pwc-archive/papers-with-abstracts huggingface dataset:
How Conservative are Language Models? Adapting to the Introduction of Gender-Neutral Pronouns
Authors: Stephanie Brandl, Ruixiang Cui, Anders Søgaard
Date: 2022-04-11 00:00:00
Proceeding: NAACL 2022 7

Gender-neutral pronouns have recently been introduced in many languages to a) include non-binary people and b) as a generic singular. Recent results from psycholinguistics suggest that gender-neutral pronouns (in Swedish) are not associated with human processing difficulties. This, we show, is in sharp contrast with automated processing. We show that gender-neutral pronouns in Danish, English, and Swedish are associated with higher perplexity, more dispersed attention patterns, and worse downstream performance. We argue that such conservativity in language models may limit widespread adoption of gender-neutral pronouns and must therefore be resolved.
D-LinkNet: LinkNet with Pretrained Encoder and Dilated Convolution for High Resolution Satellite Imagery Road Extraction
Authors: Lichen Zhou, Chuang Zhang, Ming Wu
Date: 2018-12-08 00:00:00
Proceeding: CVPR 2018 2018 12
Tasks: Road Segmentation, Semantic Segmentation

Road extraction is a fundamental task in the field of remote sensing which has been a hot research topic in the past
decade. In this paper, we propose a semantic segmentation
neural network, named D-LinkNet, which adopts encoderdecoder structure, dilated convolution and pretrained encoder for road extraction task. The network is built with
LinkNet architecture and has dilated convolution layers in
its center part. Linknet architecture is efficient in computation and memory. Dilation convolution is a powerful tool
that can enlarge the receptive field of feature points without
reducing the resolution of the feature maps. In the CVPR
DeepGlobe 2018 Road Extraction Challenge, our best IoU
scores on the validation set and the test set are 0.6466 and
0.6342 respectively.
Connectionist Temporal Classification: Labelling Unsegmented Sequence Data with Recurrent Neural Networks
Authors: Alex Graves, Santiago Fernández, Faustino Gomez, Jürgen Schmidhuber
Date: 2006-07-25 00:00:00
Proceeding: ICML 2006 2006 7
Tasks: speech-recognition, Speech Recognition

Many real-world sequence learning tasks require the prediction of sequences of labels from noisy, unsegmented input data. In speech recognition, for example, an acoustic signal is transcribed into words or sub-word units. Recurrent neural networks (RNNs) are powerful sequence learners that would seem well suited to such tasks. However, because they require pre-segmented training data, and post-processing to transform their outputs into label sequences, their applicability has so far been limited. This paper presents a novel method for training RNNs to label unsegmented sequences directly, thereby solving both problems. An experiment on the TIMIT speech corpus demonstrates its advantages over both a baseline HMM and a hybrid HMM-RNN.
Knowledge Graph Embedding by Translating on Hyperplanes
Authors: Zhen Wang, Jianwen Zhang, Jianlin Feng, Zheng Chen
Date: 2014-07-27 00:00:00
Proceeding: AAAI 2014 2014 7
Tasks: Graph Embedding, Knowledge Graph Embedding, Link Prediction, Triplet

We deal with embedding a large scale knowledge graph composed of entities and relations into a continuous vector space. TransE is a promising method proposed recently, which is very efficient while achieving state-of-the-art predictive performance. We discuss some mapping properties of relations which should be considered in embedding, such as reflexive, one-to-many, many-to-one, and many-to-many. We note that TransE does not do well in dealing with these properties. Some complex models are capable of preserving these mapping properties but sacrifice efficiency in the process. To make a good trade-off between model capacity and efficiency, in this paper we propose TransH which models a relation as a hyperplane together with a translation operation on it. In this way, we can well preserve the above mapping properties of relations with almost the same model complexity of TransE. Additionally, as a practical knowledge graph is often far from completed, how to construct negative examples to reduce false negative labels in training is very important. Utilizing the one-to-many/many-to-one mapping property of a relation, we propose a simple trick to reduce the possibility of false negative labeling. We conduct extensive experiments on link prediction, triplet classification and fact extraction on benchmark datasets like WordNet and Freebase. Experiments show TransH delivers significant improvements over TransE on predictive accuracy with comparable capability to scale up.
Learning Entity and Relation Embeddings for Knowledge Graph Completion
Authors: Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, Xuan Zhu
Date: 2015-01-25 00:00:00
Proceeding: AAAI 2015 2015 1
Tasks: Knowledge Graph Completion, Knowledge Graph Embeddings, Link Prediction, Relation, Triple Classification

