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Found 54 papers, 28 papers with code
XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalisation
However, these broad-coverage benchmarks have been mostly limited to English, and despite an increasing interest in multilingual models, a benchmark that enables the comprehensive evaluation of such methods on a diverse range of languages and tasks is still missing.
Ranked #1 on
Zero-Shot Cross-Lingual Transfer
on XTREME
(AVG metric)
Explicit Attention-Enhanced Fusion for RGB-Thermal Perception Tasks
Specifically, we consider the following cases: i) both RGB data and thermal data, ii) only one of the types of data, and iii) none of them generate discriminative features.
Lifelong-MonoDepth: Lifelong Learning for Multi-Domain Monocular Metric Depth Estimation
In this paper, we seek to enable lifelong learning for MDE, which performs cross-domain depth learning sequentially, to achieve high plasticity on a new domain and maintain good stability on original domains.
ConTEXTual Net: A Multimodal Vision-Language Model for Segmentation of Pneumothorax
In this work, we develop a vision-language model for the task of pneumothorax segmentation.
Domain-adapted large language models for classifying nuclear medicine reports
Domain adaptation improved the performance of large language models in interpreting nuclear medicine text reports.
Peer Learning for Unbiased Scene Graph Generation
Unbiased scene graph generation (USGG) is a challenging task that requires predicting diverse and heavily imbalanced predicates between objects in an image.
Attentional Graph Convolutional Network for Structure-aware Audio-Visual Scene Classification
Then, to build multi-scale hierarchical information of input features, we utilize an attention fusion mechanism to aggregate features from multiple layers of the backbone network.
Beyond Counting Datasets: A Survey of Multilingual Dataset Construction and Necessary Resources
While the NLP community is generally aware of resource disparities among languages, we lack research that quantifies the extent and types of such disparity.
Progressive Self-Distillation for Ground-to-Aerial Perception Knowledge Transfer
In this paper, we introduce the ground-to-aerial perception knowledge transfer and propose a progressive semi-supervised learning framework that enables drone perception using only labeled data of ground viewpoint and unlabeled data of flying viewpoints.
Data-free Dense Depth Distillation
We study data-free knowledge distillation (KD) for monocular depth estimation (MDE), which learns a lightweight model for real-world depth perception tasks by compressing it from a trained teacher model while lacking training data in the target domain.
MIA 2022 Shared Task: Evaluating Cross-lingual Open-Retrieval Question Answering for 16 Diverse Languages
We present the results of the Workshop on Multilingual Information Access (MIA) 2022 Shared Task, evaluating cross-lingual open-retrieval question answering (QA) systems in 16 typologically diverse languages.
Utilizing Language-Image Pretraining for Efficient and Robust Bilingual Word Alignment
Word translation without parallel corpora has become feasible, rivaling the performance of supervised methods.
Local Byte Fusion for Neural Machine Translation
Subword tokenization schemes are the dominant technique used in current NLP models.
Deep Depth Completion from Extremely Sparse Data: A Survey
Depth completion aims at predicting dense pixel-wise depth from an extremely sparse map captured from a depth sensor, e. g., LiDARs.
DEEP: DEnoising Entity Pre-training for Neural Machine Translation
It has been shown that machine translation models usually generate poor translations for named entities that are infrequent in the training corpus.
Advanced Statistical Learning on Short Term Load Process Forecasting
The predictive information is fundamental for the risk and production management of electricity consumers.
FEANet: Feature-Enhanced Attention Network for RGB-Thermal Real-time Semantic Segmentation
To better extract detail spatial information, we propose a two-stage Feature-Enhanced Attention Network (FEANet) for the RGB-T semantic segmentation task.
Ranked #6 on
Thermal Image Segmentation
on MFN Dataset
GlobalWoZ: Globalizing MultiWoZ to Develop Multilingual Task-Oriented Dialogue Systems
Much recent progress in task-oriented dialogue (ToD) systems has been driven by available annotation data across multiple domains for training.
PLNet: Plane and Line Priors for Unsupervised Indoor Depth Estimation
Unsupervised learning of depth from indoor monocular videos is challenging as the artificial environment contains many textureless regions.
AfroMT: Pretraining Strategies and Reproducible Benchmarks for Translation of 8 African Languages
Reproducible benchmarks are crucial in driving progress of machine translation research.
Learn2Agree: Fitting with Multiple Annotators without Objective Ground Truth
For such issues, we propose a novel Learning to Agreement (Learn2Agree) framework to tackle the challenge of learning from multiple annotators without objective ground truth.
Networks of News and Cross-Sectional Returns
We uncover networks from news articles to study cross-sectional stock returns.
Phrase-level Active Learning for Neural Machine Translation
Neural machine translation (NMT) is sensitive to domain shift.
Boosting Light-Weight Depth Estimation Via Knowledge Distillation
In this paper, we aim to achieve accurate depth estimation with a light-weight network.
XTREME-R: Towards More Challenging and Nuanced Multilingual Evaluation
While a sizeable gap to human-level performance remains, improvements have been easier to achieve in some tasks than in others.
Multilingual Multimodal Pre-training for Zero-Shot Cross-Lingual Transfer of Vision-Language Models
Specifically, we focus on multilingual text-to-video search and propose a Transformer-based model that learns contextualized multilingual multimodal embeddings.
A Two-stage Unsupervised Approach for Low light Image Enhancement
However, most of them suffer from the following problems: 1) the need of pairs of low light and normal light images for training, 2) the poor performance for dark images, 3) the amplification of noise.
