Search Results for author:
Found 10 papers, 4 papers with code
Bridging the Gap between Text, Audio, Image, and Any Sequence: A Novel Approach using Gloss-based Annotation
This paper presents an innovative approach called BGTAI to simplify multimodal understanding by utilizing gloss-based annotation as an intermediate step in aligning Text and Audio with Images.
SignLLM: Sign Language Production Large Language Models
In this paper, we propose SignLLM, a multilingual Sign Language Production (SLP) large language model, which includes two novel multilingual SLP modes MLSF and Prompt2LangGloss that allow sign language gestures generation from query texts input and question-style prompts input respectively.
Generative AI to Generate Test Data Generators
The results show that LLMs can successfully generate realistic test data generators in a wide range of domains at all three levels of integrability.
RepairLLaMA: Efficient Representations and Fine-Tuned Adapters for Program Repair
This results in RepairLLaMA producing a highly effective `program repair adapter' for fixing bugs with AI.
Supersonic: Learning to Generate Source Code Optimizations in C/C++
Software optimization refines programs for resource efficiency while preserving functionality.
Echotune: A Modular Extractor Leveraging the Variable-Length Nature of Speech in ASR Tasks
The Transformer architecture has proven to be highly effective for Automatic Speech Recognition (ASR) tasks, becoming a foundational component for a plethora of research in the domain.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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SignDiff: Diffusion Models for American Sign Language Production
In this paper, we propose a dual-condition diffusion pre-training model named SignDiff that can generate human sign language speakers from a skeleton pose.
UniBriVL: Robust Universal Representation and Generation of Audio Driven Diffusion Models
Multimodal large models have been recognized for their advantages in various performance and downstream tasks.
Exploring Efficient-Tuned Learning Audio Representation Method from BriVL
Recently, researchers have gradually realized that in some cases, the self-supervised pre-training on large-scale Internet data is better than that of high-quality/manually labeled data sets, and multimodal/large models are better than single or bimodal/small models.
Deep Smart Contract Intent Detection
In recent years, research in software security has concentrated on identifying vulnerabilities in smart contracts to prevent significant losses of crypto assets on blockchains.