Knowledge graph completion aims to perform link prediction between entities. In this paper, we consider the approach of knowledge graph embeddings. Recently, models such as TransE and TransH build entity and relation embeddings by regarding a relation as translation from head entity to tail entity. We note that these models simply put both entities and relations within the same semantic space. In fact, an entity may have multiple aspects and various relations may focus on different aspects of entities, which makes a common space insufficient for modeling. In this paper, we propose TransR to build entity and relation embeddings in separate entity space and relation spaces. Afterwards, we learn embeddings by first projecting entities from entity space to corresponding relation space and then building translations between projected entities. In experiments, we evaluate our models on three tasks including link prediction, triple classification and relational fact extraction. Experimental results show significant and consistent improvements compared to state-of-the-art baselines including TransE and TransH. The source code of this paper can be obtained from https://github.com/mrlyk423/relation_extraction.
How Do Drivers Allocate Their Potential Attention? Driving Fixation Prediction via Convolutional Neural Networks
Authors: Tao Deng, Hongmei Yan, Long Qin, Thuyen Ngo, and B. S. Manjunath
Date: 2019-05-30 00:00:00
Proceeding: IEEE Transactions on Intelligent Transportation Systems 2019 5
Tasks: object-detection, Object Detection

The traffic driving environment is a complex and dynamic changing scene in which drivers have to pay close attention to salient and important targets or regions for safe driving. Modeling drivers’ eye movements and attention allocation in traffic driving can also help guiding unmanned intelligent vehicles. However, until now, few studies have modeled drivers’ true fixations and allocations while driving. To this end, we collect an eye tracking dataset from a total of 28 experienced drivers viewing 16 traffic driving videos. Based on the multiple drivers’ attention allocation dataset, we propose a convolutional-deconvolutional neural network (CDNN) to predict the drivers’ eye fixations. The experimental results indicate that the proposed CDNN outperforms the state-of-the-art saliency models and predicts drivers’ attentional locations more accurately. The proposed CDNN can predict the major fixation location and shows excellent detection of secondary important information or regions that cannot be ignored during driving if they exist.
Compared with the present object detection models in autonomous and assisted driving systems, our human-like driving model does not detect all of the objects appearing in the driving scenes, but it provides the most
relevant regions or targets, which can largely reduce the interference of irrelevant scene information.
SimAM: A Simple, Parameter-Free Attention Module for Convolutional Neural Networks
Authors: Lingxiao Yang, Ru-Yuan Zhang, Lida Li, Xiaohua Xie
Date: 2022-07-24 00:00:00
Proceeding: Proceedings of Machine Learning Research 2022 7

In this paper, we propose a conceptually simple but very effective attention module for Convolutional Neural Networks (ConvNets). In contrast to existing channel-wise and spatial-wise attention modules, our module instead infers 3-D attention weights for the feature map in a layer without adding parameters to the original networks. Specifically, we base on some well-known neuroscience theories and propose to optimize an energy function to find the importance of each neuron. We further derive a fast closed-form solution for the energy function, and show that the solution can be implemented in less than ten lines of code. Another advantage of the module is that most of the operators are selected based on the solution to the defined energy function, avoiding too many efforts for structure tuning. Quantitative evaluations on various visual tasks demonstrate that the proposed module is flexible and effective to improve the representation ability of many ConvNets. Our code is available at Pytorch-SimAM.
Patch-Wise Spatial-Temporal Quality Enhancement for HEVC Compressed Video
Authors: Qing Ding, Liquan Shen, Liangwei Yu, Hao Yang, Mai Xu
Date: 2021-07-08 00:00:00
Proceeding: journal 2021 7
Tasks: Quantization, Video Enhancement

Recently, many deep learning based researches are conducted to explore the potential quality improvement of compressed videos. These methods mostly utilize either the spatial or temporal information to perform frame-level video enhancement. However, they fail in combining different spatial-temporal information to adaptively utilize adjacent patches to enhance the current patch and achieve limited enhancement performance especially on scene-changing and strong-motion videos. To overcome these limitations, we propose a patch-wise spatial-temporal quality enhancement network which firstly extracts spatial and temporal features, then recalibrates and fuses the obtained spatial and temporal features. Specifically, we design a temporal and spatial-wise attention-based feature distillation structure to adaptively utilize the adjacent patches for distilling patch-wise temporal features. For adaptively enhancing different patch with spatial and temporal information, a channel and spatial-wise attention fusion block is proposed to achieve patch-wise recalibration and fusion of spatial and temporal features. Experimental results demonstrate our network achieves peak signal-to-noise ratio improvement, 0.55 – 0.69 dB compared with the compressed videos at different quantization parameters, outperforming state- of-the-art approaches.
Multi-class probabilistic classification using inductive and cross Venn-Abers predictors
Authors: Valery Manokhin
Date: 2017-06-16 00:00:00
Proceeding: Proceedings of the Sixth Workshop on Conformal and Probabilistic Prediction and Applications, PMLR 60:1-2 2017 6
Tasks: Binary Classification, Classification, Classifier calibration, Conformal Prediction, Prediction

Inductive (IVAP) and cross (CVAP) Venn–Abers predictors are computationally efficient algorithms for probabilistic prediction in binary classification problems. We present a new approach to multi-class probability estimation by turning IVAPs and CVAPs into multi-class probabilistic predictors. The proposed multi-class predictors are experimentally more accurate than both uncalibrated predictors and existing calibration methods.

