Cascaded Sparse Spatial Bins for Efficient and Effective Generic Object Detection

2015-04-27 · ICCV 2015 12 · David Novotny, Jiri Matas

A novel efficient method for extraction of object proposals is introduced. Its "objectness" function exploits deep spatial pyramid features, a novel fast-to-compute HoG-based edge statistic and the EdgeBoxes score. The efficiency is achieved by the use of spatial bins in a novel combination with sparsity-inducing group normalized SVM. State-of-the-art recall performance is achieved on Pascal VOC07, significantly outperforming methods with comparable speed. Interestingly, when only 100 proposals per image are considered the method attains 78% recall on VOC07. The method improves mAP of the RCNN state-of-the-art class-specific detector, increasing it by 10 points when only 50 proposals are used in each image. The system trained on twenty classes performs well on the two hundred class ILSVRC2013 set confirming generalization capability.

📄 PDF Abstract BibTeX arXiv:1504.07029

Code (0)

등록된 구현이 없습니다.

Tasks

object-detection Object Detection

Methods 이 논문이 사용한 방법론

SVM A Support Vector Machine, or SVM, is a non-parametric supervised learning model. For non-linear classification and regression, they utilise the kernel trick to map inputs…

EdgeBoxes EdgeBoxes is an approach for generating object bounding box proposals directly from edges. Similar to segments, edges provide a simplified but informative representation of an…

Randomized Quantization: A Generic Augmentation for Data Agnostic Self-supervised Learning

2022-12-19 · ICCV 2023 1 · Huimin Wu, Chenyang Lei, Xiao Sun, Peng-Shuai Wang 외

Self-supervised representation learning follows a paradigm of withholding some part of the data and tasking the network to predict it from the remaining part. Among many techniques, data augmentation lies at the core for…

Data Augmentation Quantization Representation Learning Self-Supervised Learning

TRIC-track: Tracking by Regression With Incrementally Learned Cascades

2015-12-01 · ICCV 2015 12 · Xiaomeng Wang, Michel Valstar, Brais Martinez, Muhammad Haris Khan 외

This paper proposes a novel approach to part-based tracking by replacing local matching of an appearance model by direct prediction of the displacement between local image patches and part locations. We propose to use ca…

Incremental Learning regression

Open-Vocabulary Camouflaged Object Segmentation with Cascaded Vision Language Models

2025-06-24 · Kai Zhao, Wubang Yuan, Zheng Wang, Guanyi Li 외

Open-Vocabulary Camouflaged Object Segmentation (OVCOS) seeks to segment and classify camouflaged objects from arbitrary categories, presenting unique challenges due to visual ambiguity and unseen categories.Recent appro…

Camouflaged Object Segmentation Segmentation Semantic Segmentation

CabinSep: IR-Augmented Mask-Based MVDR for Real-Time In-Car Speech Separation with Distributed Heterogeneous Arrays

2025-09-01 · Runduo Han, Yanxin Hu, Yihui Fu, Zihan Zhang 외 arxiv

Separating overlapping speech from multiple speakers is crucial for effective human-vehicle interaction. This paper proposes CabinSep, a lightweight neural mask-based minimum variance distortionless response (MVDR) speec…

Speech Recognition Speech Separation Data Augmentation

Sparser2Sparse: Single-shot Sparser-to-Sparse Learning for Spatial Transcriptomics Imputation with Natural Image Co-learning

2025-07-22 · Yaoyu Fang, Jiahe Qian, Xinkun Wang, Lee A. Cooper 외 arxiv

Spatial transcriptomics (ST) has revolutionized biomedical research by enabling high resolution gene expression profiling within tissues. However, the high cost and scarcity of high resolution ST data remain significant …

Self-Supervised Learning Sparse Learning