**CoOp**, or **Context Optimization**, is an automated prompt engineering method that avoids manual prompt tuning by modeling context words with continuous vectors that are end-to-end learned from data. The context could be shared among all classes or designed to be class-specific. During training, we simply minimize the prediction error using the cross-entropy loss with respect to the learnable context vectors, while keeping the pre-trained parameters fixed. The gradients can be back-propagated all the way through the text encoder, distilling the rich knowledge encoded in the parameters for learning task-relevant context.

The **GAN Hinge Loss** is a hinge loss based loss function for [generative adversarial networks](https://paperswithcode.com/methods/category/generative-adversarial-networks):

$$ L\_{D} = -\mathbb{E}\_{\left(x, y\right)\sim{p}\_{data}}\left[\min\left(0, -1 + D\left(x, y\right)\right)\right] -\mathbb{E}\_{z\sim{p\_{z}}, y\sim{p\_{data}}}\left[\min\left(0, -1 - D\left(G\left(z\right), y\right)\right)\right] $$

$$ L\_{G} = -\mathbb{E}\_{z\sim{p\_{z}}, y\sim{p\_{data}}}D\left(G\left(z\right), y\right) $$

GAN Hinge Loss

Geometric GAN

CoOp

Learning to Prompt for Vision-Language Models

**State-Aware Tracker** is a pipeline for semi-supervised video object segmentation. It takes each target object as a tracklet, which not only makes the pipeline more efficient but also filters distractors to facilitate target modeling. For more stable and robust performance over video sequences, SAT gets awareness for each state and makes self-adaptation via two feedback loops. One loop assists SAT in generating more stable tracklets. The other loop helps to construct a more robust and holistic target representation.

Source	Learning to Prompt for Vision-Language Models
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com