Region-aware Distribution Contrast: A Novel Approach to Multi-Task Partially Supervised Learning

• URL: http://arxiv.org/abs/2403.10252v1
• Date: Fri, 15 Mar 2024 12:41:30 GMT
• Title: Region-aware Distribution Contrast: A Novel Approach to Multi-Task Partially Supervised Learning
• Authors: Meixuan Li, Tianyu Li, Guoqing Wang, Peng Wang, Yang Yang, Heng Tao Shen,
• Abstract summary: Multi-task dense prediction involves semantic segmentation, depth estimation, and surface normal estimation. Existing solutions typically rely on learning global image representations for global cross-task image matching. Our proposal involves modeling region-wise representations using Gaussian Distributions.
• Score: 50.88504784466931
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Beyond LLaVA-HD: Diving into High-Resolution Large Multimodal Models [44.437693135170576]
  We propose a new framework, LMM with Sophisticated Tasks, Local image compression, and Mixture of global Experts (SliME) We extract contextual information from the global view using a mixture of adapters, based on the observation that different adapters excel at different tasks. The proposed method achieves leading performance across various benchmarks with only 2 million training data.
  arXiv  Detail & Related papers  (2024-06-12T17:59:49Z)
• BLADE: Box-Level Supervised Amodal Segmentation through Directed Expansion [10.57956193654977]
  Box-level supervised amodal segmentation addresses this challenge by relying solely on ground truth bounding boxes and instance classes as supervision. We present a novel solution by introducing a directed expansion approach from visible masks to corresponding amodal masks. Our approach involves a hybrid end-to-end network based on the overlapping region - the area where different instances intersect.
  arXiv  Detail & Related papers  (2024-01-03T09:37:03Z)
• Distribution Matching for Multi-Task Learning of Classification Tasks: a Large-Scale Study on Faces & Beyond [62.406687088097605]
  Multi-Task Learning (MTL) is a framework, where multiple related tasks are learned jointly and benefit from a shared representation space. We show that MTL can be successful with classification tasks with little, or non-overlapping annotations. We propose a novel approach, where knowledge exchange is enabled between the tasks via distribution matching.
  arXiv  Detail & Related papers  (2024-01-02T14:18:11Z)
• Rotated Multi-Scale Interaction Network for Referring Remote Sensing Image Segmentation [63.15257949821558]
  Referring Remote Sensing Image (RRSIS) is a new challenge that combines computer vision and natural language processing. Traditional Referring Image (RIS) approaches have been impeded by the complex spatial scales and orientations found in aerial imagery. We introduce the Rotated Multi-Scale Interaction Network (RMSIN), an innovative approach designed for the unique demands of RRSIS.
  arXiv  Detail & Related papers  (2023-12-19T08:14:14Z)
• Progressive Feature Self-reinforcement for Weakly Supervised Semantic Segmentation [55.69128107473125]
  We propose a single-stage approach for Weakly Supervised Semantic (WSSS) with image-level labels. We adaptively partition the image content into deterministic regions (e.g., confident foreground and background) and uncertain regions (e.g., object boundaries and misclassified categories) for separate processing. Building upon this, we introduce a complementary self-enhancement method that constrains the semantic consistency between these confident regions and an augmented image with the same class labels.
  arXiv  Detail & Related papers  (2023-12-14T13:21:52Z)
• Region-Based Semantic Factorization in GANs [67.90498535507106]
  We present a highly efficient algorithm to factorize the latent semantics learned by Generative Adversarial Networks (GANs) concerning an arbitrary image region. Through an appropriately defined generalized Rayleigh quotient, we solve such a problem without any annotations or training. Experimental results on various state-of-the-art GAN models demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2022-02-19T17:46:02Z)
• Point-Level Region Contrast for Object Detection Pre-Training [147.47349344401806]
  We present point-level region contrast, a self-supervised pre-training approach for the task of object detection. Our approach performs contrastive learning by directly sampling individual point pairs from different regions. Compared to an aggregated representation per region, our approach is more robust to the change in input region quality.
  arXiv  Detail & Related papers  (2022-02-09T18:56:41Z)
• Empirical Study of Multi-Task Hourglass Model for Semantic Segmentation Task [0.7614628596146599]
  We propose to use a multi-task approach by complementing the semantic segmentation task with edge detection, semantic contour, and distance transform tasks. We demonstrate the effectiveness of learning in a multi-task setting for hourglass models in the Cityscapes, CamVid, and Freiburg Forest datasets.
  arXiv  Detail & Related papers  (2021-05-28T01:08:10Z)
• Spatially Consistent Representation Learning [12.120041613482558]
  We propose a spatially consistent representation learning algorithm (SCRL) for multi-object and location-specific tasks. We devise a novel self-supervised objective that tries to produce coherent spatial representations of a randomly cropped local region. On various downstream localization tasks with benchmark datasets, the proposed SCRL shows significant performance improvements.
  arXiv  Detail & Related papers  (2021-03-10T15:23:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.