abstract stringlengths 13 4.33k | field sequence | task sequence | method sequence | dataset sequence | metric sequence | title stringlengths 10 194 |
|---|---|---|---|---|---|---|
This paper addresses semantic image segmentation by incorporating rich
information into Markov Random Field (MRF), including high-order relations and
mixture of label contexts. Unlike previous works that optimized MRFs using
iterative algorithm, we solve MRF by proposing a Convolutional Neural Network
(CNN), namely Dee... | [] | [
"Semantic Segmentation"
] | [] | [
"Cityscapes test"
] | [
"Mean IoU (class)"
] | Semantic Image Segmentation via Deep Parsing Network |
We propose the Encoder-Recurrent-Decoder (ERD) model for recognition and
prediction of human body pose in videos and motion capture. The ERD model is a
recurrent neural network that incorporates nonlinear encoder and decoder
networks before and after recurrent layers. We test instantiations of ERD
architectures in the ... | [] | [
"Human Dynamics",
"Human Pose Forecasting",
"Motion Capture",
"Optical Flow Estimation",
"Representation Learning"
] | [] | [
"Human3.6M"
] | [
"MAR, walking, 400ms",
"MAR, walking, 1,000ms"
] | Recurrent Network Models for Human Dynamics |
Zero-shot learning (ZSL) is a challenging problem that aims to recognize the target categories without seen data, where semantic information is leveraged to transfer knowledge from some source classes. Although ZSL has made great progress in recent years, most existing approaches are easy to overfit the sources classes... | [] | [
"Generalized Zero-Shot Learning",
"Transfer Learning",
"Zero-Shot Learning"
] | [] | [
"SUN Attribute",
"CUB-200-2011"
] | [
"average top-1 classification accuracy",
"Harmonic mean"
] | Transferable Contrastive Network for Generalized Zero-Shot Learning |
A novel algorithm to segment a primary object in a video sequence is proposed in this work. First, we generate candidate regions for the primary object using both color and motion edges. Second,we estimate initial primary object regions, by exploiting the recurrence property of the primary object. Third, we augment the... | [] | [
"Semantic Segmentation",
"Unsupervised Video Object Segmentation"
] | [] | [
"DAVIS 2016"
] | [
"F-measure (Decay)",
"Jaccard (Mean)",
"F-measure (Recall)",
"Jaccard (Decay)",
"Jaccard (Recall)",
"F-measure (Mean)",
"J&F"
] | Primary Object Segmentation in Videos Based on Region Augmentation and Reduction |
Numerous models describing the human emotional states have been built by the
psychology community. Alongside, Deep Neural Networks (DNN) are reaching
excellent performances and are becoming interesting features extraction tools
in many computer vision tasks.Inspired by works from the psychology community,
we first stud... | [] | [
"Emotion Recognition",
"Facial Expression Recognition"
] | [] | [
"AffectNet"
] | [
"Accuracy (7 emotion)",
"Accuracy (8 emotion)"
] | CAKE: Compact and Accurate K-dimensional representation of Emotion |
We propose an octree guided neural network architecture and spherical
convolutional kernel for machine learning from arbitrary 3D point clouds. The
network architecture capitalizes on the sparse nature of irregular point
clouds, and hierarchically coarsens the data representation with space
partitioning. At the same ti... | [] | [
"3D Object Classification",
"3D Part Segmentation",
"Object Classification"
] | [] | [
"ShapeNet-Part"
] | [
"Class Average IoU",
"Instance Average IoU"
] | Octree guided CNN with Spherical Kernels for 3D Point Clouds |
Masked Language Model (MLM) framework has been widely adopted for self-supervised language pre-training. In this paper, we argue that randomly sampled masks in MLM would lead to undesirably large gradient variance. Thus, we theoretically quantify the gradient variance via correlating the gradient covariance with the Ha... | [] | [
"Language Modelling",
"Sentence Classification"
] | [] | [
"ACL-ARC"
] | [
"F1"
] | Improving Self-supervised Pre-training via a Fully-Explored Masked Language Model |
Background
Atrial fibrillation (AF) is the most common and debilitating abnormalities of the arrhythmias worldwide, with a major impact on morbidity and mortality. The detection of AF becomes crucial in preventing both acute and chronic cardiac rhythm disorders.
Objective
Our objective is to devise a method for re... | [] | [
"Atrial Fibrillation Detection"
] | [] | [
"MIT-BIH AF"
] | [
"Accuracy"
] | Automatic online detection of atrial fibrillation based on symbolic dynamics and Shannon entropy |
Recent research efforts enable study for natural language grounded navigation in photo-realistic environments, e.g., following natural language instructions or dialog. However, existing methods tend to overfit training data in seen environments and fail to generalize well in previously unseen environments. To close the... | [] | [
"Vision-Language Navigation"
] | [] | [
"VLN Challenge",
"Cooperative Vision-and-Dialogue Navigation"
] | [
"length",
"spl",
"oracle success",
"dist_to_end_reduction",
"success",
"error"
] | Environment-agnostic Multitask Learning for Natural Language Grounded Navigation |
Segmentation is a fundamental task in medical image analysis. However, most existing methods focus on primary region extraction and ignore edge information, which is useful for obtaining accurate segmentation. In this paper, we propose a generic medical segmentation method, called Edge-aTtention guidance Network (ET-Ne... | [] | [
"Medical Image Segmentation",
"Optic Disc Segmentation",
"Semantic Segmentation"
] | [] | [
"Montgomery County",
"LUNA",
"DRIVE"
] | [
"mIoU",
"Accuracy"
] | ET-Net: A Generic Edge-aTtention Guidance Network for Medical Image Segmentation |
We present a transition-based AMR parser that directly generates AMR parses
from plain text. We use Stack-LSTMs to represent our parser state and make
decisions greedily. In our experiments, we show that our parser achieves very
competitive scores on English using only AMR training data. Adding additional
information, ... | [] | [
"AMR Parsing"
] | [] | [
"LDC2014T12"
] | [
"F1 Newswire",
"F1 Full"
] | AMR Parsing using Stack-LSTMs |
Neural architecture search (NAS) has shown promising results discovering models that are both accurate and fast. For NAS, training a one-shot model has become a popular strategy to rank the relative quality of different architectures (child models) using a single set of shared weights. However, while one-shot model wei... | [] | [
"Neural Architecture Search"
] | [] | [
"ImageNet"
] | [
"Top-1 Error Rate",
"MACs",
"Params",
"Accuracy"
] | BigNAS: Scaling Up Neural Architecture Search with Big Single-Stage Models |
Estimating individual level treatment effects (ITE) from observational data is a challenging and important area in causal machine learning and is commonly considered in diverse mission-critical applications. In this paper, we propose an information theoretic approach in order to find more reliable representations for e... | [] | [
"Causal Inference"
] | [] | [
"IDHP"
] | [
"Average Treatment Effect Error"
