Zobrazeno 1 - 10
of 13 261
pro vyhledávání: '"Zero shot learning"'
Zero-shot learning enables models to generalize to unseen classes by leveraging semantic information, bridging the gap between training and testing sets with non-overlapping classes. While much research has focused on zero-shot learning in computer v
Externí odkaz:
http://arxiv.org/abs/2412.03771
Audio-visual Zero-Shot Learning (ZSL) has attracted significant attention for its ability to identify unseen classes and perform well in video classification tasks. However, modal imbalance in (G)ZSL leads to over-reliance on the optimal modality, re
Externí odkaz:
http://arxiv.org/abs/2412.11715
Autor:
Fujii, Takuro, Katsumata, Satoru
Recently some studies have highlighted the potential of Large Language Models (LLMs) as effective generators of supervised training data, offering advantages such as enhanced inference efficiency and reduced costs associated with data collection. How
Externí odkaz:
http://arxiv.org/abs/2412.06738
Compositional Zero-Shot Learning (CZSL) aims to recognize unseen combinations of seen attributes and objects. Current CLIP-based methods in CZSL, despite their advancements, often fail to effectively understand and link the attributes and objects due
Externí odkaz:
http://arxiv.org/abs/2412.07161
Open-World Compositional Zero-Shot Learning (OW-CZSL) addresses the challenge of recognizing novel compositions of known primitives and entities. Even though prior works utilize language knowledge for recognition, such approaches exhibit limited inte
Externí odkaz:
http://arxiv.org/abs/2412.04083
Compositional Zero-Shot Learning (CZSL) recognizes new combinations by learning from known attribute-object pairs. However, the main challenge of this task lies in the complex interactions between attributes and object visual representations, which l
Externí odkaz:
http://arxiv.org/abs/2412.00121
Compositional zero-shot learning (CZSL) aims to recognize novel compositions of attributes and objects learned from seen compositions. Previous works disentangle attribute and object by extracting shared and exclusive parts between image pairs sharin
Externí odkaz:
http://arxiv.org/abs/2411.12584
Zero-shot learning (ZSL) aims to leverage additional semantic information to recognize unseen classes. To transfer knowledge from seen to unseen classes, most ZSL methods often learn a shared embedding space by simply aligning visual embeddings with
Externí odkaz:
http://arxiv.org/abs/2411.11351
Autor:
Liu, Man, Bai, Huihui, Li, Feng, Zhang, Chunjie, Wei, Yunchao, Wang, Meng, Chua, Tat-Seng, Zhao, Yao
Generalized zero-shot learning (GZSL) endeavors to identify the unseen categories using knowledge from the seen domain, necessitating the intrinsic interactions between the visual features and attribute semantic features. However, GZSL suffers from i
Externí odkaz:
http://arxiv.org/abs/2410.11560
Autor:
Jin, Pengfei, Shu, Peng, Kim, Sekeun, Xiao, Qing, Song, Sifan, Chen, Cheng, Liu, Tianming, Li, Xiang, Li, Quanzheng
Foundation models have become a cornerstone in deep learning, with techniques like Low-Rank Adaptation (LoRA) offering efficient fine-tuning of large models. Similarly, methods such as Retrieval-Augmented Generation (RAG), which leverage vectorized d
Externí odkaz:
http://arxiv.org/abs/2410.09908