Task-Specific Data Augmentation and Inference Processing for VIPriors Instance Segmentation Challenge

2022-11-21 · Bo Yan, XingRan Zhao, Yadong Li, Hongbin Wang

Instance segmentation is applied widely in image editing, image analysis and autonomous driving, etc. However, insufficient data is a common problem in practical applications. The Visual Inductive Priors(VIPriors) Instance Segmentation Challenge has focused on this problem. VIPriors for Data-Efficient Computer Vision Challenges ask competitors to train models from scratch in a data-deficient setting, but there are some visual inductive priors that can be used. In order to address the VIPriors instance segmentation problem, we designed a Task-Specific Data Augmentation(TS-DA) strategy and Inference Processing(TS-IP) strategy. The main purpose of task-specific data augmentation strategy is to tackle the data-deficient problem. And in order to make the most of visual inductive priors, we designed a task-specific inference processing strategy. We demonstrate the applicability of proposed method on VIPriors Instance Segmentation Challenge. The segmentation model applied is Hybrid Task Cascade based detector on the Swin-Base based CBNetV2 backbone. Experimental results demonstrate that proposed method can achieve a competitive result on the test set of 2022 VIPriors Instance Segmentation Challenge, with 0.531 AP@0.50:0.95.

📄 PDF Abstract BibTeX arXiv:2211.11282

Code (0)

등록된 구현이 없습니다.

Tasks

Autonomous Driving Data Augmentation Instance Segmentation Segmentation Semantic Segmentation

Methods 이 논문이 사용한 방법론

Test 설명 없음

UmlsBERT: Clinical Domain Knowledge Augmentation of Contextual Embeddings Using the Unified Medical Language System Metathesaurus

2020-10-20 · NAACL 2021 4 · George Michalopoulos, Yuanxin Wang, Hussam Kaka, Helen Chen 외

Contextual word embedding models, such as BioBERT and Bio_ClinicalBERT, have achieved state-of-the-art results in biomedical natural language processing tasks by focusing their pre-training process on domain-specific cor…

named-entity-recognition Named Entity Recognition Named Entity Recognition (NER)Natural Language Inference+2

Improving the Natural Language Inference robustness to hard dataset by data augmentation and preprocessing

2024-12-10 · Zijiang Yang

Natural Language Inference (NLI) is the task of inferring whether the hypothesis can be justified by the given premise. Basically, we classify the hypothesis into three labels(entailment, neutrality and contradiction) gi…

Data Augmentation Natural Language Inference

Multi-Scales Data Augmentation Approach In Natural Language Inference For Artifacts Mitigation And Pre-Trained Model Optimization

2022-12-16 · Zhenyuan Lu

Machine learning models can reach high performance on benchmark natural language processing (NLP) datasets but fail in more challenging settings. We study this issue when a pre-trained model learns dataset artifacts in n…

Data Augmentation Model Optimization Natural Language Inference Sentence

Inference Computation Scaling for Feature Augmentation in Recommendation Systems

2025-02-22 · Weihao Liu, Zhaocheng Du, Haiyuan Zhao, Wenbo Zhang 외

Large language models have become a powerful method for feature augmentation in recommendation systems. However, existing approaches relying on quick inference often suffer from incomplete feature coverage and insufficie…

Math Recommendation Systems Specificity

Music-Source-Separation-Training (MSST): A Unified Framework for Training and Evaluating Music Demixing Models

2026-07-25 · Roman Solovyev, Ilya Kiselev, Alexander Stempkovskiy, Tatiana Gabruseva arxiv

Music Source Separation (MSS), the task of recovering individual sound components (stems) from a polyphonic mixture, is central to applications ranging from karaoke and remixing to audio restoration and content productio…

Music Source Separation