https://github.com/naver-intel-co-lab/gaudi-lavcap
공식 코드LAVCap: LLM-based Audio-Visual Captioning using Optimal Transport
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Audio Captioning is the task of describing audio using text. The general approach is to use an audio encoder to encode the audio (example: PANN, CAV-MAE), and to use a decoder (example: transformer) to generate the text. To judge the quality of audio captions, though machine translation metrics (BLEU, METEOR, ROUGE) and image captioning metrics (SPICE, CIDER) are used, they are not very well-suited. Attempts have been made to use pretrained language model based metrics such as Sentence-BERT.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
LAVCap: LLM-based Audio-Visual Captioning using Optimal Transport
Audio Difference Captioning Utilizing Similarity-Discrepancy Disentanglement
Automated Audio Captioning and Language-Based Audio Retrieval
A Whisper transformer for audio captioning trained with synthetic captions and transfer learning
Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Models
FusionAudio-1.2M: Towards Fine-grained Audio Captioning with Multimodal Contextual Fusion
OpenSep: Leveraging Large Language Models with Textual Inversion for Open World Audio Separation
VAST: A Vision-Audio-Subtitle-Text Omni-Modality Foundation Model and Dataset
Crowdsourcing and Evaluating Text-Based Audio Retrieval Relevances
Weakly-supervised Automated Audio Captioning via text only training
LAVCap: LLM-based Audio-Visual Captioning using Optimal Transport
Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Models
LauraGPT: Listen, Attend, Understand, and Regenerate Audio with GPT
LauraGPT: Listen, Attend, Understand, and Regenerate Audio with GPT
M2D2: Exploring General-purpose Audio-Language Representations Beyond CLAP
M2D2: Exploring General-purpose Audio-Language Representations Beyond CLAP
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.