] | Reliable Estimation of Individual Treatment Effect with Causal Information Bottleneck |
Accurate segmenting nuclei instances is a crucial step in computer-aided
image analysis to extract rich features for cellular estimation and following
diagnosis as well as treatment. While it still remains challenging because the
wide existence of nuclei clusters, along with the large morphological variances
among diff... | [] | [
"Instance Segmentation",
"Multi-tissue Nucleus Segmentation",
"Semantic Segmentation"
] | [] | [
"Kumar"
] | [
"Hausdorff Distance (mm)",
"Dice"
] | CIA-Net: Robust Nuclei Instance Segmentation with Contour-aware Information Aggregation |
Domain adaptation (DA) and domain generalization (DG) have emerged as a solution to the domain shift problem where the distribution of the source and target data is different. The task of DG is more challenging than DA as the target data is totally unseen during the training phase in DG scenarios. The current state-of-... | [] | [
"Domain Adaptation",
"Domain Generalization"
] | [] | [
"Office-31",
"Office-Home",
"ImageCLEF-DA"
] | [
"Average Accuracy",
"Accuracy"
] | Correlation-aware Adversarial Domain Adaptation and Generalization |
In this paper, we analyze neural network-based dialogue systems trained in an end-to-end manner using an updated version of the recent Ubuntu Dialogue Corpus, a dataset containing almost 1 million multi-turn dialogues, with a total of over 7 million utterances and 100 million words. This dataset is interesting because ... | [] | [
"Conversation Disentanglement",
"Feature Engineering"
] | [] | [
"Linux IRC (Ch2 Elsner)",
"Linux IRC (Ch2 Kummerfeld)"
] | [
"1-1",
"Shen F-1",
"Local"
] | Training End-to-End Dialogue Systems with the Ubuntu Dialogue Corpus |
Object segmentation and structure localization are important steps in
automated image analysis pipelines for microscopy images. We present a
convolution neural network (CNN) based deep learning architecture for
segmentation of objects in microscopy images. The proposed network can be used
to segment cells, nuclei and g... | [] | [
"Multi-tissue Nucleus Segmentation",
"Semantic Segmentation"
] | [] | [
"Kumar"
] | [
"Hausdorff Distance (mm)",
"Dice"
] | Micro-Net: A unified model for segmentation of various objects in microscopy images |
We present our system for the WNUT 2017 Named Entity Recognition challenge on Twitter data. We describe two modifications of a basic neural network architecture for sequence tagging. First, we show how we exploit additional labeled data, where the Named Entity tags differ from the target task. Then, we propose a way to... | [] | [
"Named Entity Recognition",
"Transfer Learning"
] | [] | [
"Long-tail emerging entities"
] | [
"F1 (surface form)",
"F1"
] | Transfer Learning and Sentence Level Features for Named Entity Recognition on Tweets |
Recently, emotion detection in conversations becomes a hot research topic in the Natural Language Processing community. In this paper, we focus on emotion detection in multi-speaker conversations instead of traditional two-speaker conversations in existing studies. Different from non-conversation text, emotion detectio... | [] | [
"Emotion Recognition in Conversation"
] | [] | [
"MELD"
] | [
"Weighted Macro-F1"
] | Modeling both context- and speaker-sensitive dependence for emotion detection in multi-speaker conversations |
Self-training is a competitive approach in domain adaptive segmentation, which trains the network with the pseudo labels on the target domain. However inevitably, the pseudo labels are noisy and the target features are dispersed due to the discrepancy between source and target domains. In this paper, we rely on represe... | [] | [
"Domain Adaptation",
"Image-to-Image Translation",
"Semantic Segmentation",
"Synthetic-to-Real Translation",
"Unsupervised Domain Adaptation"
] | [] | [
"GTA5 to Cityscapes",
"GTAV-to-Cityscapes Labels",
"SYNTHIA-to-Cityscapes"
] | [
"mIoU (13 classes)",
"mIoU"
] | Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentation |
Deep learning based general language models have achieved state-of-the-art results in many popular tasks such as sentiment analysis and QA tasks. Text in domains like social media has its own salient characteristics. Domain knowledge should be helpful in domain relevant tasks. In this work, we devise a simple method to... | [] | [
"Emotion Classification",
"Language Modelling",
"Sentiment Analysis"
] | [] | [
"SemEval 2018 Task 1E-c"
] | [
"Micro-F1",
"Macro-F1",
"Accuracy"
] | Improving Multi-label Emotion Classification by Integrating both General and Domain-specific Knowledge |
With the rise in popularity of machine and deep learning models, there is an increased focus on their vulnerability to malicious inputs. These adversarial examples drift model predictions away from the original intent of the network and are a growing concern in practical security. In order to combat these attacks, neur... | [] | [
"Adversarial Attack",
"Music Genre Recognition"
] | [] | [
"1B Words"
] | [
"10 Hops"
] | SAD: Saliency-based Defenses Against Adversarial Examples |
3D semantic scene labeling is fundamental to agents operating in the real
world. In particular, labeling raw 3D point sets from sensors provides
fine-grained semantics. Recent works leverage the capabilities of Neural
Networks (NNs), but are limited to coarse voxel predictions and do not
explicitly enforce global consi... | [] | [
"Semantic Segmentation"
] | [] | [
"Semantic3D",
"S3DIS Area5"
] | [
"mAcc",
"mIoU"
] | SEGCloud: Semantic Segmentation of 3D Point Clouds |
This paper addresses the problem of 3D human pose estimation from a single
image. We follow a standard two-step pipeline by first detecting the 2D
position of the $N$ body joints, and then using these observations to infer 3D
pose. For the first step, we use a recent CNN-based detector. For the second
step, most existi... | [] | [
"3D Human Pose Estimation",
"Pose Estimation",
"Regression"
] | [] | [
"HumanEva-I"
] | [
"Mean Reconstruction Error (mm)"
] | 3D Human Pose Estimation from a Single Image via Distance Matrix Regression |
Faster RCNN has achieved great success for generic object detection including
PASCAL object detection and MS COCO object detection. In this report, we
propose a detailed designed Faster RCNN method named FDNet1.0 for face
detection. Several techniques were employed including multi-scale training,
multi-scale testing, l... | [] | [
"Face Detection",
"Object Detection"
] | [] | [
"WIDER Face (Hard)",
"WIDER Face (Medium)",
"WIDER Face (Easy)"
] | [
"AP"
] | Face Detection Using Improved Faster RCNN |
Point cloud data from 3D LiDAR sensors are one of the most crucial sensor modalities for versatile safety-critical applications such as self-driving vehicles. Since the annotations of point cloud data is an expensive and time-consuming process, therefore recently the utilisation of simulated environments and 3D LiDAR s... | [] | [
"Domain Adaptation",
"Unsupervised Domain Adaptation"
] | [] | [
"PreSIL to KITTI"
] | [
"AP@0.7"
] | Domain Adaptation for Vehicle Detection from Bird's Eye View LiDAR Point Cloud Data |
