AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
A text-guided Zero-Shot Localization framework for unseen object categories (ZSOL) for addressing the aforementioned challenges, which can be guided by prompt words to identify and localize unseen object categories in images by transferring localization knowledge learned from supervised base categories.
A novel framework that mines target knowledge using the strong reasoning capacity of Multimodal Large Language Models (MLLMs) and employs it to enhance FSS, which shows promising results and largely surpasses existing methods.
A new ITM framework that improves the model's discriminative performance by focusing on localized core attributes and has a better robustness in handling highly similar hard negatives, which provides a new way to address cross-modal hard sample discrimination.
This work proposes FineHOI, a zero-shot HOI framework that explicitly models interactions from dense patch-level features, and introduces an Adaptive Part-Level Attention module that decomposes humans and objects into semantically coherent parts via unsupervised clustering, and re-weights them based on their interactio...
Experimental results show that the proposed open-vocabulary object detection framework performs excellently on multiple benchmark datasets such as LVIS and COCO-O, demonstrating stronger adaptability to complex scenarios.
A multimodal Graph-based Fusion (MGF) framework that learns visually grounded semantic representations to enhance FSOR performance and achieves superior open-set recognition and competitive closed-set classification performance.
This work provides a systematic review of TBPS by formalizing the problem setting and presenting a structured taxonomy of representative methods along four key technical dimensions: external knowledge-based approaches that leverage semantic priors to alleviate the inherent modality gap between visual and textual data.
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