VMFormer: Visual Clues-Guided Multi-Stage Transformer for Image Captioning

Abstract

Transformer-based models have significantly advanced image captioning through self-attention mechanisms and parallel computation. However, existing methods typically adopt teacher-forcing strategies during training by conditioning the decoder exclusively on ground-truth tokens, whereas at inference, captions are generated autoregressively based solely on previously predicted tokens. Such discrepancy between training and inference conditions leads to a progressive accumulation of prediction errors, resulting in captions that deviate significantly from the visual content. While scheduled sampling strategies mitigate this issue, directly integrating them disrupts Transformer parallelism and overlooks visual saliency differences. To tackle these limitations, we present VMFormer, a multi-stage decoding Transformer featuring a Visual-Aware Scheduled Sampling (VASS) module that bridges this gap through two key innovations: 1) A two-stage decoding scheme where an initial self-study stage generates candidate tokens, followed by a hybrid stage dynamically blending ground-truth references with predictions via a visual clues controlled gate. 2) A cognitive-inspired prioritization mechanism that retains visual keywords (nouns/verbs/attributes) in early training phases before transitioning to linguistic refinements, mirroring human captioning patterns. Crucially, the VASS module preserves the parallel computational strengths of Transformer architectures and is designed as a plug-and-play component, readily adaptable to Transformer-based captioning models. Experiments on challenging MSCOCO achieve 142.2 CIDEr. We extend VMFormer to video captioning and demonstrate consistent improvements on MSRVTT and MSVD datasets.