Emotion recognition in conversations (ERC) has received much attention recently in the natural language processing community. Considering that the emotions of the utterances in conversations are interactive, previous works usually implicitly model the emotion interaction between utterances by modeling dialogue context,... | [] | [
"Emotion Recognition",
"Emotion Recognition in Conversation"
] | [] | [
"IEMOCAP",
"MELD"
] | [
"Weighted Macro-F1",
"F1"
] | An Iterative Emotion Interaction Network for Emotion Recognition in Conversations |
Three-dimensional objects are commonly represented as 3D boxes in a point-cloud. This representation mimics the well-studied image-based 2D bounding-box detection but comes with additional challenges. Objects in a 3D world do not follow any particular orientation, and box-based detectors have difficulties enumerating a... | [] | [
"3D Multi-Object Tracking",
"3D Object Detection",
"3D Object Tracking",
"Object Detection",
"Object Tracking"
] | [] | [
"waymo pedestrian",
"waymo cyclist",
"nuScenes",
"waymo all_ns"
] | [
"mAAE",
"mAP",
"APH/L2",
"mAVE",
"mASE",
"mAOE",
"NDS",
"amota",
"mATE"
] | Center-based 3D Object Detection and Tracking |
This paper revisits the bilinear attention networks in the visual question answering task from a graph perspective. The classical bilinear attention networks build a bilinear attention map to extract the joint representation of words in the question and objects in the image but lack fully exploring the relationship bet... | [] | [
"Question Answering",
"Visual Question Answering"
] | [] | [
"VQA v2 test-std",
"GQA Test2019"
] | [
"Binary",
"number",
"overall",
"other",
"Validity",
"Consistency",
"Plausibility",
"Distribution",
"yes/no",
"Accuracy",
"Open"
] | Bilinear Graph Networks for Visual Question Answering |
There is a natural correlation between the visual and auditive elements of a
video. In this work we leverage this connection to learn general and effective
models for both audio and video analysis from self-supervised temporal
synchronization. We demonstrate that a calibrated curriculum learning scheme, a
careful choic... | [] | [
"Action Recognition",
"Audio Classification",
"Curriculum Learning",
"Temporal Action Localization"
] | [] | [
"ESC-50"
] | [
"Top-1 Accuracy"
] | Cooperative Learning of Audio and Video Models from Self-Supervised Synchronization |
Obtaining large, human labelled speech datasets to train models for emotion
recognition is a notoriously challenging task, hindered by annotation cost and
label ambiguity. In this work, we consider the task of learning embeddings for
speech classification without access to any form of labelled audio. We base our
approa... | [] | [
"Emotion Recognition",
"Facial Expression Recognition",
"Speech Emotion Recognition"
] | [] | [
"FERPlus"
] | [
"Accuracy"
] | Emotion Recognition in Speech using Cross-Modal Transfer in the Wild |
Conversational Emotion Recognition (CER) is a crucial task in Natural Language Processing (NLP) with wide applications. Prior works in CER generally focus on modeling emotion influences solely with utterance-level features, with little attention paid on phrase-level semantic connection between utterances. Phrases carry... | [] | [
"Emotion Recognition",
"Emotion Recognition in Conversation"
] | [] | [
"IEMOCAP",
"MELD"
] | [
"Weighted Macro-F1",
"F1",
"Accuracy"
] | Summarize before Aggregate: A Global-to-local Heterogeneous Graph Inference Network for Conversational Emotion Recognition |
Temporal coherence is a valuable source of information in the context of
optical flow estimation. However, finding a suitable motion model to leverage
this information is a non-trivial task. In this paper we propose an
unsupervised online learning approach based on a convolutional neural network
(CNN) that estimates su... | [] | [
"Optical Flow Estimation"
] | [] | [
"Sintel-clean"
] | [
"Average End-Point Error"
] | ProFlow: Learning to Predict Optical Flow |
Information selection is the most important component in document summarization task. In this paper, we propose to extend the basic neural encoding-decoding framework with an information selection layer to explicitly model and optimize the information selection process in abstractive document summarization. Specificall... | [] | [
"Abstractive Text Summarization",
"Document Summarization",
"Machine Translation",
"Text Generation"
] | [] | [
"CNN / Daily Mail"
] | [
"ROUGE-L",
"ROUGE-1",
"ROUGE-2"
] | Improving Neural Abstractive Document Summarization with Explicit Information Selection Modeling |
Recent neural sequence-to-sequence models have shown significant progress on short text summarization. However, for document summarization, they fail to capture the long-term structure of both documents and multi-sentence summaries, resulting in information loss and repetitions. In this paper, we propose to leverage th... | [] | [
"Abstractive Text Summarization",
"Document Summarization",
"Machine Translation",
"Sentence Summarization",
"Text Generation",
"Text Summarization"
] | [] | [
"CNN / Daily Mail"
] | [
"ROUGE-L",
"ROUGE-1",
"ROUGE-2"
] | Improving Neural Abstractive Document Summarization with Structural Regularization |
Existing named entity recognition (NER) systems rely on large amounts of human-labeled data for supervision. However, obtaining large-scale annotated data is challenging particularly in specific domains like health-care, e-commerce and so on. Given the availability of domain specific knowledge resources, (e.g., ontolog... | [] | [
"Denoising",
"Named Entity Recognition"
] | [] | [
"BC5CDR"
] | [
"F1"
] | Reinforcement-based denoising of distantly supervised NER with partial annotation |
Recently, anchor-free detection methods have been through great progress. The major two families, anchor-point detection and key-point detection, are at opposite edges of the speed-accuracy trade-off, with anchor-point detectors having the speed advantage. In this work, we boost the performance of the anchor-point dete... | [] | [
"Feature Selection",
"Object Detection"
] | [] | [
"COCO test-dev"
] | [
"APM",
"box AP",
"AP75",
"APS",
"APL",
"AP50"
] | Soft Anchor-Point Object Detection |
This work studies the problem of object goal navigation which involves navigating to an instance of the given object category in unseen environments. End-to-end learning-based navigation methods struggle at this task as they are ineffective at exploration and long-term planning. We propose a modular system called, `Goa... | [] | [
"Robot Navigation"
] | [] | [
"Habitat 2020 Object Nav test-std"
] | [
"SOFT_SPL",
"DISTANCE_TO_GOAL",
"SUCCESS",
"SPL"
] | Object Goal Navigation using Goal-Oriented Semantic Exploration |
Depth estimation provides essential information to perform autonomous driving
and driver assistance. Especially, Monocular Depth Estimation is interesting
from a practical point of view, since using a single camera is cheaper than
many other options and avoids the need for continuous calibration strategies as
required ... | [] | [
"Autonomous Driving",
"Depth Estimation",
"Monocular Depth Estimation",
"Semantic Segmentation"
