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Found 20 papers, 4 papers with code
Family of Origin and Family of Choice: Massively Parallel Lexiconized Iterative Pretraining for Severely Low Resource Text-based Translation
In other words, given a text in 124 source languages, we translate it into a severely low resource language using only ∼1, 000 lines of low resource data without any external help.
Few-Shot Open-Set Object Detection via Conditional Evidence Decoupling
To mitigate this issue, we develop a two-stage open-set object detection framework with prompt learning, which delves into conditional evidence decoupling for the unknown rejection.
Massively Multilingual Text Translation For Low-Resource Languages
Performance gain comes from massive source parallelism by careful choice of close-by language families, style-consistent corpus-level paraphrases within the same language and strategic adaptation of existing large pretrained multilingual models to the domain first and then to the language.
Train Global, Tailor Local: Minimalist Multilingual Translation into Endangered Languages
We examine two approaches: 1. best selection of seed sentences to jump start translations in a new language in view of best generalization to the remainder of a larger targeted text(s), and 2. we adapt large general multilingual translation engines from many other languages to focus on a specific text in a new, unknown language.
QR-CLIP: Introducing Explicit Open-World Knowledge for Location and Time Reasoning
Daily images may convey abstract meanings that require us to memorize and infer profound information from them.
Towards Generalized Few-Shot Open-Set Object Detection
In this paper, we seek a solution for the generalized few-shot open-set object detection (G-FOOD), which aims to avoid detecting unknown classes as known classes with a high confidence score while maintaining the performance of few-shot detection.
Active Learning for Massively Parallel Translation of Constrained Text into Low Resource Languages
We compare the portion-based approach that optimizes coherence of the text locally with the random sampling approach that increases coverage of the text globally.
Family of Origin and Family of Choice: Massively Parallel Lexiconized Iterative Pretraining for Severely Low Resource Machine Translation
In other words, given a text in 124 source languages, we translate it into a severely low resource language using only ~1, 000 lines of low resource data without any external help.
SIGAN: A Novel Image Generation Method for Solar Cell Defect Segmentation and Augmentation
Moreover, we release a new solar cell EL image dataset named as EL-2019, which includes three types of images: crack, finger interruption and defect-free.
BAF-Detector: An Efficient CNN-Based Detector for Photovoltaic Cell Defect Detection
Finally, the experimental results on a large-scale EL dataset including 3629 images, 2129 of which are defective, show that the proposed method achieves 98. 70% (F-measure), 88. 07% (mAP), and 73. 29% (IoU) in terms of multi-scale defects classification and detection results in raw PV cell EL images.
Object-aware Feature Aggregation for Video Object Detection
As a consequence, augmenting features with such prior knowledge can effectively improve the classification and localization performance.
Gun Source and Muzzle Head Detection
We have interesting results both in bounding the shooter as well as detecting the gun smoke.
Gated Path Selection Network for Semantic Segmentation
Semantic segmentation is a challenging task that needs to handle large scale variations, deformations and different viewpoints.
Using Interlinear Glosses as Pivot in Low-Resource Multilingual Machine Translation
Firstly, we pool IGT for 1, 497 languages in ODIN (54, 545 glosses) and 70, 918 glosses in Arapaho and train a gloss-to-target NMT system from IGT to English, with a BLEU score of 25. 94.
SANTLR: Speech Annotation Toolkit for Low Resource Languages
In this work, we present SANTLR: Speech Annotation Toolkit for Low Resource Languages.
Part-level Car Parsing and Reconstruction from Single Street View
As it is labor-intensive to annotate semantic parts on real street views, we propose a specific approach to implicitly transfer part features from synthesized images to real street views.
Paraphrases as Foreign Languages in Multilingual Neural Machine Translation
Our multi-paraphrase NMT that trains only on two languages outperforms the multilingual baselines.
A Network Structure to Explicitly Reduce Confusion Errors in Semantic Segmentation
Confusing classes that are ubiquitous in real world often degrade performance for many vision related applications like object detection, classification, and segmentation.
Massively Parallel Cross-Lingual Learning in Low-Resource Target Language Translation
The main challenges we identify are the lack of low-resource language data, effective methods for cross-lingual transfer, and the variable-binding problem that is common in neural systems.
Tweet2Vec: Character-Based Distributed Representations for Social Media
Text from social media provides a set of challenges that can cause traditional NLP approaches to fail.
