Segment anything in high quality
Abstract The recent Segment Anything Model (SAM) represents a big leap in scaling up
segmentation models, allowing for powerful zero-shot capabilities and flexible prompting …
segmentation models, allowing for powerful zero-shot capabilities and flexible prompting …
Side adapter network for open-vocabulary semantic segmentation
This paper presents a new framework for open-vocabulary semantic segmentation with the
pre-trained vision-language model, named SAN. Our approach models the semantic …
pre-trained vision-language model, named SAN. Our approach models the semantic …
Revisiting class-incremental learning with pre-trained models: Generalizability and adaptivity are all you need
Class-incremental learning (CIL) aims to adapt to emerging new classes without forgetting
old ones. Traditional CIL models are trained from scratch to continually acquire knowledge …
old ones. Traditional CIL models are trained from scratch to continually acquire knowledge …
Vision-language models for vision tasks: A survey
Most visual recognition studies rely heavily on crowd-labelled data in deep neural networks
(DNNs) training, and they usually train a DNN for each single visual recognition task …
(DNNs) training, and they usually train a DNN for each single visual recognition task …
Maple: Multi-modal prompt learning
Pre-trained vision-language (VL) models such as CLIP have shown excellent generalization
ability to downstream tasks. However, they are sensitive to the choice of input text prompts …
ability to downstream tasks. However, they are sensitive to the choice of input text prompts …