To make ROSETTA automatically determine which experience is available and useful, a prototypical task correlation guided Gating Diversity Controller(GDC) is introduced to adaptively adjust the diversity of gates for the new task based on class-specific prototypes.
Fine-tuning from pre-trained ImageNet models has been a simple, effective, and popular approach for various computer vision tasks.
Directly performing cross-attention may aggregate these features from support to query and bias the query features.
Ranked #9 on Few-Shot Semantic Segmentation on PASCAL-5i (1-Shot)
For object detection, the well-established classification and regression loss functions have been carefully designed by considering diverse learning challenges.
The resulting model zoo is more training efficient than SOTA NAS models, e. g. 6x faster than RegNetY-16GF, and 1. 7x faster than EfficientNetB3.
Panoptic segmentation that unifies instance segmentation and semantic segmentation has recently attracted increasing attention.
Ranked #16 on Panoptic Segmentation on COCO test-dev
In this work, we propose an efficient, cooperative and highly automated framework to simultaneously search for all main components including backbone, segmentation branches, and feature fusion module in a unified panoptic segmentation pipeline based on the prevailing one-shot Network Architecture Search (NAS) paradigm.
We introduce a Bidirectional Graph Reasoning Network (BGRNet), which incorporates graph structure into the conventional panoptic segmentation network to mine the intra-modular and intermodular relations within and between foreground things and background stuff classes.