Unlike other methods such as Platt's scaler and Isotonic Regression Venn-ABERS predictors (being a form of conformal prediction) contain inbuilt mathematical guarantees of validity (lack of bias). In addition Venn-ABERS predictors are multi-output predictors that output two probability predictions of class 1. Such two probability prediction of assigning label 1 for each test objectc can be considered prediction interval. The interval width contains valuable information about degree of certainty of prediction, such information is not available when using other calibration methods such as Platt's and isotonic regression.
A comprehensive survey on video frame interpolation techniques
Authors: Anil Singh Parihar, Disha Varshney, Kshitija Pandya & Ashray Aggarwal
Date: 2021-01-04 00:00:00
Proceeding: The Visual Computer 2021 1
Tasks: Survey, Video Frame Interpolation, Video Restoration

Video frame interpolation is an important area in the computer vision research activities for video post-processing, surveillance, and video restoration tasks. It aims toward increasing the frame rate of a video sequence by calculating intermittent frames between consecutive input frames. This ensures extra smooth, clear motion in order to make animation fluid enough and reduce display motion blur. Advanced deep learning algorithms have the potential to discover knowledge from large-scale diverse video data. These algorithms gain insights about intermediate motion and provide new opportunities to further improve video interpolation technologies. This survey demonstrates a comprehensive overview of about a good number of contributions over past decade pertinent to the latest developments in this domain. The survey paper highlights common challenges in the area of video frame interpolation based on three key aspects: high visual quality, low complexity, and high efficiency of interpolated output from regular videos with the standard frame rate. We scrutinize the architectures, workflows, performance, advantages, and disadvantages and generate a broad categorization along with an overview of experimental results of various state-of-the-art methods executed on benchmark datasets. This survey discusses applications of diverse interpolation frameworks. It provides a backbone reference that inspires future researchers to optimize current techniques on academic and industrial grounds.
Microcontroller based bidirectional buck boost converter for photo voltaic power plant
Authors: Viswanatha V, Venkata Siva Reddy R
Date: 2021-01-23 00:00:00

A common configuration for a stand-alone PV power system may consist of three converters: a buck converter for the PV panelto charge the battery, a boost converter for the battery to discharge to the load and one for the load voltage regulation. Such a systemrequires a coordinated control scheme for three converters which can be complicated. A simple structure for a stand-alone PV plantconsists of a PV array, a battery unit, and its associated bidirectional converter which is a combination of a buck and boost converter.When controlled properly the system can provide uninterrupted power to the load, despite the intermittent availability of sunlight. Inthis paper complete design of the converter is carried out and the simulation has been performed using Psim. From the simulation,the graphs are presented to show the converter working in buck mode and boost mode. Controller is designed to take care of modetransition, buck to boost and boost to buck mode automatically based on source voltage. Hardware implementation has been doneusing microcontroller (8051).
SUES-200: A Multi-height Multi-scene Cross-view Image Benchmark Across Drone and Satellite
Authors: Runzhe Zhu, Ling Yin, Mingze Yang, Fei Wu, Yuncheng Yang, WenBo Hu
Date: 2022-04-22 00:00:00
Tasks: geo-localization

Cross-view image matching aims to match images of the same target scene acquired from different platforms. With the rapid development of drone technology, cross-view matching by neural network models has been a widely accepted choice for drone position or navigation. However, existing public datasets do not include images obtained by drones at different heights, and the types of scenes are relatively homogeneous, which yields issues in assessing a model's capability to adapt to complex and changing scenes. In this end, we present a new cross-view dataset called SUES-200 to address these issues. SUES-200 contains 24120 images acquired by the drone at four different heights and corresponding satellite view images of the same target scene. To the best of our knowledge, SUES-200 is the first public dataset that considers the differences generated in aerial photography captured by drones flying at different heights. In addition, we developed an evaluation for efficient training, testing and evaluation of cross-view matching models, under which we comprehensively analyze the performance of nine architectures. Then, we propose a robust baseline model for use with SUES-200. Experimental results show that SUES-200 can help the model to learn highly discriminative features of the height of the drone.
Exploiting Session Information in BERT-based Session-aware Sequential Recommendation
Authors: Jinseok Seol, Youngrok Ko, Sang-goo Lee
Date: 2022-04-22 00:00:00
Tasks: Recommendation Systems, Sequential Recommendation

