Transformers in Action: Elevating Image Captioning with Dual-Objective Optimization
From Pixels to Perfect Phrases — Why Transformers Matter In image captioning, the Transformer architecture has emerged as a game-changer, capable of understanding intricate visual cues and translating them into context-aware sentences. Unlike recurrent networks that process sequences step-by-step, Transformers leverage self-attention to capture long-range dependencies in one shot. Yet, even the most advanced Transformers often fall prey to the loss–evaluation mismatch — producing captions that minimize cross-entropy loss but fail to impress human evaluators. This is where our Dual-Objective Optimization (DOO) framework steps in: pairing traditional loss minimization with BLEU score maximization to ensure captions are both technically precise and linguistically rich . Use Case: Disaster Scene Assessment Imagine a rescue team relying on an automated captioning system to describe drone images after an earthquake. Baseline Transformer Caption: "Buildings are damaged." (A...
