**CornerNet-Squeeze Hourglass** is a convolutional neural network and object detection backbone used in the [CornerNet-Squeeze](https://paperswithcode.com/method/cornernet-squeeze) object detector. It uses a modified [hourglass module](https://paperswithcode.com/method/hourglass-module) that makes use of a [fire module](https://paperswithcode.com/method/fire-module): containing 1x1 convolutions and depthwise convolutions.

**Skim and Intensive Reading Model**, or **SIRM**, is a deep neural network for figuring out implied textual meaning. It consists of two main components, namely the skim reading component and intensive reading component. N-gram features are quickly extracted from the skim reading component, which is a combination of several convolutional neural networks, as skim (entire) information. An intensive reading component enables a hierarchical investigation for both local (sentence) and global (paragraph) representation, which encapsulates the current embedding and the contextual information with a dense connection.

SIRM

Read Beyond the Lines: Understanding the Implied Textual Meaning via a Skim and Intensive Reading Model

CornerNet-Squeeze Hourglass

CornerNet-Lite: Efficient Keypoint Based Object Detection

**ACER**, or **Actor Critic with Experience Replay**, is an actor-critic deep reinforcement learning agent with [experience replay](https://paperswithcode.com/method/experience-replay). It can be seen as an off-policy extension of [A3C](https://paperswithcode.com/method/a3c), where the off-policy estimator is made feasible by:

- Using [Retrace](https://paperswithcode.com/method/retrace) Q-value estimation.
- Using truncated importance sampling with bias correction.
- Using a trust region policy optimization method.
- Using a [stochastic dueling network](https://paperswithcode.com/method/stochastic-dueling-network) architecture.

Source	CornerNet-Lite: Efficient Keypoint Based Object Detection
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com