Anticipatory Semantics with Bidirectional Guidance for Image Captioning

Producing captions that are not only grammatically fluent but also semantically faithful to visual content has long stood as a central problem at the junction of computer vision and natural language processing. Conventional encoder-decoder frameworks with attention modules, although powerful, typically confine the decoding process to a retrospective scope: every prediction is conditioned solely on historical tokens, thereby ignoring what future semantics may dictate. This retrospective bias hinders models from fully capturing scene-level coherence. To address this limitation, we introduce \textbf{FISRA} (Future-Infused Semantic Revision Architecture), a dual-pass attention paradigm that supplements training with anticipatory semantic signals. Specifically, FISRA first constructs a global caption hypothesis serving as a semantic scaffold and then refines the decoding trajectory through revision-oriented attention that aligns each step with both preceding context and projected continuations. This design enhances coherence by weaving forward-looking cues into the generative process. The framework is model-agnostic and integrates seamlessly with existing attention-based captioners. Empirical evaluations on MS-COCO reveal that FISRA consistently advances the state of the art, delivering 133.4 CIDEr-D on the Karpathy split and 131.6 CIDEr-D on the official evaluation server. These findings confirm that our approach significantly strengthens semantic alignment and alleviates the exposure bias endemic to unidirectional captioning systems.