{"url":"/method/htcn","slug":"htcn","name":"HTCN","full_name":"Hierarchical Transferability Calibration Network","full_name_withheld":false,"description_markdown":"**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.","description_state":"present","introduced_year":null,"introduced_by":{"title":"Harmonizing Transferability and Discriminability for Adapting Object Detectors","paper":"/paper/harmonizing-transferability-and","first_author":"Chaoqi Chen","n_authors":5,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/harmonizing-transferability-and"},"source":{"url":"https://arxiv.org/abs/2003.06297v1","title":"Harmonizing Transferability and Discriminability for Adapting Object Detectors","url_on_a_paper_host":true},"code_snippet_url":null,"code_snippet_url_on_a_code_host":false,"categories":[{"area":"Computer Vision","area_id":"computer-vision","collection":"Object Detection Models","url":"/methods/category/object-detection-models","pwc_aliases":[]}],"n_papers_tagged":4,"archive_num_papers":4,"papers_newest_first":[{"paper":"/paper/heterogeneous-tri-stream-clustering-network","title":"Heterogeneous Tri-stream Clustering Network","date":"2023-01-11","arxiv_id":"2301.04451","n_code_links":1,"syntology":null},{"paper":"/paper/hierarchic-temporal-convolutional-network","title":"Hierarchic Temporal Convolutional Network With Cross-Domain Encoder for Music Source Separation","date":"2022-06-30","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/cross-enhancement-transformer-for-action","title":"Cross-Enhancement Transformer for Action Segmentation","date":"2022-05-19","arxiv_id":"2205.09445","n_code_links":1,"syntology":null},{"paper":"/paper/harmonizing-transferability-and","title":"Harmonizing Transferability and Discriminability for Adapting Object Detectors","date":"2020-03-13","arxiv_id":"2003.06297","n_code_links":1,"syntology":null}],"papers_shown":4,"tasks":[{"task":"/task/action-segmentation","name":"Action Segmentation","papers":1},{"task":"/task/audio-source-separation","name":"Audio Source Separation","papers":1},{"task":"/task/clustering","name":"Clustering","papers":1},{"task":"/task/contrastive-learning","name":"Contrastive Learning","papers":1},{"task":"/task/decoder","name":"Decoder","papers":1},{"task":"/task/deep-clustering","name":"Deep Clustering","papers":1},{"task":"/task/music-source-separation","name":"Music Source Separation","papers":1},{"task":"/task/object","name":"Object","papers":1},{"task":"/task/object-detection","name":"Object Detection","papers":1},{"task":"/task/segmentation","name":"Segmentation","papers":1},{"task":"/task/time-series-1","name":"Time Series","papers":1},{"task":"/task/time-series","name":"Time Series Analysis","papers":1},{"task":"/task/weakly-supervised-object-detection","name":"Weakly Supervised Object Detection","papers":1},{"task":"/task/object-detection-1","name":"object-detection","papers":1}],"tasks_shown":14,"n_tasks":14,"usage_by_year":[{"year":"2020","papers":1},{"year":"2022","papers":2},{"year":"2023","papers":1}],"row_source":"methods_table","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/htcn"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}