Calibration & Reconstruction: Deep Integrated Language for Referring Image Segmentation
This work addresses the problem of language information distortion in transformer-based models for referring image segmentation, which is an incremental improvement in a domain-specific task.
The paper tackles the challenge of fine-grained semantic information propagation in referring image segmentation by introducing CRFormer, which iteratively calibrates multi-modal features and includes a language reconstruction module, achieving superior performance on RefCOCO, RefCOCO+, and G-Ref datasets compared to state-of-the-art methods.
Referring image segmentation aims to segment an object referred to by natural language expression from an image. The primary challenge lies in the efficient propagation of fine-grained semantic information from textual features to visual features. Many recent works utilize a Transformer to address this challenge. However, conventional transformer decoders can distort linguistic information with deeper layers, leading to suboptimal results. In this paper, we introduce CRFormer, a model that iteratively calibrates multi-modal features in the transformer decoder. We start by generating language queries using vision features, emphasizing different aspects of the input language. Then, we propose a novel Calibration Decoder (CDec) wherein the multi-modal features can iteratively calibrated by the input language features. In the Calibration Decoder, we use the output of each decoder layer and the original language features to generate new queries for continuous calibration, which gradually updates the language features. Based on CDec, we introduce a Language Reconstruction Module and a reconstruction loss. This module leverages queries from the final layer of the decoder to reconstruct the input language and compute the reconstruction loss. This can further prevent the language information from being lost or distorted. Our experiments consistently show the superior performance of our approach across RefCOCO, RefCOCO+, and G-Ref datasets compared to state-of-the-art methods.