AgriCLIP: Adapting CLIP for Agriculture and Livestock via Domain-Specialized Cross-Model Alignment

Challenge:	Recent studies have addressed this problem by building domain-specialized image-text data.
Approach:	They propose a vision-language foundational model dedicated to agriculture and livestock . they propose combining contrastive and self-supervised learning to learn fine-grained features .
Outcome:	The proposed model achieves 9.07% gain over standard CLIP training on 20 tasks.

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Overview

Challenge

Approach

Outcome

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Haonan Wang, Minbin Huang, Runhui Huang, Lanqing Hong, Hang Xu, Tianyang Hu, Xiaodan Liang, Zhenguo Li, Hong Cheng, Kenji Kawaguchi

Language Models

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Label Agnostic Pre-training for Zero-shot Text Classification (2023.findings-acl)

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Christopher Clarke, Yuzhao Heng, Yiping Kang, Krisztian Flautner, Lingjia Tang, Jason Mars

Language Models

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Tianlu Zheng, Yifan Zhang, Xiang An, Ziyong Feng, Kaicheng Yang, Qichuan Ding

Semantic Text Processing

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ASDOT: Any-Shot Data-to-Text Generation with Pretrained Language Models (2022.findings-emnlp)

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Jiannan Xiang, Zhengzhong Liu, Yucheng Zhou, Eric Xing, Zhiting Hu

Text Generation

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Yongjin Yang, Jongwoo Ko, Se-Young Yun

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RecBase: Generative Foundation Model Pretraining for Zero-Shot Recommendation (2025.emnlp-main)

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Language Models

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microCLIP: Unsupervised CLIP Adaptation via Coarse-Fine Token Fusion for Fine-Grained Image Classification (2026.findings-acl)

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Sathira Silva, Eman Ali, Chetan Arora, Muhammad Haris Khan

Information Retrieval

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RWKV-CLIP: A Robust Vision-Language Representation Learner (2024.emnlp-main)

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Tiancheng Gu, Kaicheng Yang, Xiang An, Ziyong Feng, Dongnan Liu, Weidong Cai, Jiankang Deng

Semantic Text Processing

Challenge:	Using large image-text datasets, large-scale image-data sets have been used for visionlanguage pre-training.
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Retrieval-enriched zero-shot image classification in low-resource domains (2024.emnlp-main)

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Nicola Dall’Asen, Yiming Wang, Enrico Fini, Elisa Ricci

Information Retrieval

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