Low-Light Image Enhancement
Simultaneous Localization and Mapping
Semantic Histogram Based Graph Matching for Real-Time Multi-Robot Global Localization in Large Scale Environment
The core problem of visual multi-robot simultaneous localization and mapping (MR-SLAM) is how to efficiently and accurately perform multi-robot global localization (MR-GL).
Explicit Alignment Objectives for Multilingual Bidirectional Encoders
Pre-trained cross-lingual encoders such as mBERT (Devlin et al., 2019) and XLMR (Conneau et al., 2020) have proven to be impressively effective at enabling transfer-learning of NLP systems from high-resource languages to low-resource languages.
On Learning Language-Invariant Representations for Universal Machine Translation
The goal of universal machine translation is to learn to translate between any pair of languages, given a corpus of paired translated documents for \emph{a small subset} of all pairs of languages.
TICO-19: the Translation Initiative for Covid-19
Further, the team is converting the test and development data into translation memories (TMXs) that can be used by localizers from and to any of the languages.
Unsupervised Multimodal Neural Machine Translation with Pseudo Visual Pivoting
In this paper, we investigate how to utilize visual content for disambiguation and promoting latent space alignment in unsupervised MMT.
XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization
However, these broad-coverage benchmarks have been mostly limited to English, and despite an increasing interest in multilingual models, a benchmark that enables the comprehensive evaluation of such methods on a diverse range of languages and tasks is still missing.
Risk of Bitcoin Market: Volatility, Jumps, and Forecasts
Cryptocurrency, the most controversial and simultaneously the most interesting asset, has attracted many investors and speculators in recent years.
Analysis of Deep Networks for Monocular Depth Estimation Through Adversarial Attacks with Proposal of a Defense Method
However, the prediction of saliency maps is itself vulnerable to the attacks, even though it is not the direct target of the attacks.
Domain Differential Adaptation for Neural Machine Translation
We then use these learned domain differentials to adapt models for the target task accordingly.
What Makes A Good Story? Designing Composite Rewards for Visual Storytelling
Previous storytelling approaches mostly focused on optimizing traditional metrics such as BLEU, ROUGE and CIDEr.
REO-Relevance, Extraness, Omission: A Fine-grained Evaluation for Image Captioning
In this study, we present a fine-grained evaluation method REO for automatically measuring the performance of image captioning systems.
Handling Syntactic Divergence in Low-resource Machine Translation
Despite impressive empirical successes of neural machine translation (NMT) on standard benchmarks, limited parallel data impedes the application of NMT models to many language pairs.
Unsupervised Domain Adaptation for Neural Machine Translation with Domain-Aware Feature Embeddings
The recent success of neural machine translation models relies on the availability of high quality, in-domain data.
Domain Adaptation of Neural Machine Translation by Lexicon Induction
It has been previously noted that neural machine translation (NMT) is very sensitive to domain shift.
A Hybrid Retrieval-Generation Neural Conversation Model
In this paper, we propose a hybrid neural conversation model that combines the merits of both response retrieval and generation methods.
Visualization of Convolutional Neural Networks for Monocular Depth Estimation
We formulate it as an optimization problem of identifying the smallest number of image pixels from which the CNN can estimate a depth map with the minimum difference from the estimate from the entire image.
compare-mt: A Tool for Holistic Comparison of Language Generation Systems
In this paper, we describe compare-mt, a tool for holistic analysis and comparison of the results of systems for language generation tasks such as machine translation.
The ARIEL-CMU Systems for LoReHLT18
This paper describes the ARIEL-CMU submissions to the Low Resource Human Language Technologies (LoReHLT) 2018 evaluations for the tasks Machine Translation (MT), Entity Discovery and Linking (EDL), and detection of Situation Frames in Text and Speech (SF Text and Speech).
Contextual Encoding for Translation Quality Estimation
In this paper, propose a method to effectively encode the local and global contextual information for each target word using a three-part neural network approach.
Rapid Adaptation of Neural Machine Translation to New Languages
This paper examines the problem of adapting neural machine translation systems to new, low-resourced languages (LRLs) as effectively and rapidly as possible.
Automatic Estimation of Simultaneous Interpreter Performance
Simultaneous interpretation, translation of the spoken word in real-time, is both highly challenging and physically demanding.
Revisiting Single Image Depth Estimation: Toward Higher Resolution Maps with Accurate Object Boundaries
Experimental results show that these two improvements enable to attain higher accuracy than the current state-of-the-arts, which is given by finer resolution reconstruction, for example, with small objects and object boundaries.
Ranked #39 on
Monocular Depth Estimation
on NYU-Depth V2
Principled Hybrids of Generative and Discriminative Domain Adaptation
This provides us a very general way to interpolate between generative and discriminative extremes through different choices of priors.
Structural Embedding of Syntactic Trees for Machine Comprehension
Deep neural networks for machine comprehension typically utilizes only word or character embeddings without explicitly taking advantage of structured linguistic information such as constituency trees and dependency trees.
Ranked #40 on
Question Answering
on SQuAD1.1 dev
Semi-Supervised QA with Generative Domain-Adaptive Nets
In this framework, we train a generative model to generate questions based on the unlabeled text, and combine model-generated questions with human-generated questions for training question answering models.
Words or Characters? Fine-grained Gating for Reading Comprehension
Previous work combines word-level and character-level representations using concatenation or scalar weighting, which is suboptimal for high-level tasks like reading comprehension.
Ranked #50 on
Question Answering
on SQuAD1.1 dev
Learning Lexical Entries for Robotic Commands using Crowdsourcing
Robotic commands in natural language usually contain various spatial descriptions that are semantically similar but syntactically different.
