**gCANS**, or **Global Coupled Adaptive Number of Shots**, is a variational quantum algorithm for stochastic gradient descent. It adaptively allocates shots for the measurement of each gradient component at each iteration. The optimizer uses a criterion for allocating shots that incorporates information about the overall scale of the shot cost for the iteration.

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.

MAVL

Class-agnostic Object Detection with Multi-modal Transformer

gCANS

Adaptive shot allocation for fast convergence in variational quantum algorithms

VisualBERT aims to reuse self-attention to implicitly align elements of the input text and regions in the input image. Visual embeddings are used to model images where the representations are represented by a bounding region in an image obtained from an object detector. These visual embeddings are constructed by summing three embeddings: 1) visual feature representation, 2) a segment embedding indicate whether it is an image embedding, and 3) position embedding. Essentially, image regions and language are combined with a Transformer to allow self-attention to discover implicit alignments between language and vision. VisualBERT is trained using COCO, which consists of images paired with captions. It is pre-trained using two objectives: masked language modeling objective and sentence-image prediction task. It can then be fine-tuned on different downstream tasks.

Source	Adaptive shot allocation for fast convergence in variational quantum algorithms
Year	2000
Data Source	Papers with Code archive (CC BY-SA)