MTIVE: Multi-Task Image Verification Engine Using Vision-Language Models for E-commerce

Yu-Tong Cao, Vishnu Prabhakaran, Arunita Das, Purav Aggarwal, Anoop Saladi

Keywords: VLM, LLM, multi task learning, E-commerce

TL;DR: We present MTIVE, a comprehensive framework for adapting VLMs to multi-task e-commerce scenarios.

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.

Submission Type: Deployed

Submission Number: 512