] | [] | [
"KITTI Eigen split"
] | [
"absolute relative error"
] | Monocular Depth Estimation by Learning from Heterogeneous Datasets |
Progress in Sentence Simplification has been hindered by the lack of supervised data, particularly in languages other than English. Previous work has aligned sentences from original and simplified corpora such as English Wikipedia and Simple English Wikipedia, but this limits corpus size, domain, and language. In this ... | [] | [
"Text Simplification"
] | [] | [
"ASSET",
"TurkCorpus"
] | [
"BLEU",
"SARI (EASSE>=0.2.1)"
] | Multilingual Unsupervised Sentence Simplification |
We introduce GEM, a living benchmark for natural language Generation (NLG), its Evaluation, and Metrics. Measuring progress in NLG relies on a constantly evolving ecosystem of automated metrics, datasets, and human evaluation standards. However, due to this moving target, new models often still evaluate on divergent an... | [] | [
"Abstractive Text Summarization",
"Cross-Lingual Abstractive Summarization",
"Data-to-Text Generation",
"Extreme Summarization",
"Question Answering",
"Task-Oriented Dialogue Systems",
"Text Generation",
"Text Simplification"
] | [] | [
"SGD",
"Cleaned E2E NLG Challenge",
"WebNLG en",
"WebNLG ru",
"MLSUM de",
"MLSUM es",
"ASSET",
"Czech restaurant information",
"TurkCorpus",
"CommonGen",
"DART",
"ToTTo"
] | [
"METEOR"
] | The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics |
The intensive annotation cost and the rich but unlabeled data contained in videos motivate us to propose an unsupervised video-based person re-identification (re-ID) method. We start from two assumptions: 1) different video tracklets typically contain different persons, given that the tracklets are taken at distinct pl... | [] | [
"Person Re-Identification",
"Video-Based Person Re-Identification"
] | [] | [
"PRID2011"
] | [
"Rank-1",
"Rank-20",
"Rank-5"
] | Stepwise Metric Promotion for Unsupervised Video Person Re-Identification |
Sentence simplification aims to simplify the content and structure of complex
sentences, and thus make them easier to interpret for human readers, and easier
to process for downstream NLP applications. Recent advances in neural machine
translation have paved the way for novel approaches to the task. In this paper,
we a... | [] | [
"Machine Translation",
"Text Simplification"
] | [] | [
"PWKP / WikiSmall",
"Newsela",
"TurkCorpus"
] | [
"BLEU",
"SARI (EASSE>=0.2.1)",
"SARI"
] | Sentence Simplification with Memory-Augmented Neural Networks |
Sentence simplification aims to improve readability and understandability,
based on several operations such as splitting, deletion, and paraphrasing.
However, a valid simplified sentence should also be logically entailed by its
input sentence. In this work, we first present a strong pointer-copy mechanism
based sequenc... | [] | [
"Multi-Task Learning",
"Paraphrase Generation",
"Text Simplification"
] | [] | [
"PWKP / WikiSmall",
"Newsela",
"TurkCorpus"
] | [
"BLEU",
"SARI (EASSE>=0.2.1)",
"SARI"
] | Dynamic Multi-Level Multi-Task Learning for Sentence Simplification |
Multi-person 3D human pose estimation from a single image is a challenging problem, especially for in-the-wild settings due to the lack of 3D annotated data. We propose HG-RCNN, a Mask-RCNN based network that also leverages the benefits of the Hourglass architecture for multi-person 3D Human Pose Estimation. A two-stag... | [] | [
"3D Human Pose Estimation",
"Pose Estimation"
] | [] | [
"MuPoTS-3D"
] | [
"3DPCK"
] | Multi-Person 3D Human Pose Estimation from Monocular Images |
Monocular depth estimation is a challenging task in scene understanding, with the goal to acquire the geometric properties of 3D space from 2D images. Due to the lack of RGB-depth image pairs, unsupervised learning methods aim at deriving depth information with alternative supervision such as stereo pairs. However, mos... | [] | [
"Depth Estimation",
"Monocular Depth Estimation",
"Scene Understanding"
] | [] | [
"KITTI Eigen split"
] | [
"absolute relative error"
] | Towards Scene Understanding: Unsupervised Monocular Depth Estimation With Semantic-Aware Representation |
The key of Weakly Supervised Fine-grained Image Classification (WFGIC) is how to pick out the discriminative regions and learn the discriminative features from them. However, most recent WFGIC methods pick out the discriminative regions independently and utilize their features directly, while neglecting the facts that ... | [] | [
"Fine-Grained Image Classification",
"Image Classification"
] | [] | [
" CUB-200-2011",
"Stanford Cars",
"FGVC Aircraft"
] | [
"Accuracy"
] | Graph-propagation based Correlation Learning for Weakly Supervised Fine-grained Image Classification |
Unsupervised domain adaptation (UDA) transfers knowledge from a label-rich source domain to a fully-unlabeled target domain. To tackle this task, recent approaches resort to discriminative domain transfer in virtue of pseudo-labels to enforce the class-level distribution alignment across the source and target domains. ... | [] | [
"Domain Adaptation",
"Unsupervised Domain Adaptation"
] | [] | [
"SVHN-to-MNIST"
] | [
"Accuracy"
] | Progressive Feature Alignment for Unsupervised Domain Adaptation |
We present an efficient method for detecting anomalies in videos. Recent
applications of convolutional neural networks have shown promises of
convolutional layers for object detection and recognition, especially in
images. However, convolutional neural networks are supervised and require
labels as learning signals. We ... | [] | [
"Anomaly Detection",
"Object Detection"
] | [] | [
"UBI-Fights"
] | [
"AUC"
] | Abnormal Event Detection in Videos using Spatiotemporal Autoencoder |
Current methods for training convolutional neural networks depend on large amounts of labeled samples for supervised training. In this paper we present an approach for training a convolutional neural network using only unlabeled data. We train the network to discriminate between a set of surrogate classes. Each surroga... | [] | [
"Image Classification",
"Object Recognition"
] | [] | [
"STL-10",
"CIFAR-10"
] | [
"Percentage correct"
] | Discriminative Unsupervised Feature Learning with Convolutional Neural Networks |
PyOD is an open-source Python toolbox for performing scalable outlier detection on multivariate data. Uniquely, it provides access to a wide range of outlier detection algorithms, including established outlier ensembles and more recent neural network-based approaches, under a single, well-documented API designed for us... | [] | [
"Anomaly Detection",
"Outlier Detection",
"outlier ensembles"
] | [] | [] | [] | PyOD: A Python Toolbox for Scalable Outlier Detection |
Sentiment analysis (SA) is one of the most useful natural language processing applications. Literature is flooding with many papers and systems addressing this task, but most of the work is focused on English. In this paper, we present {``}Mazajak{''}, an online system for Arabic SA. The system is based on a deep learn... | [] | [
"Arabic Sentiment Analysis",
"Sentiment Analysis",
"Twitter Sentiment Analysis"
] | [] | [
