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Current one-stage methods for visual grounding encode the language query asone holistic sentence embedding before fusion with visual feature. Such aformulation does not treat each word of a query sentence on par when modelinglanguage to visual attention, therefore prone to neglect words which are lessimportant for sentence embedding but critical for visual grounding. In thispaper we propose Word2Pix: a one-stage visual grounding network based onencoder-decoder transformer architecture that enables learning for textual tovisual feature correspondence via word to pixel attention. The embedding ofeach word from the query sentence is treated alike by attending to visualpixels individually instead of single holistic sentence embedding. In this way,each word is given equivalent opportunity to adjust the language to visionattention towards the referent target through multiple stacks of transformerdecoder layers. We conduct the experiments on RefCOCO, RefCOCO+ and RefCOCOgdatasets and the proposed Word2Pix outperforms existing one-stage methods by anotable margin. The results obtained also show that Word2Pix surpassestwo-stage visual grounding models, while at the same time keeping the merits ofone-stage paradigm namely end-to-end training and real-time inference speedintact.

Word2Pix: Word to Pixel Cross Attention Transformer in Visual Grounding