In recommendation systems, utilizing the user interaction history as sequential information has resulted in great performance improvement. However, in many online services, user interactions are commonly grouped by sessions that presumably share preferences, which requires a different approach from ordinary sequence representation techniques. To this end, sequence representation models with a hierarchical structure or various viewpoints have been developed but with a rather complex network structure. In this paper, we propose three methods to improve recommendation performance by exploiting session information while minimizing additional parameters in a BERT-based sequential recommendation model: using session tokens, adding session segment embeddings, and a time-aware self-attention. We demonstrate the feasibility of the proposed methods through experiments on widely used recommendation datasets.
Control-NeRF: Editable Feature Volumes for Scene Rendering and Manipulation
Authors: Verica Lazova, Vladimir Guzov, Kyle Olszewski, Sergey Tulyakov, Gerard Pons-Moll
Date: 2022-04-22 00:00:00
Tasks: NeRF, Neural Rendering, Novel View Synthesis

We present a novel method for performing flexible, 3D-aware image content manipulation while enabling high-quality novel view synthesis. While NeRF-based approaches are effective for novel view synthesis, such models memorize the radiance for every point in a scene within a neural network. Since these models are scene-specific and lack a 3D scene representation, classical editing such as shape manipulation, or combining scenes is not possible. Hence, editing and combining NeRF-based scenes has not been demonstrated. With the aim of obtaining interpretable and controllable scene representations, our model couples learnt scene-specific feature volumes with a scene agnostic neural rendering network. With this hybrid representation, we decouple neural rendering from scene-specific geometry and appearance. We can generalize to novel scenes by optimizing only the scene-specific 3D feature representation, while keeping the parameters of the rendering network fixed. The rendering function learnt during the initial training stage can thus be easily applied to new scenes, making our approach more flexible. More importantly, since the feature volumes are independent of the rendering model, we can manipulate and combine scenes by editing their corresponding feature volumes. The edited volume can then be plugged into the rendering model to synthesize high-quality novel views. We demonstrate various scene manipulations, including mixing scenes, deforming objects and inserting objects into scenes, while still producing photo-realistic results.
Self-Supervised Video Object Segmentation via Cutout Prediction and Tagging
Authors: Jyoti Kini, Fahad Shahbaz Khan, Salman Khan, Mubarak Shah
Date: 2022-04-22 00:00:00
Tasks: Object, Segmentation, Semantic Segmentation, TAG, Video Object Segmentation, Video Semantic Segmentation

We propose a novel self-supervised Video Object Segmentation (VOS) approach that strives to achieve better object-background discriminability for accurate object segmentation. Distinct from previous self-supervised VOS methods, our approach is based on a discriminative learning loss formulation that takes into account both object and background information to ensure object-background discriminability, rather than using only object appearance. The discriminative learning loss comprises cutout-based reconstruction (cutout region represents part of a frame, whose pixels are replaced with some constant values) and tag prediction loss terms. The cutout-based reconstruction term utilizes a simple cutout scheme to learn the pixel-wise correspondence between the current and previous frames in order to reconstruct the original current frame with added cutout region in it. The introduced cutout patch guides the model to focus as much on the significant features of the object of interest as the less significant ones, thereby implicitly equipping the model to address occlusion-based scenarios. Next, the tag prediction term encourages object-background separability by grouping tags of all pixels in the cutout region that are similar, while separating them from the tags of the rest of the reconstructed frame pixels. Additionally, we introduce a zoom-in scheme that addresses the problem of small object segmentation by capturing fine structural information at multiple scales. Our proposed approach, termed CT-VOS, achieves state-of-the-art results on two challenging benchmarks: DAVIS-2017 and Youtube-VOS. A detailed ablation showcases the importance of the proposed loss formulation to effectively capture object-background discriminability and the impact of our zoom-in scheme to accurately segment small-sized objects.
Learning to Scaffold: Optimizing Model Explanations for Teaching
Authors: Patrick Fernandes, Marcos Treviso, Danish Pruthi, André F. T. Martins, Graham Neubig
Date: 2022-04-22 00:00:00
Tasks: Meta-Learning, model

Modern machine learning models are opaque, and as a result there is a burgeoning academic subfield on methods that explain these models' behavior. However, what is the precise goal of providing such explanations, and how can we demonstrate that explanations achieve this goal? Some research argues that explanations should help teach a student (either human or machine) to simulate the model being explained, and that the quality of explanations can be measured by the simulation accuracy of students on unexplained examples. In this work, leveraging meta-learning techniques, we extend this idea to improve the quality of the explanations themselves, specifically by optimizing explanations such that student models more effectively learn to simulate the original model. We train models on three natural language processing and computer vision tasks, and find that students trained with explanations extracted with our framework are able to simulate the teacher significantly more effectively than ones produced with previous methods. Through human annotations and a user study, we further find that these learned explanations more closely align with how humans would explain the required decisions in these tasks. Our code is available at https://github.com/coderpat/learning-scaffold