"ArSAS",
"SemEval 2017 Task 4-A",
"ASTD"
] | [
"Average Recall"
] | Mazajak: An Online Arabic Sentiment Analyser |
In the absence of large labelled datasets, self-supervised learning techniques
can boost performance by learning useful representations from unlabelled data,
which is often more readily available. However, there is often a domain shift
between the unlabelled collection and the downstream target problem data. We
sho... | [] | [
"Image Classification",
"Meta-Learning",
"Self-Supervised Learning"
] | [] | [
"STL-10"
] | [
"Percentage correct"
] | Don’t Wait, Just Weight: Improving Unsupervised Representations by Learning Goal-Driven Instance Weights |
Understanding a narrative requires reading between the lines and reasoning
about the unspoken but obvious implications about events and people's mental
states - a capability that is trivial for humans but remarkably hard for
machines. To facilitate research addressing this challenge, we introduce a new
annotation frame... | [] | [
"Emotion Classification"
] | [] | [
"ROCStories"
] | [
"F1"
] | Modeling Naive Psychology of Characters in Simple Commonsense Stories |
In active learning, sampling bias could pose a serious inconsistency problem and hinder the algorithm from finding the optimal hypothesis. However, many methods for neural networks are hypothesis space agnostic and do not address this problem. We examine active learning with convolutional neural networks through the pr... | [] | [
"Active Learning",
"Image Classification"
] | [] | [
"STL-10"
] | [
"Percentage correct"
] | Effective Version Space Reduction for Convolutional Neural Networks |
Recently, skeleton based action recognition gains more popularity due to
cost-effective depth sensors coupled with real-time skeleton estimation
algorithms. Traditional approaches based on handcrafted features are limited to
represent the complexity of motion patterns. Recent methods that use Recurrent
Neural Networks ... | [] | [
"3D Action Recognition",
"Action Recognition",
"Data Augmentation",
"Skeleton Based Action Recognition",
"Temporal Action Localization"
] | [] | [
"NTU RGB+D"
] | [
"Accuracy (CS)",
"Accuracy (CV)"
] | Modeling Temporal Dynamics and Spatial Configurations of Actions Using Two-Stream Recurrent Neural Networks |
We address the unsupervised learning of several interconnected problems in
low-level vision: single view depth prediction, camera motion estimation,
optical flow, and segmentation of a video into the static scene and moving
regions. Our key insight is that these four fundamental vision problems are
coupled through geom... | [] | [
"Depth Estimation",
"Monocular Depth Estimation",
"Motion Estimation",
"Motion Segmentation",
"Optical Flow Estimation"
] | [] | [
"KITTI Eigen split"
] | [
"absolute relative error"
] | Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation |
In this paper, we study abstractive summarization for open-domain videos. Unlike the traditional text news summarization, the goal is less to "compress" text information but rather to provide a fluent textual summary of information that has been collected and fused from different source modalities, in our case video an... | [] | [
"Abstractive Text Summarization",
"Text Summarization"
] | [] | [
"How2"
] | [
"ROUGE-L",
"Content F1"
] | Multimodal Abstractive Summarization for How2 Videos |
Text-to-image retrieval is an essential task in multi-modal information retrieval, i.e. retrieving relevant images from a large and unlabelled image dataset given textual queries. In this paper, we propose VisualSparta, a novel text-to-image retrieval model that shows substantial improvement over existing models on bot... | [] | [
"Cross-Modal Retrieval",
"Image Retrieval",
"Information Retrieval",
"Text-Image Retrieval",
"Text-to-Image Retrieval"
] | [] | [
"MSCOCO-1k",
"COCO 2014",
"Flickr30k",
"Flickr30K 1K test"
] | [
"recall@5",
"recall@10",
"QPS",
"recall@1",
"R@10",
"Text-to-image R@10",
"Text-to-image R@1",
"R@5",
"R@1",
"Text-to-image R@5"
] | VisualSparta: Sparse Transformer Fragment-level Matching for Large-scale Text-to-Image Search |
(Unsupervised) Domain Adaptation (DA) seeks for classifying target instances when solely provided with source labeled and target unlabeled examples for training. Learning domain-invariant features helps to achieve this goal, whereas it underpins unlabeled samples drawn from a single or multiple explicit target domains ... | [] | [
"Domain Adaptation",
"Transfer Learning",
"Unsupervised Domain Adaptation"
] | [] | [
"Office-31",
"Office-Home"
] | [
"Accuracy"
] | Blending-target Domain Adaptation by Adversarial Meta-Adaptation Networks |
This work considers the problem of unsupervised domain adaptation in person re-identification (re-ID), which aims to transfer knowledge from the source domain to the target domain. Existing methods are primary to reduce the inter-domain shift between the domains, which however usually overlook the relations among targe... | [] | [
"Domain Adaptation",
"Person Re-Identification",
"Unsupervised Domain Adaptation"
] | [] | [
"Duke to Market",
"Duke to MSMT",
"Market to Duke",
"Market to MSMT"
] | [
"rank-10",
"mAP",
"rank-5",
"rank-1"
] | Learning to Adapt Invariance in Memory for Person Re-identification |
In Multi-Label Text Classification (MLTC), one sample can belong to more than one class. It is observed that most MLTC tasks, there are dependencies or correlations among labels. Existing methods tend to ignore the relationship among labels. In this paper, a graph attention network-based model is proposed to capture th... | [] | [
"Document Classification",
"Graph Representation Learning",
"Multi-Label Text Classification",
"Text Classification"
] | [] | [
"Slashdot",
"RCV1-v2",
"Reuters-21578",
"RCV1",
"AAPD"
] | [
"Micro-F1",
"F1",
"Micro F1"
] | MAGNET: Multi-Label Text Classification using Attention-based Graph Neural Network |
Document-level Relation Extraction (RE) requires extracting relations expressed within and across sentences. Recent works show that graph-based methods, usually constructing a document-level graph that captures document-aware interactions, can obtain useful entity representations thus helping tackle document-level RE. ... | [] | [
"Relation Extraction"
] | [] | [
"DocRED"
] | [
"Ign F1",
"F1"
] | Coarse-to-Fine Entity Representations for Document-level Relation Extraction |
A fundamental trade-off between effectiveness and efficiency needs to be
balanced when designing an online question answering system. Effectiveness
comes from sophisticated functions such as extractive machine reading
comprehension (MRC), while efficiency is obtained from improvements in
preliminary retrieval component... | [] | [
"Machine Reading Comprehension",
"Question Answering",
"Reading Comprehension"
] | [] | [
"MS MARCO"
] | [
"Rouge-L",
"BLEU-1"
] | A Deep Cascade Model for Multi-Document Reading Comprehension |
Recent advances in video super-resolution have shown that convolutional
neural networks combined with motion compensation are able to merge information
from multiple low-resolution (LR) frames to generate high-quality images.
