**OA-Loss** reduces the domain gap between the original and augmented images. OA-Loss enables the model to learn semantic relations of foreground and background instances from multi-domain.

**SEED** (Scalable, Efficient, Deep-RL) is a scalable reinforcement learning agent. It utilizes an architecture that features centralized inference and an optimized communication layer. SEED adopts two state of the art distributed algorithms, [IMPALA](https://paperswithcode.com/method/impala)/[V-trace](https://paperswithcode.com/method/v-trace) (policy gradients) and R2D2 ([Q-learning](https://paperswithcode.com/method/q-learning)).

SEED RL

SEED RL: Scalable and Efficient Deep-RL with Accelerated Central Inference

OA-Loss

Object-Aware Domain Generalization for Object Detection

A **Bottleneck Transformer Block** is a block used in [Bottleneck Transformers](https://www.paperswithcode.com/method/bottleneck-transformer) that replaces the spatial 3 × 3 [convolution](https://paperswithcode.com/method/convolution) layer in a [Residual Block](https://paperswithcode.com/method/residual-block) with Multi-Head Self-Attention (MHSA).

Source	Object-Aware Domain Generalization for Object Detection
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com