**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.

**Hierarchical Transferability Calibration Network** (HTCN) is an adaptive object detector that hierarchically (local-region/image/instance) calibrates the transferability of feature representations for harmonizing transferability and discriminability. The proposed model consists of three components: (1) Importance Weighted Adversarial Training with input Interpolation (IWAT-I), which strengthens the global discriminability by re-weighting the interpolated image-level features; (2) Context-aware Instance-Level Alignment (CILA) module, which enhances the local discriminability by capturing the complementary effect between the instance-level feature and the global context information for the instance-level feature alignment; (3) local feature masks that calibrate the local transferability to provide semantic guidance for the following discriminative pattern alignment.

HTCN

Harmonizing Transferability and Discriminability for Adapting Object Detectors

ENet Bottleneck

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

**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.

Source	ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com