Deep learning-based approaches have achieved great success in the semantic segmentation [24, 43, 2, 7, 42, 3, 17, 10], thanks to a large amount of fully annotated data. However, collecting large-scale accurate pixel-level annotations can be extremely time and cost consuming [6]. An appealing alternative is to use off-the-shelf simulators to render synthetic data for which groundtruth annotations are generated automatically [33, 34, 32]. Unfortunately, models trained purely on simulated data often fail to generalize to the real world due to thedomain shifts.

With a separately trained critic model that selects high-quality examples, we find that training on the localized commonsense corpus can successfully distill existing VL models to support a reference-as-input interface.

Convolutional neural networks (CNNs) haveso far been the de-facto model for visualdata. Recent workhasshownthat(Vision)Transformer models (ViT)can achieve comparable or even superior performance on image classification tasks.

Delete, retrieve, generate: Asimpleapproachtosentiment transfer.