Current state-of-the-art methods process a batch of LR frames to generate a
single high-resolu... | [] | [
"Motion Compensation",
"Multi-Frame Super-Resolution",
"Super-Resolution",
"Video Super-Resolution"
] | [] | [
"Vid4 - 4x upscaling"
] | [
"SSIM",
"PSNR"
] | Frame-Recurrent Video Super-Resolution |
We present the first dataset targeted at end-to-end NLG in Czech in the restaurant domain, along with several strong baseline models using the sequence-to-sequence approach. While non-English NLG is under-explored in general, Czech, as a morphologically rich language, makes the task even harder: Since Czech requires in... | [] | [
"Data-to-Text Generation",
"Language Modelling"
] | [] | [
"Czech Restaurant NLG"
] | [
"CIDER",
"BLEU score",
"METEOR",
"NIST"
] | Neural Generation for Czech: Data and Baselines |
Neural models of dialog rely on generalized latent representations of language. This paper introduces a novel training procedure which explicitly learns multiple representations of language at several levels of granularity. The multi-granularity training algorithm modifies the mechanism by which negative candidate resp... | [] | [
"Conversational Response Selection"
] | [] | [
"Ubuntu Dialogue (v1, Ranking)"
] | [
"R10@1",
"R2@1"
] | Multi-Granularity Representations of Dialog |
Generative adversarial networks (GANs) have great successes on synthesizing
data. However, the existing GANs restrict the discriminator to be a binary
classifier, and thus limit their learning capacity for tasks that need to
synthesize output with rich structures such as natural language descriptions.
In this paper, we... | [] | [
"Text Generation"
] | [] | [
"Chinese Poems",
"EMNLP2017 WMT",
"COCO Captions"
] | [
"BLEU-3",
"BLEU-4",
"BLEU-2",
"BLEU-5"
] | Adversarial Ranking for Language Generation |
We achieve 3D semantic scene labeling by exploring semantic relation between each point and its contextual neighbors through edges. Besides an encoder-decoder branch for predicting point labels, we construct an edge branch to hierarchically integrate point features and generate edge features. To incorporate point featu... | [] | [
"Scene Labeling",
"Semantic Segmentation"
] | [] | [
"S3DIS Area5"
] | [
"oAcc",
"mAcc",
"mIoU"
] | Hierarchical Point-Edge Interaction Network for Point Cloud Semantic Segmentation |
Head pose estimation, which computes the intrinsic Euler angles (yaw, pitch, roll) from the human, is crucial for gaze estimation, face alignment, and 3D reconstruction. Traditional approaches heavily relies on the accuracy of facial landmarks. It limits their performances, especially when the visibility of the face is... | [] | [
"3D Reconstruction",
"Face Alignment",
"Gaze Estimation",
"Head Pose Estimation",
"Pose Estimation",
"Quantization",
"Regression"
] | [] | [
"AFLW2000",
"AFLW",
"BIWI"
] | [
"MAE",
"MAE (trained with BIWI data)"
] | Hybrid coarse-fine classification for head pose estimation |
Face alignment, which fits a face model to an image and extracts the semantic
meanings of facial pixels, has been an important topic in CV community.
However, most algorithms are designed for faces in small to medium poses (below
45 degree), lacking the ability to align faces in large poses up to 90 degree.
The challen... | [] | [
"3D Face Reconstruction",
"Face Alignment",
"Face Model",
"Head Pose Estimation"
] | [] | [
"AFLW2000",
"300W",
"Florence",
"AFLW2000-3D",
"BIWI"
] | [
"Error rate",
"NME",
"MAE (trained with other data)",
"MAE",
"Mean NME "
] | Face Alignment Across Large Poses: A 3D Solution |
In this paper, we present Deep Graph Kernels (DGK), a unified framework to learn latent representations of sub-structures for graphs, inspired by latest advancements in language modeling and deep learning. Our framework leverages the dependency information between sub-structures by learning their latent representations... | [] | [
"Graph Classification",
"Language Modelling"
] | [] | [
"COLLAB",
"RE-M12K",
"IMDb-B",
"ENZYMES",
"Android Malware Dataset",
"PROTEINS",
"D&D",
"NCI1",
"MUTAG",
"IMDb-M",
"RE-M5K"
] | [
"Accuracy"
] | Deep Graph Kernels |
Can neural networks learn to compare graphs without feature engineering? In
this paper, we show that it is possible to learn representations for graph
similarity with neither domain knowledge nor supervision (i.e.\ feature
engineering or labeled graphs). We propose Deep Divergence Graph Kernels, an
unsupervised method ... | [] | [
"Feature Engineering",
"Graph Classification",
"Graph Similarity"
] | [] | [
"MUTAG",
"D&D",
"PTC",
"NCI1"
] | [
"Accuracy"
] | DDGK: Learning Graph Representations for Deep Divergence Graph Kernels |
Visual question answering (VQA) and image captioning require a shared body of general knowledge connecting language and vision. We present a novel approach to improve VQA performance that exploits this connection by jointly generating captions that are targeted to help answer a specific visual question. The model is tr... | [] | [
"Image Captioning",
"Question Answering",
"Visual Question Answering"
] | [] | [
"VQA v2 test-std"
] | [
"overall"
] | Generating Question Relevant Captions to Aid Visual Question Answering |
The success of deep supervised learning depends on its automatic data representation abilities. A good representation of high-dimensional complex data should enjoy low-dimensionally and disentanglement while losing as little information as possible.
