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Found 5 papers, 1 papers with code
Fuse & Calibrate: A bi-directional Vision-Language Guided Framework for Referring Image Segmentation
Referring Image Segmentation (RIS) aims to segment an object described in natural language from an image, with the main challenge being a text-to-pixel correlation.
Calibration & Reconstruction: Deep Integrated Language for Referring Image Segmentation
In this paper, we introduce CRFormer, a model that iteratively calibrates multi-modal features in the transformer decoder.
Beyond Literal Descriptions: Understanding and Locating Open-World Objects Aligned with Human Intentions
To promote classic VG towards human intention interpretation, we propose a new intention-driven visual grounding (IVG) task and build a large-scale IVG dataset termed IntentionVG with free-form intention expressions.
EAVL: Explicitly Align Vision and Language for Referring Image Segmentation
In previous approaches, fused vision-language features are directly fed into a decoder and pass through a convolution with a fixed kernel to obtain the result, which follows a similar pattern as traditional image segmentation.
MMNet: Multi-Mask Network for Referring Image Segmentation
However, this task is challenging due to the distinct data properties between text and image, and the randomness introduced by diverse objects and unrestricted language expression.
