**Temporal ROI Align** is an operator for extracting features from other frames' feature maps for current frame proposals by utilizing feature similarity. Considering the features of the same object instance are highly similar among frames in a video, the proposed operator implicitly extracts the most similar ROI features from support frames feature map for target frame proposals based on feature similarity.

**ENet Bottleneck** is an image model block used in the [ENet](https://paperswithcode.com/method/enet) semantic segmentation architecture. Each block consists of three convolutional layers: a 1 × 1 projection that reduces the dimensionality, a main convolutional layer, and a 1 × 1 expansion. We place [Batch Normalization](https://paperswithcode.com/method/batch-normalization) and [PReLU](https://paperswithcode.com/method/prelu) between all convolutions. If the bottleneck is downsampling, a [max pooling](https://paperswithcode.com/method/max-pooling) layer is added to the main branch.
Also, the first 1 × 1 projection is replaced with a 2 × 2 [convolution](https://paperswithcode.com/method/convolution) with stride 2 in both dimensions. We zero pad the activations, to match the number of feature maps.

ENet Bottleneck

ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation

Temporal ROIAlign

Temporal RoI Align for Video Object Recognition

**Inception v2** is the second generation of Inception convolutional neural network architectures which notably uses [batch normalization](https://paperswithcode.com/method/batch-normalization). Other changes include dropping [dropout](https://paperswithcode.com/method/dropout) and removing [local response normalization](https://paperswithcode.com/method/local-response-normalization), due to the benefits of batch normalization.

Source	Temporal RoI Align for Video Object Recognition
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com