**STAC** is a semi-supervised framework for visual object detection along with a data augmentation strategy. STAC deploys highly confident pseudo labels of localized objects from an unlabeled image and updates the model by enforcing consistency via strong augmentations. We generate pseudo labels (i.e., bounding boxes and their class labels) for unlabeled data using test-time inference, including NMS , of the teacher model trained with labeled data. We then compute unsupervised loss with respect to pseudo labels whose confidence scores are above a threshold $\tau$ . The strong augmentations are applied for augmentation consistency during the model training. Target boxes are augmented when global geometric transformations are used.

The **BP-Transformer (BPT)** is a type of [Transformer](https://paperswithcode.com/method/transformer) that is motivated by the need to find a better balance between capability and computational complexity for self-attention. The architecture partitions the input sequence into different multi-scale spans via binary partitioning (BP). It incorporates an inductive bias of attending the context information from fine-grain to coarse-grain as the relative distance increases. The farther the context information is, the coarser its representation is.
BPT can be regard as graph neural network, whose nodes are the multi-scale spans. A token node can attend the smaller-scale span for the closer context and the larger-scale span for the longer distance context. The representations of nodes are updated with [Graph Self-Attention](https://paperswithcode.com/method/graph-self-attention).

BP-Transformer

BP-Transformer: Modelling Long-Range Context via Binary Partitioning

STAC

A Simple Semi-Supervised Learning Framework for Object Detection

**Convolutional Block Attention Module (CBAM)** is an attention module for convolutional neural networks. Given an intermediate feature map, the module sequentially infers attention maps along two separate dimensions, channel and spatial, then the attention maps are multiplied to the input feature map for adaptive feature refinement.

Given an intermediate feature map $\mathbf{F} \in \mathbb{R}^{C×H×W}$ as input, CBAM sequentially infers a 1D channel attention map $\mathbf{M}\_{c} \in \mathbb{R}^{C×1×1}$ and a 2D spatial attention map $\mathbf{M}\_{s} \in \mathbb{R}^{1×H×W}$. The overall attention process can be summarized as:

$$ \mathbf{F}' = \mathbf{M}\_{c}\left(\mathbf{F}\right) \otimes \mathbf{F} $$

$$ \mathbf{F}'' = \mathbf{M}\_{s}\left(\mathbf{F'}\right) \otimes \mathbf{F'} $$

During multiplication, the attention values are broadcasted (copied) accordingly: channel attention values are broadcasted along the spatial dimension, and vice versa. $\mathbf{F}''$ is the final refined
output.

Source	A Simple Semi-Supervised Learning Framework for Object Detection
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com