Ashwin: Plug-and-Play System for Machine-Human Image Annotation
We present an end-to-end machine-human image annotation system where each component can be attached in a plug-and-play fashion. These components include Feature Extraction, Machine Classifier, Task Sampling and Crowd Consensus.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
PAS: Data-Efficient Plug-and-Play Prompt Augmentation System
In recent years, the rise of Large Language Models (LLMs) has spurred a growing demand for plug-and-play AI systems. Among the various AI techniques, prompt engineering stands out as particularly significant. However, us…
Prompt EngineeringIncorporating Connections Beyond Knowledge Embeddings: A Plug-and-Play Module to Enhance Commonsense Reasoning in Machine Reading Comprehension
Conventional Machine Reading Comprehension (MRC) has been well-addressed by pattern matching, but the ability of commonsense reasoning remains a gap between humans and machines. Previous methods tackle this problem by en…
Knowledge Graph EmbeddingsKnowledge GraphsMachine Reading ComprehensionReading ComprehensionFrom Camera to World: A Plug-and-Play Module for Human Mesh Transformation
Reconstructing accurate 3D human meshes in the world coordinate system from in-the-wild images remains challenging due to the lack of camera rotation information. While existing methods achieve promising results in the c…
Cascaded Beam Search: Plug-and-Play Terminology-Forcing For Neural Machine Translation
This paper presents a plug-and-play approach for translation with terminology constraints. Terminology constraints are an important aspect of many modern translation pipelines. In both specialized domains and newly emerg…
Language ModelingLanguage ModellingMachine TranslationSentence+1Plug-and-Play Multilingual Few-shot Spoken Words Recognition
As technology advances and digital devices become prevalent, seamless human-machine communication is increasingly gaining significance. The growing adoption of mobile, wearable, and other Internet of Things (IoT) devices…
Few-Shot LearningKeyword Spotting