Object proposal generation is a preprocessing technique that has been widely used in current object detection pipelines to guide the search of objects and avoid exhaustive sliding window search across images.
DeepMask is an object proposal algorithm based on a convolutional neural network. Given an input image patch, DeepMask generates a class-agnostic mask and an associated score which estimates the likelihood of the patch fully containing a centered object (without any notion of an object category). The core of the model is a ConvNet which jointly predicts the mask and the object score. A large part of the network is shared between those two tasks: only the last few network layers are specialized for separately outputting a mask and score prediction.
Multiscale Attention ViT with Late fusion (MAVL) is a multi-modal network, trained with aligned image-text pairs, capable of performing targeted detection using human understandable natural language text queries. It utilizes multi-scale image features and uses deformable convolutions with late multi-modal fusion. The authors demonstrate excellent ability of MAVL as class-agnostic object detector when queried using general human understandable natural language command, such as "all objects", "all entities", etc.