Abstract:
Previous works on the task of weakly supervised phrase grounding (WSG) rely heavily on object detectors providing RoIs for the localization. However, such methods cannot ...Show MoreMetadata
Abstract:
Previous works on the task of weakly supervised phrase grounding (WSG) rely heavily on object detectors providing RoIs for the localization. However, such methods cannot be applied effectively to real-world scenarios largely because that the detectors are trained with limited categories. In this paper, we propose a refinement-based approach to WSG through fine-tuning a detector-free phrase grounding model with a visual prompt. This visual prompt is extracted from the text-related representations in CLIP. Furthermore, we combine the visual prompt with learnable features and then fine-tune the grounding network. Our experimental results significantly outperform state-of-the-art methods on the WSG task and shows the effectiveness of our method.
Published in: ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Date of Conference: 14-19 April 2024
Date Added to IEEE Xplore: 18 March 2024
ISBN Information: