Region-aware Distribution Contrast: A Novel Approach to Multi-Task
  Partially Supervised Learning
Meixuan Li Tianyu Li Guoqing Wang Peng Wang Yang Yang Heng Tao Shen
In this study, we address the intricate challenge of multi-task dense
prediction, encompassing tasks such as semantic segmentation, depth estimation,
and surface normal estimation, particularly when dealing with partially
annotated data (MTPSL). The complexity arises from the absence of complete task
labels for each training image. Given the inter-related nature of these
pixel-wise dense tasks, our focus is on mining and capturing cross-task
relationships. Existing solutions typically rely on learning global image
representations for global cross-task image matching, imposing constraints
that, unfortunately, sacrifice the finer structures within the images.
Attempting local matching as a remedy faces hurdles due to the lack of precise
region supervision, making local alignment a challenging endeavor. The
introduction of Segment Anything Model (SAM) sheds light on addressing local
alignment challenges by providing free and high-quality solutions for region
detection. Leveraging SAM-detected regions, the subsequent challenge lies in
aligning the representations within these regions. Diverging from conventional
methods that directly learn a monolithic image representation, our proposal
involves modeling region-wise representations using Gaussian Distributions.
Aligning these distributions between corresponding regions from different tasks
imparts higher flexibility and capacity to capture intra-region structures,
accommodating a broader range of tasks. This innovative approach significantly
enhances our ability to effectively capture cross-task relationships, resulting
in improved overall performance in partially supervised multi-task dense
prediction scenarios. Extensive experiments conducted on two widely used
benchmarks underscore the superior effectiveness of our proposed method,
showcasing state-of-the-art performance even when compared to fully supervised
methods.