**Video Panoptic Segmentation Network**, or **VPSNet**, is a model for video panoptic segmentation. On top of UPSNet, which is a method for image panoptic segmentation, VPSNet is designed to take an additional frame as the reference to correlate time information at two levels: pixel-level fusion and object-level tracking. To pick up the complementary feature points in the reference frame, a flow-based feature map alignment module is introduced along with an asymmetric attention block that computes similarities between the target and reference features to fuse them into one-frame shape. Additionally, to associate object instances across time, 
 an object track head is added which learns the correspondence between the instances in the target and reference frames based
on their RoI feature similarity.

CDCC-NET is a multi-task network that analyzes the detected counter region and predicts 9 outputs: eight float numbers referring to the corner positions (x0/w, y0/h, ... , x3/w, y3/h) and an array containing two float numbers regarding the probability of the counter being legible/operational or illegible/faulty.

CDCC-NET

Towards Image-based Automatic Meter Reading in Unconstrained Scenarios: A Robust and Efficient Approach

VPSNet

Video Panoptic Segmentation

**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)).

Source	Video Panoptic Segmentation
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com