In this work, we give a statistical understanding of how deep represe... | [] | [
"Image Classification",
"Regression",
"Representation Learning"
] | [] | [
"Kuzushiji-MNIST",
"STL-10"
] | [
"Percentage correct",
"Accuracy"
] | Toward Understanding Supervised Representation Learning with RKHS and GAN |
In this paper, we introduce a new model for leveraging unlabeled data to
improve generalization performances of image classifiers: a two-branch
encoder-decoder architecture called HybridNet. The first branch receives
supervision signal and is dedicated to the extraction of invariant
class-related representations. The s... | [] | [
"Image Classification"
] | [] | [
"STL-10"
] | [
"Percentage correct"
] | HybridNet: Classification and Reconstruction Cooperation for Semi-Supervised Learning |
Although deep learning has been applied to successfully address many data mining problems, relatively limited work has been done on deep learning for anomaly detection. Existing deep anomaly detection methods, which focus on learning new feature representations to enable downstream anomaly detection methods, perform in... | [] | [
"Anomaly Detection",
"Cyber Attack Detection",
"Fraud Detection",
"Network Intrusion Detection",
"Representation Learning"
] | [] | [
"Kaggle-Credit Card Fraud Dataset",
"NB15-Backdoor",
"Census",
"Thyroid"
] | [
"Average Precision",
"AUC"
] | Deep Anomaly Detection with Deviation Networks |
Temporal receptive fields of models play an important role in action segmentation. Large receptive fields facilitate the long-term relations among video clips while small receptive fields help capture the local details. Existing methods construct models with hand-designed receptive fields in layers. Can we effectively ... | [] | [
"Action Segmentation"
] | [] | [
"50 Salads"
] | [
"Acc",
"Edit",
"F1@10%",
"F1@25%",
"F1@50%"
] | Global2Local: Efficient Structure Search for Video Action Segmentation |
Neural networks are a powerful means of classifying object images. The proposed
image category classification method for object images combines convolutional neural
networks (CNNs) and support vector machines (SVMs). A pre-trained CNN, called Alex-Net,
is used as a pattern-feature extractor. Alex-Net is pre-trained ... | [] | [
"Data Augmentation",
"Image Augmentation",
"Image Classification"
] | [] | [
"STL-10"
] | [
"Percentage correct"
] | Image Augmentation for Object Image Classification Based On Combination of PreTrained CNN and SVM |
Inferring the depth of images is a fundamental inverse problem within the field of Computer Vision since depth information is obtained through 2D images, which can be generated from infinite possibilities of observed real scenes. Benefiting from the progress of Convolutional Neural Networks (CNNs) to explore structural... | [] | [
"Autonomous Navigation",
"Autonomous Vehicles",
"Depth Completion",
"Monocular Depth Estimation",
"Semantic Segmentation",
"Surface Normals Estimation",
"Visual Odometry"
] | [] | [
"NYU-Depth V2",
"NYU-Depth V2 Surface Normals",
"KITTI Eigen split",
"KITTI Depth Completion Eigen Split"
] | [
"RMSE",
"REL",
"absolute relative error"
] | On Deep Learning Techniques to Boost Monocular Depth Estimation for Autonomous Navigation |
Neural Machine Translation (NMT), though recently developed, has shown promising results for various language pairs. Despite that, NMT has only been applied to mostly formal texts such as those in the WMT shared tasks. This work further explores the effectiveness of NMT in spoken language domains by participating in th... | [] | [
"Machine Translation"
] | [] | [
"IWSLT2015 English-Vietnamese"
] | [
"BLEU"
] | Stanford Neural Machine Translation Systems for Spoken Language Domains |
We study how to sample negative examples to automatically construct a training set for effective model learning in retrieval-based dialogue systems. Following an idea of dynamically adapting negative examples to matching models in learning, we consider four strategies including minimum sampling, maximum sampling, semi-... | [] | [
"Conversational Response Selection"
] | [] | [
"Ubuntu Dialogue (v1, Ranking)"
] | [
"R10@1",
"R10@5",
"R2@1",
"R10@2"
] | Sampling Matters! An Empirical Study of Negative Sampling Strategies for Learning of Matching Models in Retrieval-based Dialogue Systems |
While depth cameras and inertial sensors have been frequently leveraged for
human action recognition, these sensing modalities are impractical in many
scenarios where cost or environmental constraints prohibit their use. As such,
there has been recent interest on human action recognition using low-cost,
readily-availab... | [] | [
"Action Recognition",
"Multimodal Activity Recognition",
"Pose Estimation",
"Skeleton Based Action Recognition",
"Temporal Action Localization"
] | [] | [
"UTD-MHAD",
"J-HMDB"
] | [
"Accuracy (CS)",
"Accuracy (RGB+pose)"
] | STAR-Net: Action Recognition using Spatio-Temporal Activation Reprojection |
A new deep learning-based electroencephalography (EEG) signal analysis framework is proposed. While deep neural networks, specifically convolutional neural networks (CNNs), have gained remarkable attention recently, they still suffer from high dimensionality of the training data. Two-dimensional input images of CNNs ar... | [] | [
"Dimensionality Reduction",
"EEG",
"Image Generation",
"Seizure Detection",
"Time Series"
] | [] | [
"CHB-MIT"
] | [
"Accuracy"
] | EEG Signal Dimensionality Reduction and Classification using Tensor Decomposition and Deep Convolutional Neural Networks |
Real-world data often follow a long-tailed distribution as the frequency of each class is typically different. For example, a dataset can have a large number of under-represented classes and a few classes with more than sufficient data. However, a model to represent the dataset is usually expected to have reasonably ho... | [] | [
"Image Classification"
] | [] | [
"iNaturalist 2018"
] | [
"Top-1 Accuracy"
] | Feature Space Augmentation for Long-Tailed Data |
We present our UWB system for the task of capturing discriminative attributes at SemEval 2018. Given two words and an attribute, the system decides, whether this attribute is discriminative between the words or not. Assuming Distributional Hypothesis, i.e., a word meaning is related to the distribution across contexts,... | [] | [
"Relation Extraction"
] | [] | [
"SemEval 2018 Task 10"
] | [
"F1-Score"
] | UWB at SemEval-2018 Task 10: Capturing Discriminative Attributes from Word Distributions |
Knowledge Graph Completion (KGC) aims at automatically predicting missing links for large-scale knowledge graphs. A vast number of state-of-the-art KGC techniques have got published at top conferences in several research fields, including data mining, machine learning, and natural language processing. However, we notic... | [] | [
"Knowledge Graph Completion",
"Knowledge Graphs",
"Link Prediction"
] | [] | [
"FB15k-237"
] | [
"Hits@10",
"MR",
"MRR"
] | A Re-evaluation of Knowledge Graph Completion Methods |
We present an online approach to efficiently and simultaneously detect and track the 2D pose of multiple people in a video sequence. We build upon Part Affinity Field (PAF) representation designed for static images, and propose an architecture that can encode and predict Spatio-Temporal Affinity Fields (STAF) across a ... | [] | [
"Pose Tracking"
] | [] | [
"PoseTrack2017"
] | [
"MOTA"
] | Efficient Online Multi-Person 2D Pose Tracking with Recurrent Spatio-Temporal Affinity Fields |
