, Seokil Hong, Kyoung Mu Lee
Date: 2019-06-13 00:00:00
Proceeding: CVPR 2020 6
Tasks: Few-Shot Learning, Meta-Learning

Few-shot learning is a challenging problem where the goal is to achieve generalization from only few examples. Model-agnostic meta-learning (MAML) tackles the problem by formulating prior knowledge as a common initialization across tasks, which is then used to quickly adapt to unseen tasks. However, forcibly sharing an initialization can lead to conflicts among tasks and the compromised (undesired by tasks) location on optimization landscape, thereby hindering the task adaptation. Further, we observe that the degree of conflict differs among not only tasks but also layers of a neural network. Thus, we propose task-and-layer-wise attenuation on the compromised initialization to reduce its influence. As the attenuation dynamically controls (or selectively forgets) the influence of prior knowledge for a given task and each layer, we name our method as L2F (Learn to Forget). The experimental results demonstrate that the proposed method provides faster adaptation and greatly improves the performance. Furthermore, L2F can be easily applied and improve other state-of-the-art MAML-based frameworks, illustrating its simplicity and generalizability.
Semantics to Space(S2S): Embedding semantics into spatial space for zero-shot verb-object query inferencing
Authors: Sungmin Eum, Heesung Kwon
Date: 2019-06-13 00:00:00
Tasks: Human-Object Interaction Detection, Zero-Shot Learning

We present a novel deep zero-shot learning (ZSL) model for inferencing human-object-interaction with verb-object (VO) query. While the previous two-stream ZSL approaches only use the semantic/textual information to be fed into the query stream, we seek to incorporate and embed the semantics into the visual representation stream as well. Our approach is powered by Semantics-to-Space (S2S) architecture where semantics derived from the residing objects are embedded into a spatial space of the visual stream. This architecture allows the co-capturing of the semantic attributes of the human and the objects along with their location/size/silhouette information. To validate, we have constructed a new dataset, Verb-Transferability 60 (VT60). VT60 provides 60 different VO pairs with overlapping verbs tailored for testing two-stream ZSL approaches with VO query. Experimental evaluations show that our approach not only outperforms the state-of-the-art, but also shows the capability of consistently improving performance regardless of which ZSL baseline architecture is used.
IntrinSeqNet: Learning to Estimate the Reflectance from Varying Illumination
Authors: Grégoire Nieto, Mohammad Rouhani, Philippe Robert
Date: 2019-06-13 00:00:00
Tasks: Intrinsic Image Decomposition

Intrinsic image decomposition describes an image based on its reflectance and shading components. In this paper we tackle the problem of estimating the diffuse reflectance from a sequence of images captured from a fixed viewpoint under various illuminations. To this end we propose a deep learning approach to avoid heuristics and strong assumptions on the reflectance prior. We compare two network architectures: one classic 'U' shaped Convolutional Neural Network (CNN) and a Recurrent Neural Network (RNN) composed of Convolutional Gated Recurrent Units (CGRU). We train our networks on a new dataset specifically designed for the task of intrinsic decomposition from sequences. We test our networks on MIT and BigTime datasets and outperform state-of-the-art algorithms both qualitatively and quantitatively.
Gradient Descent Maximizes the Margin of Homogeneous Neural Networks
Authors: Kaifeng Lyu, Jian Li
Date: 2019-06-13 00:00:00
Proceeding: ICLR 2020 1

In this paper, we study the implicit regularization of the gradient descent algorithm in homogeneous neural networks, including fully-connected and convolutional neural networks with ReLU or LeakyReLU activations. In particular, we study the gradient descent or gradient flow (i.e., gradient descent with infinitesimal step size) optimizing the logistic loss or cross-entropy loss of any homogeneous model (possibly non-smooth), and show that if the training loss decreases below a certain threshold, then we can define a smoothed version of the normalized margin which increases over time. We also formulate a natural constrained optimization problem related to margin maximization, and prove that both the normalized margin and its smoothed version converge to the objective value at a KKT point of the optimization problem. Our results generalize the previous results for logistic regression with one-layer or multi-layer linear networks, and provide more quantitative convergence results with weaker assumptions than previous results for homogeneous smooth neural networks. We conduct several experiments to justify our theoretical finding on MNIST and CIFAR-10 datasets. Finally, as margin is closely related to robustness, we discuss potential benefits of training longer for improving the robustness of the model.
On the Effect of Word Order on Cross-lingual Sentiment Analysis
Authors: Àlex R. Atrio, Toni Badia, Jeremy Barnes
Date: 2019-06-13 00:00:00
Tasks: Cross-Lingual Sentiment Classification, General Classification, Language Modeling, Language Modelling, Sentence, Sentiment Analysis, Sentiment Classification

