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    <abstract>Vision-language models show promise for e-commerce automation but struggle with noisy real-world images and multi-task requirements. We introduce MTIVE, a curriculum learning framework that progressively adapts base models through three stages: continued pre-training on large-scale e-commerce datasets with contrastive learning and diverse dialogue templates, instruction tuning on synthetic data, and modular task-specific expert training. Our architecture uses frozen base weights with stacked LoRA adapters—shared modules for domain knowledge and lightweight task-specific experts—enabling continual learning without catastrophic forgetting. MTIVE outperforms open-source and proprietary baselines in both standard and continual learning settings.</abstract>
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%0 Conference Proceedings
%T MTIVE: Multi-Task Image Verification Engine Using Vision-Language Models for E-commerce
%A Cao, Yu-Tong
%A Prabhakaran, Vishnu
%A Das, Arunita
%A Aggarwal, Purav
%A Saladi, Anoop
%Y Li, Yunyao
%Y Rehm, Georg
%Y Tu, Mei
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-394-4
%F cao-etal-2026-mtive
%X Vision-language models show promise for e-commerce automation but struggle with noisy real-world images and multi-task requirements. We introduce MTIVE, a curriculum learning framework that progressively adapts base models through three stages: continued pre-training on large-scale e-commerce datasets with contrastive learning and diverse dialogue templates, instruction tuning on synthetic data, and modular task-specific expert training. Our architecture uses frozen base weights with stacked LoRA adapters—shared modules for domain knowledge and lightweight task-specific experts—enabling continual learning without catastrophic forgetting. MTIVE outperforms open-source and proprietary baselines in both standard and continual learning settings.
%U https://aclanthology.org/2026.acl-industry.143/
%P 2148-2159

Markdown (Informal)

[MTIVE: Multi-Task Image Verification Engine Using Vision-Language Models for E-commerce](https://aclanthology.org/2026.acl-industry.143/) (Cao et al., ACL 2026)

• MTIVE: Multi-Task Image Verification Engine Using Vision-Language Models for E-commerce (Cao et al., ACL 2026)

ACL

• Yu-Tong Cao, Vishnu Prabhakaran, Arunita Das, Purav Aggarwal, and Anoop Saladi. 2026. MTIVE: Multi-Task Image Verification Engine Using Vision-Language Models for E-commerce. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), pages 2148–2159, San Diego, California, USA. Association for Computational Linguistics.