In the financial domain, risk modeling and profit generation heavily rely on the sophisticated and intricate stock movement prediction task. Stock forecasting is complex, given the stochastic dynamics and non-stationary behavior of the market. Stock movements are influenced by varied factors beyond the conventionally s... | [] | [
"Decision Making",
"Stock Market Prediction"
] | [] | [
"stocknet"
] | [
"F1"
] | Deep Attentive Learning for Stock Movement Prediction From Social Media Text and Company Correlations |
Attention operators have been widely applied in various fields, including computer vision, natural language processing, and network embedding learning. Attention operators on graph data enables learnable weights when aggregating information from neighboring nodes. However, graph attention operators (GAOs) consume exces... | [] | [
"Graph Classification",
"Graph Embedding",
"Graph Representation Learning",
"Network Embedding",
"Representation Learning"
] | [] | [
"COLLAB",
"PROTEINS",
"D&D",
"IMDb-M",
"MUTAG",
"PTC"
] | [
"Accuracy"
] | Graph Representation Learning via Hard and Channel-Wise Attention Networks |
We propose a general framework for self-supervised learning of transferable visual representations based on Video-Induced Visual Invariances (VIVI). We consider the implicit hierarchy present in the videos and make use of (i) frame-level invariances (e.g. stability to color and contrast perturbations), (ii) shot/clip-l... | [] | [
"Image Classification",
"Self-Supervised Learning",
"Transfer Learning"
] | [] | [
"VTAB-1k"
] | [
"Top-1 Accuracy"
] | Self-Supervised Learning of Video-Induced Visual Invariances |
The incorporation of pseudo data in the training of grammatical error correction models has been one of the main factors in improving the performance of such models. However, consensus is lacking on experimental configurations, namely, choosing how the pseudo data should be generated or used. In this study, these choic... | [] | [
"Grammatical Error Correction"
] | [] | [
"CoNLL-2014 Shared Task",
"BEA-2019 (test)"
] | [
"F0.5"
] | An Empirical Study of Incorporating Pseudo Data into Grammatical Error Correction |
In this paper, we present a novel method named RECON, that automatically identifies relations in a sentence (sentential relation extraction) and aligns to a knowledge graph (KG). RECON uses a graph neural network to learn representations of both the sentence as well as facts stored in a KG, improving the overall extrac... | [] | [
"Relation Extraction"
] | [] | [
"New York Times Corpus"
] | [
"P@30%",
"P@10%"
] | RECON: Relation Extraction using Knowledge Graph Context in a Graph Neural Network |
To answer the question in machine comprehension (MC) task, the models need to
establish the interaction between the question and the context. To tackle the
problem that the single-pass model cannot reflect on and correct its answer, we
present Ruminating Reader. Ruminating Reader adds a second pass of attention
and a n... | [] | [
"Question Answering",
"Reading Comprehension"
] | [] | [
"SQuAD1.1 dev",
"SQuAD1.1"
] | [
"EM",
"F1"
] | Ruminating Reader: Reasoning with Gated Multi-Hop Attention |
Graph classification is a significant problem in many scientific domains. It
addresses tasks such as the classification of proteins and chemical compounds
into categories according to their functions, or chemical and structural
properties. In a supervised setting, this problem can be framed as learning the
structure, f... | [] | [
"Graph Classification"
] | [] | [
"NCI109",
"ENZYMES",
"PROTEINS",
"D&D",
"NCI1",
"MUTAG",
"PTC"
] | [
"Accuracy"
] | Capsule Neural Networks for Graph Classification using Explicit Tensorial Graph Representations |
Objects moving at high speed along complex trajectories often appear in videos, especially videos of sports. Such objects elapse non-negligible distance during exposure time of a single frame and therefore their position in the frame is not well defined. They appear as semi-transparent streaks due to the motion blur an... | [] | [
"Deblurring",
"Image Matting",
"Object Tracking"
] | [] | [
"Falling Objects",
"TbD",
"TbD-3D"
] | [
"SSIM",
"TIoU",
"PSNR"
] | Intra-frame Object Tracking by Deblatting |
Semantic segmentation of 3D point cloud data is essential for enhanced high-level perception in autonomous platforms. Furthermore, given the increasing deployment of LiDAR sensors onboard of cars and drones, a special emphasis is also placed on non-computationally intensive algorithms that operate on mobile GPUs. Previ... | [] | [
"Real-Time Semantic Segmentation",
"Semantic Segmentation"
] | [] | [
"SemanticKITTI"
] | [
"Speed (FPS)",
"mIOU",
"mIoU"
] | Multi Projection Fusion for Real-time Semantic Segmentation of 3D LiDAR Point Clouds |
Recognizing text from natural images is a hot research topic in computer
vision due to its various applications. Despite the enduring research of
several decades on optical character recognition (OCR), recognizing texts from
natural images is still a challenging task. This is because scene texts are
often in irregular ... | [] | [
"Optical Character Recognition"
] | [] | [
"ICDAR2015",
"ICDAR 2003"
] | [
"Accuracy"
] | AON: Towards Arbitrarily-Oriented Text Recognition |
We aim to detect all instances of a category in an image and, for each
instance, mark the pixels that belong to it. We call this task Simultaneous
Detection and Segmentation (SDS). Unlike classical bounding box detection, SDS
requires a segmentation and not just a box. Unlike classical semantic
segmentation, we require... | [] | [
"Object Detection",
"Semantic Segmentation"
] | [] | [
"PASCAL VOC 2012",
"PASCAL VOC 2012 test"
] | [
"Mean IoU",
"MAP"
] | Simultaneous Detection and Segmentation |
We investigate a new commonsense inference task: given an event described in a short free-form text ("X drinks coffee in the morning"), a system reasons about the likely intents ("X wants to stay awake") and reactions ("X feels alert") of the event's participants. To support this study, we construct a new crowdsourced ... | [] | [
"Common Sense Reasoning"
] | [] | [
"Event2Mind test",
"Event2Mind dev"
] | [
"Average Cross-Ent"
] | Event2Mind: Commonsense Inference on Events, Intents, and Reactions |
End of preview. Expand in Data Studio
PwC4KPG dataset
Due to the strict copyright restriction, the dataset is only available for non-commercial research use ONLY.
Currently it requires manual approval for access. Please send an email to yijiang@whu.edu.cn, stating (1) Huggingface account name; (2) institute/company name; (3) the purpose of using this dataset.
PwC4KPG dataset
we extract the fields, tasks, methods, datasets, metrics, titles and abstracts from the raw corpus of PwC, provided that the paper has a full title and abstract. A total of 6,012 papers were extracted, of which 2,119 included all five categories of “keyphrases”, and the remaining 3,839 contained only some of them. Note that PwC does not contain the research fields as we define them, so we used the “main_collection” of methods as an alternative.
Train: 5,012 / Dev: 500 / Test: 500
We randomly select 1,000 papers with full information,half of which are used for testing and the other half for validation. The remaining 5,012 served as the training set.
Paper: JASIST 2023, Generating keyphrases for readers: A controllable keyphrase generation framework.
@inproceedings{Jiang2023PwC4KPG,
title={ Generating keyphrases for readers: A controllable keyphrase generation framework},
author={Jiang, Yi and Meng, Rui and Huang, Yong and Lu, Wei and Liu, Jiawei},
booktitle={Journal of the Association for Information Science and Technology},
year={2023},
volume={74},
issue={7},
pages={759--774},
}
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