Current state-of-the-art models for sentiment analysis make use of word order either explicitly by pre-training on a language modeling objective or implicitly by using recurrent neural networks (RNNs) or convolutional networks (CNNs). This is a problem for cross-lingual models that use bilingual embeddings as features, as the difference in word order between source and target languages is not resolved. In this work, we explore reordering as a pre-processing step for sentence-level cross-lingual sentiment classification with two language combinations (English-Spanish, English-Catalan). We find that while reordering helps both models, CNNS are more sensitive to local reorderings, while global reordering benefits RNNs.
Can generalised relative pose estimation solve sparse 3D registration?
Authors: Siddhant Ranade, Xin Yu, Shantnu Kakkar, Pedro Miraldo, Srikumar Ramalingam
Date: 2019-06-13 00:00:00
Tasks: Pose Estimation

Popular 3D scan registration projects, such as Stanford digital Michelangelo or KinectFusion, exploit the high-resolution sensor data for scan alignment. It is particularly challenging to solve the registration of sparse 3D scans in the absence of RGB components. In this case, we can not establish point correspondences since the same 3D point cannot be captured in two successive scans. In contrast to correspondence based methods, we take a different viewpoint and formulate the sparse 3D registration problem based on the constraints from the intersection of line segments from adjacent scans. We obtain the line segments by modeling every horizontal and vertical scan-line as piece-wise linear segments. We propose a new alternating projection algorithm for solving the scan alignment problem using line intersection constraints. We develop two new minimal solvers for scan alignment in the presence of plane correspondences: 1) 3 line intersections and 1 plane correspondence, and 2) 1 line intersection and 2 plane correspondences. We outperform other competing methods on Kinect and LiDAR datasets.
Sentiment analysis is not solved! Assessing and probing sentiment classification
Authors: Jeremy Barnes, Lilja Øvrelid, Erik Velldal
Date: 2019-06-13 00:00:00
Proceeding: WS 2019 8
Tasks: Classification, General Classification, Negation, Sentiment Analysis, Sentiment Classification

Neural methods for SA have led to quantitative improvements over previous approaches, but these advances are not always accompanied with a thorough analysis of the qualitative differences. Therefore, it is not clear what outstanding conceptual challenges for sentiment analysis remain. In this work, we attempt to discover what challenges still prove a problem for sentiment classifiers for English and to provide a challenging dataset. We collect the subset of sentences that an (oracle) ensemble of state-of-the-art sentiment classifiers misclassify and then annotate them for 18 linguistic and paralinguistic phenomena, such as negation, sarcasm, modality, etc. The dataset is available at https://github.com/ltgoslo/assessing_and_probing_sentiment. Finally, we provide a case study that demonstrates the usefulness of the dataset to probe the performance of a given sentiment classifier with respect to linguistic phenomena.
Joint Concept Matching based Learning for Zero-Shot Recognition
Authors: Wen Tang, Ashkan Panahi, Hamid Krim
Date: 2019-06-13 00:00:00
Tasks: Object, Zero-Shot Learning

