Methods › Computer Vision › Object Detection Models › HTCN

Hierarchical Transferability Calibration Network

HTCN

4 papers tagged archive 2025-07-28

Introduced by Chaoqi Chen et al. in Harmonizing Transferability and Discriminability for Adapting Object Detectors

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

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.

PaperSource

Papers archive 2025-07-28

4 shown of 4, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

14 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Action Segmentation1
Audio Source Separation1
Clustering1
Contrastive Learning1
Decoder1
Deep Clustering1
Music Source Separation1
Object1
Object Detection1
Segmentation1
Time Series1
Time Series Analysis1
Weakly Supervised Object Detection1
object-detection1

Usage over time archive 2025-07-28

Papers per year tagged with HTCN: 2020 to 2023, peak 2 2 0 2020: 1 paper 2020 2021: 0 papers 2021 2022: 2 papers 2022 2023: 1 paper 2023
Papers per year the archive tags with this method, by the paper's archive date (4 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Object Detection Models

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections