**Prediction-aware One-To-One**, or **POTO**, is an assignment rule for object detection which dynamically assigns the foreground samples according to the quality of classification and regression simultaneously.

**Greedy Policy Search** (GPS) is a simple algorithm that learns a policy for test-time data augmentation based on the predictive performance on a validation set. GPS starts with an empty policy and builds it in an iterative fashion. Each step selects a sub-policy that provides the largest improvement in calibrated log-likelihood of ensemble predictions and adds it to the current policy.

Greedy Policy Search: A Simple Baseline for Learnable Test-Time Augmentation

POTO

End-to-End Object Detection with Fully Convolutional Network

**TaBERT** is a pretrained language model (LM) that jointly learns representations for natural language sentences and (semi-)structured tables. TaBERT is trained on a large corpus of 26 million tables and their English contexts. 

In summary, TaBERT's process for learning representations for NL sentences is as follows: Given an utterance $u$ and a table $T$, TaBERT first creates a content snapshot of $T$. This snapshot consists of sampled rows that summarize the information in $T$ most relevant to the input utterance. The model then linearizes each row in the snapshot, concatenates each linearized row with the utterance, and uses the concatenated string as input to a Transformer model, which outputs row-wise encoding vectors of utterance tokens and cells. The encodings for all the rows in the snapshot are fed into a series of vertical self-attention layers, where a cell representation (or an utterance token representation) is computed by attending to vertically-aligned vectors of the same column (or the same NL token). Finally, representations for each utterance token and column are generated from a pooling layer.

Source	End-to-End Object Detection with Fully Convolutional Network
Year	2000
Data Source	Papers with Code archive (CC BY-SA)