Zero-shot learning (ZSL) which aims to recognize unseen object classes by only training on seen object classes, has increasingly been of great interest in Machine Learning, and has registered with some successes. Most existing ZSL methods typically learn a projection map between the visual feature space and the semantic space and mainly suffer which is prone to a projection domain shift primarily due to a large domain gap between seen and unseen classes. In this paper, we propose a novel inductive ZSL model based on projecting both visual and semantic features into a common distinct latent space with class-specific knowledge, and on reconstructing both visual and semantic features by such a distinct common space to narrow the domain shift gap. We show that all these constraints on the latent space, class-specific knowledge, reconstruction of features and their combinations enhance the robustness against the projection domain shift problem, and improve the generalization ability to unseen object classes. Comprehensive experiments on four benchmark datasets demonstrate that our proposed method is superior to state-of-the-art algorithms.
Sub-policy Adaptation for Hierarchical Reinforcement Learning
Authors: Alexander C. Li, Carlos Florensa, Ignasi Clavera, Pieter Abbeel
Date: 2019-06-13 00:00:00
Proceeding: ICLR 2020 1
Tasks: Decision Making, Hierarchical Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Hierarchical reinforcement learning is a promising approach to tackle long-horizon decision-making problems with sparse rewards. Unfortunately, most methods still decouple the lower-level skill acquisition process and the training of a higher level that controls the skills in a new task. Leaving the skills fixed can lead to significant sub-optimality in the transfer setting. In this work, we propose a novel algorithm to discover a set of skills, and continuously adapt them along with the higher level even when training on a new task. Our main contributions are two-fold. First, we derive a new hierarchical policy gradient with an unbiased latent-dependent baseline, and we introduce Hierarchical Proximal Policy Optimization (HiPPO), an on-policy method to efficiently train all levels of the hierarchy jointly. Second, we propose a method for training time-abstractions that improves the robustness of the obtained skills to environment changes. Code and results are available at sites.google.com/view/hippo-rl
Curriculum Learning for Cumulative Return Maximization
Authors: Francesco Foglino, Christiano Coletto Christakou, Ricardo Luna Gutierrez, Matteo Leonetti
Date: 2019-06-13 00:00:00
Tasks: Combinatorial Optimization, Reinforcement Learning, Transfer Learning

Curriculum learning has been successfully used in reinforcement learning to accelerate the learning process, through knowledge transfer between tasks of increasing complexity. Critical tasks, in which suboptimal exploratory actions must be minimized, can benefit from curriculum learning, and its ability to shape exploration through transfer. We propose a task sequencing algorithm maximizing the cumulative return, that is, the return obtained by the agent across all the learning episodes. By maximizing the cumulative return, the agent not only aims at achieving high rewards as fast as possible, but also at doing so while limiting suboptimal actions. We experimentally compare our task sequencing algorithm to several popular metaheuristic algorithms for combinatorial optimization, and show that it achieves significantly better performance on the problem of cumulative return maximization. Furthermore, we validate our algorithm on a critical task, optimizing a home controller for a micro energy grid.
Dense Deformation Network for High Resolution Tissue Cleared Image Registration
Authors: Abdullah Nazib, Clinton Fookes, Dimitri Perrin
Date: 2019-06-13 00:00:00
Tasks: Deep Learning, Image Registration, Medical Image Analysis, Medical Image Registration, Vocal Bursts Intensity Prediction

The recent application of deep learning in various areas of medical image analysis has brought excellent performance gains. In particular, technologies based on deep learning in medical image registration can outperform traditional optimisation-based registration algorithms both in registration time and accuracy. However, the U-net based architectures used in most of the image registration frameworks downscale the data, which removes global information and affects the deformation. In this paper, we present a densely connected convolutional architecture for deformable image registration. Our proposed dense network downsizes data only in one stage and have dense connections instead of the skip connections in U-net architecture. The training of the network is unsupervised and does not require ground-truth deformation or any synthetic deformation as a label. The proposed architecture is trained and tested on two different versions of tissue-cleared data, at 10\% and 25\% resolution of the original single-cell-resolution dataset. We demonstrate comparable registration performance to state-of-the-art registration methods and superior performance to the deep-learning based VoxelMorph method in terms of accuracy and increased resolution handling ability. In both resolutions, the proposed DenseDeformation network outperforms VoxelMorph in registration accuracy. Importantly, it can register brains in one minute where conventional methods can take hours at 25\% resolution.
Topic Modeling via Full Dependence Mixtures
Authors: Dan Fisher, Mark Kozdoba, Shie Mannor
Date: 2019-06-13 00:00:00
Proceeding: ICML 2020 1
Tasks: GPU, Stochastic Optimization

In this paper we introduce a new approach to topic modelling that scales to large datasets by using a compact representation of the data and by leveraging the GPU architecture. In this approach, topics are learned directly from the co-occurrence data of the corpus. In particular, we introduce a novel mixture model which we term the Full Dependence Mixture (FDM) model. FDMs model second moment under general generative assumptions on the data. While there is previous work on topic modeling using second moments, we develop a direct stochastic optimization procedure for fitting an FDM with a single Kullback Leibler objective. Moment methods in general have the benefit that an iteration no longer needs to scale with the size of the corpus. Our approach allows us to leverage standard optimizers and GPUs for the problem of topic modeling. In particular, we evaluate the approach on two large datasets, NeurIPS papers and a Twitter corpus, with a large number of topics, and show that the approach performs comparably or better than the the standard benchmarks.