|
| | | | |
| | | |
| Stat |
Members: 3665
Articles: 2'599'751
Articles rated: 2609
17 January 2025 |
|
| | | | |
|
Article overview
| |
|
|
Robust Domain Adaptive Object Detection with Unified Multi-Granularity Alignment | | Libo Zhang
; Wenzhang Zhou
; Heng Fan
; Tiejian Luo
; Haibin Ling
; | | Date: |
1 Jan 2023 | | Abstract: | Domain adaptive detection aims to improve the generalization of detectors on
target domain. To reduce discrepancy in feature distributions between two
domains, recent approaches achieve domain adaption through feature alignment in
different granularities via adversarial learning. However, they neglect the
relationship between multiple granularities and different features in
alignment, degrading detection. Addressing this, we introduce a unified
multi-granularity alignment (MGA)-based detection framework for
domain-invariant feature learning. The key is to encode the dependencies across
different granularities including pixel-, instance-, and category-levels
simultaneously to align two domains. Specifically, based on pixel-level
features, we first develop an omni-scale gated fusion (OSGF) module to
aggregate discriminative representations of instances with scale-aware
convolutions, leading to robust multi-scale detection. Besides, we introduce
multi-granularity discriminators to identify where, either source or target
domains, different granularities of samples come from. Note that, MGA not only
leverages instance discriminability in different categories but also exploits
category consistency between two domains for detection. Furthermore, we present
an adaptive exponential moving average (AEMA) strategy that explores model
assessments for model update to improve pseudo labels and alleviate local
misalignment problem, boosting detection robustness. Extensive experiments on
multiple domain adaption scenarios validate the superiority of MGA over other
approaches on FCOS and Faster R-CNN detectors. Code will be released at
this https URL. | | Source: | arXiv, 2301.00371 | | Services: | Forum | Review | PDF | Favorites | |
|
|
No review found.
Did you like this article?
Note: answers to reviews or questions about the article must be posted in the forum section.
Authors are not allowed to review their own article. They can use the forum section.
|
| |
|
|
|
REGISTER