UniParser: Multi-Human Parsing with Unified Correlation Representation Learning

• URL: http://arxiv.org/abs/2310.08984v2
• Date: Mon, 20 May 2024 01:09:39 GMT
• Title: UniParser: Multi-Human Parsing with Unified Correlation Representation Learning
• Authors: Jiaming Chu, Lei Jin, Junliang Xing, Jian Zhao,
• Abstract summary: This paper introduces Uni, which integrates instance-level and category-level representations in three key aspects. Uni achieves 49.3% AP on MHPv2.0 and 60.4% AP on CIHP.
• Score: 18.372259148872228
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-human parsing is an image segmentation task necessitating both instance-level and fine-grained category-level information. However, prior research has typically processed these two types of information through separate branches and distinct output formats, leading to inefficient and redundant frameworks. This paper introduces UniParser, which integrates instance-level and category-level representations in three key aspects: 1) we propose a unified correlation representation learning approach, allowing our network to learn instance and category features within the cosine space; 2) we unify the form of outputs of each modules as pixel-level segmentation results while supervising instance and category features using a homogeneous label accompanied by an auxiliary loss; and 3) we design a joint optimization procedure to fuse instance and category representations. By virtual of unifying instance-level and category-level output, UniParser circumvents manually designed post-processing techniques and surpasses state-of-the-art methods, achieving 49.3% AP on MHPv2.0 and 60.4% AP on CIHP. We will release our source code, pretrained models, and online demos to facilitate future studies.

Related papers

• Multi-Modal Classifiers for Open-Vocabulary Object Detection [104.77331131447541]
  The goal of this paper is open-vocabulary object detection (OVOD) We adopt a standard two-stage object detector architecture. We explore three ways via: language descriptions, image exemplars, or a combination of the two.
  arXiv  Detail & Related papers  (2023-06-08T18:31:56Z)
• AIMS: All-Inclusive Multi-Level Segmentation [93.5041381700744]
  We propose a new task, All-Inclusive Multi-Level (AIMS), which segments visual regions into three levels: part, entity, and relation. We also build a unified AIMS model through multi-dataset multi-task training to address the two major challenges of annotation inconsistency and task correlation.
  arXiv  Detail & Related papers  (2023-05-28T16:28:49Z)
• MIANet: Aggregating Unbiased Instance and General Information for Few-Shot Semantic Segmentation [6.053853367809978]
  Existing few-shot segmentation methods are based on the meta-learning strategy and extract instance knowledge from a support set. We propose a multi-information aggregation network (MIANet) that effectively leverages the general knowledge, i.e., semantic word embeddings, and instance information for accurate segmentation. Experiments on PASCAL-5i and COCO-20i show that MIANet yields superior performance and set a new state-of-the-art.
  arXiv  Detail & Related papers  (2023-05-23T09:36:27Z)
• Part-guided Relational Transformers for Fine-grained Visual Recognition [59.20531172172135]
  We propose a framework to learn the discriminative part features and explore correlations with a feature transformation module. Our proposed approach does not rely on additional part branches and reaches state-the-of-art performance on 3-of-the-level object recognition.
  arXiv  Detail & Related papers  (2022-12-28T03:45:56Z)
• Part-aware Prototypical Graph Network for One-shot Skeleton-based Action Recognition [57.86960990337986]
  One-shot skeleton-based action recognition poses unique challenges in learning transferable representation from base classes to novel classes. We propose a part-aware prototypical representation for one-shot skeleton-based action recognition. We demonstrate the effectiveness of our method on two public skeleton-based action recognition datasets.
  arXiv  Detail & Related papers  (2022-08-19T04:54:56Z)
• CAD: Co-Adapting Discriminative Features for Improved Few-Shot Classification [11.894289991529496]
  Few-shot classification is a challenging problem that aims to learn a model that can adapt to unseen classes given a few labeled samples. Recent approaches pre-train a feature extractor, and then fine-tune for episodic meta-learning. We propose a strategy to cross-attend and re-weight discriminative features for few-shot classification.
  arXiv  Detail & Related papers  (2022-03-25T06:14:51Z)
• Learning Category- and Instance-Aware Pixel Embedding for Fast Panoptic Segmentation [47.26296379603003]
  Panoptic segmentation (PS) is a complex scene understanding task. PS results are simply derived by assigning each pixel to a detected instance or a stuff class. Our method not only demonstrates fast inference speed but also the first one-stage method to achieve comparable performance to two-stage methods.
  arXiv  Detail & Related papers  (2020-09-28T14:07:50Z)
• BriNet: Towards Bridging the Intra-class and Inter-class Gaps in One-Shot Segmentation [84.2925550033094]
  Few-shot segmentation focuses on the generalization of models to segment unseen object instances with limited training samples. We propose a framework, BriNet, to bridge the gaps between the extracted features of the query and support images. The effectiveness of our framework is demonstrated by experimental results, which outperforms other competitive methods.
  arXiv  Detail & Related papers  (2020-08-14T07:45:50Z)
• Commonality-Parsing Network across Shape and Appearance for Partially Supervised Instance Segmentation [71.59275788106622]
  We propose to learn the underlying class-agnostic commonalities that can be generalized from mask-annotated categories to novel categories. Our model significantly outperforms the state-of-the-art methods on both partially supervised setting and few-shot setting for instance segmentation on COCO dataset.
  arXiv  Detail & Related papers  (2020-07-24T07:23:44Z)

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.