Zobrazeno 1 - 10
of 3 023
pro vyhledávání: '"Zero-Shot learning"'
Autor:
Jingru SONG, Weiqing MIN, Pengfei ZHOU, Quanrui RAO, Guorui SHENG, Yancun YANG, Lili WANG, Shuqiang JIANG
Publikováno v:
Shipin gongye ke-ji, Vol 45, Iss 22, Pp 18-26 (2024)
As a fundamental task in food computing, food detection played a crucial role in locating and identifying food items from input images, particularly in applications such as intelligent canteen settlement and dietary health management. However, food c
Externí odkaz:
https://doaj.org/article/2bc25d48318f4148bd5ef1bd5cd76292
Publikováno v:
Proceedings of the XXth Conference of Open Innovations Association FRUCT, Vol 36, Iss 1, Pp 578-584 (2024)
Vision-language models (VLMs) have shown remarkable potential in various domains, particularly in zero-shot learning applications. This research focuses on evaluating the performance of notable VLMs—CLIP, PLIP, and BiomedCLIP—in the classificatio
Externí odkaz:
https://doaj.org/article/a864bd43d6bc47baa745bc8d5c31ced7
Publikováno v:
工程科学学报, Vol 46, Iss 9, Pp 1613-1622 (2024)
Hypersonic vehicles play a crucial role in various applications and are complex systems that integrate aviation, electronics, computer control, electrical information, and sensing technologies. Owing to this complexity and their harsh working environ
Externí odkaz:
https://doaj.org/article/55b6b86bcd5148c9befec9035c824979
Publikováno v:
PeerJ Computer Science, Vol 10, p e2463 (2024)
The proliferation of digital information necessitates advanced techniques for multiple document summarization, capable of distilling vast textual data efficiently. Traditional approaches often struggle with coherence, integration of multimodal data,
Externí odkaz:
https://doaj.org/article/1d1dc5da68fc48a79983a94734e8401d
Publikováno v:
IEEE Access, Vol 12, Pp 165822-165830 (2024)
The goal of Compositional Zero-Shot Learning (CZSL) is to recognize various compositions of state-object pairs. Because the compositions that need to be considered are only a subset of all combinations of states and objects, it is tough for models to
Externí odkaz:
https://doaj.org/article/43addab54d7543689df9d87f64fc4aa6
Autor:
Muqaddas Gull, Omar Arif
Publikováno v:
IEEE Access, Vol 12, Pp 94990-95006 (2024)
Multi-label zero-shot learning expands upon the traditional single-label zero-shot learning paradigm by addressing the challenge of accurately classifying images containing multiple unseen classes, which are not part of the training data. Current tec
Externí odkaz:
https://doaj.org/article/9a7c10df15ac4b97af013657df4a098a
Publikováno v:
EURASIP Journal on Audio, Speech, and Music Processing, Vol 2024, Iss 1, Pp 1-18 (2024)
Abstract In the era of advanced text-to-speech (TTS) systems capable of generating high-fidelity, human-like speech by referring a reference speech, voice cloning (VC), or zero-shot TTS (ZS-TTS), stands out as an important subtask. A primary challeng
Externí odkaz:
https://doaj.org/article/e08805403ca34028a8d742b9a22ab0cc
Autor:
Stephan Rau, Alexander Rau, Johanna Nattenmüller, Anna Fink, Fabian Bamberg, Marco Reisert, Maximilian F. Russe
Publikováno v:
European Radiology Experimental, Vol 8, Iss 1, Pp 1-8 (2024)
Abstract Background We investigated the potential of an imaging-aware GPT-4-based chatbot in providing diagnoses based on imaging descriptions of abdominal pathologies. Methods Utilizing zero-shot learning via the LlamaIndex framework, GPT-4 was enha
Externí odkaz:
https://doaj.org/article/aa1b79565b334cd4ac9680811f0f13db
Zero-Shot Pest Identification Based on Generative Adversarial Networks and Visual-Semantic Alignment
Publikováno v:
智慧农业, Vol 6, Iss 2, Pp 72-84 (2024)
ObjectiveAccurate identification of insect pests is crucial for the effective prevention and control of crop infestations. However, existing pest identification methods primarily rely on traditional machine learning or deep learning techniques that a
Externí odkaz:
https://doaj.org/article/a8b44deb28cc4c04ad545347298c8cbc
Autor:
Muhammad Asif, Surayya Naz, Faheem Ali, Amerah Alabrah, Abdu Salam, Farhan Amin, Faizan Ullah
Publikováno v:
IEEE Access, Vol 12, Pp 184393-184407 (2024)
Federated learning (FL) introduces new perspectives in machine learning (ML) by enabling model training across decentralized devices. The research on data security and privacy in federated learning (FL) gaining popularity nowadays. However, FL has se
Externí odkaz:
https://doaj.org/article/f00859922c14402a959599683999d414