Object Detection Models
Hierarchical Transferability Calibration Network
Introduced by Chen et al. in Harmonizing Transferability and Discriminability for Adapting Object DetectorsHierarchical 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.
Source: Harmonizing Transferability and Discriminability for Adapting Object Detectors
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Clustering | 1 | 12.50% |
| Deep Clustering | 1 | 12.50% |
| Audio Source Separation | 1 | 12.50% |
| Music Source Separation | 1 | 12.50% |
| Time Series Analysis | 1 | 12.50% |
| Action Segmentation | 1 | 12.50% |
| Object Detection | 1 | 12.50% |
| Weakly Supervised Object Detection | 1 | 12.50% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
