Related papers: Parsing Objects at a Finer Granularity: A Survey

Parsing Objects at a Finer Granularity: A Survey

URL: http://arxiv.org/abs/2212.13693v1
Date: Wed, 28 Dec 2022 04:20:10 GMT
Title: Parsing Objects at a Finer Granularity: A Survey
Authors: Yifan Zhao, Jia Li, Yonghong Tian
Abstract summary: Fine-grained visual parsing is important in many real-world applications, e.g., agriculture, remote sensing, and space technologies. Predominant research efforts tackle these fine-grained sub-tasks following different paradigms. We conduct an in-depth study of the advanced work from a new perspective of learning the part relationship.
Score: 54.72819146263311
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Fine-grained visual parsing, including fine-grained part segmentation and fine-grained object recognition, has attracted considerable critical attention due to its importance in many real-world applications, e.g., agriculture, remote sensing, and space technologies. Predominant research efforts tackle these fine-grained sub-tasks following different paradigms, while the inherent relations between these tasks are neglected. Moreover, given most of the research remains fragmented, we conduct an in-depth study of the advanced work from a new perspective of learning the part relationship. In this perspective, we first consolidate recent research and benchmark syntheses with new taxonomies. Based on this consolidation, we revisit the universal challenges in fine-grained part segmentation and recognition tasks and propose new solutions by part relationship learning for these important challenges. Furthermore, we conclude several promising lines of research in fine-grained visual parsing for future research.

Related papers

Unsupervised Object Discovery: A Comprehensive Survey and Unified Taxonomy [6.346947904159397]
Unsupervised object discovery is commonly interpreted as the task of localizing and/or categorizing objects in visual data without the need for labeled examples. This survey conducts an in-depth exploration of the existing approaches and systematically categorizes this compendium based on the tasks addressed and the families of techniques employed. We present an overview of common datasets and metrics, highlighting the challenges of comparing methods due to varying evaluation protocols.
arXiv Detail & Related papers (2024-10-30T21:22:48Z)
Recent Advances in Hate Speech Moderation: Multimodality and the Role of Large Models [52.24001776263608]
This comprehensive survey delves into the recent strides in HS moderation. We highlight the burgeoning role of large language models (LLMs) and large multimodal models (LMMs) We identify existing gaps in research, particularly in the context of underrepresented languages and cultures.
arXiv Detail & Related papers (2024-01-30T03:51:44Z)
The What, Why, and How of Context Length Extension Techniques in Large Language Models -- A Detailed Survey [6.516561905186376]
The advent of Large Language Models (LLMs) represents a notable breakthrough in Natural Language Processing (NLP) We study the inherent challenges associated with extending context length and present an organized overview of the existing strategies employed by researchers. We explore whether there is a consensus within the research community regarding evaluation standards and identify areas where further agreement is needed.
arXiv Detail & Related papers (2024-01-15T18:07:21Z)
Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark [55.898771405172155]
Federated learning has emerged as a promising paradigm for privacy-preserving collaboration among different parties. We provide a systematic overview of the important and recent developments of research on federated learning.
arXiv Detail & Related papers (2023-11-12T06:32:30Z)
A Comprehensive Survey of Forgetting in Deep Learning Beyond Continual Learning [58.107474025048866]
Forgetting refers to the loss or deterioration of previously acquired knowledge. Forgetting is a prevalent phenomenon observed in various other research domains within deep learning.
arXiv Detail & Related papers (2023-07-16T16:27:58Z)
A Survey on Semi-Supervised Semantic Segmentation [3.5450828190071655]
This study provides an overview of the current state of the art in semi-supervised semantic segmentation. It includes an experimentation with a variety of models representing all the categories of the taxonomy on the most widely used becnhmark datasets in the literature.
arXiv Detail & Related papers (2023-02-20T10:54:03Z)
Fine-Grained Image Analysis with Deep Learning: A Survey [146.22351342315233]
Fine-grained image analysis (FGIA) is a longstanding and fundamental problem in computer vision and pattern recognition. This paper attempts to re-define and broaden the field of FGIA by consolidating two fundamental fine-grained research areas -- fine-grained image recognition and fine-grained image retrieval.
arXiv Detail & Related papers (2021-11-11T09:43:56Z)
Positioning yourself in the maze of Neural Text Generation: A Task-Agnostic Survey [54.34370423151014]
This paper surveys the components of modeling approaches relaying task impacts across various generation tasks such as storytelling, summarization, translation etc. We present an abstraction of the imperative techniques with respect to learning paradigms, pretraining, modeling approaches, decoding and the key challenges outstanding in the field in each of them.
arXiv Detail & Related papers (2020-10-14T17:54:42Z)

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