TGNN[24]introduce3D-RESby extending the bounding box annotations of ScanRefer [5] to masks by incorporating the instance masks from ScanNet and proposed a two-stage pipeline. Further, 3D-STMN [65] proposed an end-to-end method that matches the text and superpoints to get the 3D segmentation of the target object directly.

Diffusion models (DMs) have recently gained attention with state-of-the-art performance in text-to-image synthesis.

Large-scale pre-training has shown great potential to enhance models on downstream tasks in vision and language. Developing similar techniques for scalp electroencephalogram (EEG) is suitable since unlabelled data is plentiful.

Understanding both the question and image, as well as modelling their interactions requires us to combine Computer Vision and NLP techniques. The problem is generally framed in terms of classification, such that the network learns to produce answers from a finite set of classes which facilitates training and evaluation.

Learning-based photometric stereo methods predict the surface normal either in a per-pixel or an all-pixel manner.

Spatial verification is a crucial technique for particular object retrieval.