Progressive Local Filter Pruning for Image Retrieval Acceleration

2020-01-24 · Xiaodong Wang, Zhedong Zheng, Yang He, Fei Yan, Zhiqiang Zeng, Yi Yang

This paper focuses on network pruning for image retrieval acceleration. Prevailing image retrieval works target at the discriminative feature learning, while little attention is paid to how to accelerate the model inference, which should be taken into consideration in real-world practice. The challenge of pruning image retrieval models is that the middle-level feature should be preserved as much as possible. Such different requirements of the retrieval and classification model make the traditional pruning methods not that suitable for our task. To solve the problem, we propose a new Progressive Local Filter Pruning (PLFP) method for image retrieval acceleration. Specifically, layer by layer, we analyze the local geometric properties of each filter and select the one that can be replaced by the neighbors. Then we progressively prune the filter by gradually changing the filter weights. In this way, the representation ability of the model is preserved. To verify this, we evaluate our method on two widely-used image retrieval datasets,i.e., Oxford5k and Paris6K, and one person re-identification dataset,i.e., Market-1501. The proposed method arrives with superior performance to the conventional pruning methods, suggesting the effectiveness of the proposed method for image retrieval.

📄 PDF Abstract BibTeX arXiv:2001.08878

Code (0)

등록된 구현이 없습니다.

Tasks

Image Retrieval Network Pruning Person Re-Identification Retrieval

Methods 이 논문이 사용한 방법론

Pruning 설명 없음

Progressive Correspondence Pruning by Consensus Learning

2021-01-03 · ICCV 2021 10 · Chen Zhao, Yixiao Ge, Feng Zhu, Rui Zhao 외

Correspondence selection aims to correctly select the consistent matches (inliers) from an initial set of putative correspondences. The selection is challenging since putative matches are typically extremely unbalanced, …

Camera Pose Estimation Denoising Pose Estimation Retrieval

Progressive Gradient Pruning for Classification, Detection and DomainAdaptation

2019-06-20 · Le Thanh Nguyen-Meidine, Eric Granger, Madhu Kiran, Louis-Antoine Blais-Morin 외

Although deep neural networks (NNs) have achievedstate-of-the-art accuracy in many visual recognition tasks,the growing computational complexity and energy con-sumption of networks remains an issue, especially for ap-pli…

Classification General Classification object-detection Object Detection

Sub-network Multi-objective Evolutionary Algorithm for Filter Pruning

2022-10-22 · Xuhua Li, Weize Sun, Lei Huang, Shaowu Chen

Filter pruning is a common method to achieve model compression and acceleration in deep neural networks (DNNs).Some research regarded filter pruning as a combinatorial optimization problem and thus used evolutionary algo…

Combinatorial Optimization Evolutionary Algorithms Model Compression

Progressive Per-Branch Depth Optimization for DEFOM-Stereo and SAM3 Joint Analysis in UAV Forestry Applications

2026-02-24 · Yida Lin, Bing Xue, Mengjie Zhang, Sam Schofield 외 arxiv

Accurate per-branch 3D reconstruction is a prerequisite for autonomous UAV-based tree pruning; however, dense disparity maps from modern stereo matchers often remain too noisy for individual branch analysis in complex fo…

Instance Segmentation Disparity Estimation 3D Reconstruction Point Clouds

Progressive Channel-Shrinking Network

2023-04-01 · Jianhong Pan, Siyuan Yang, Lin Geng Foo, Qiuhong Ke 외

Currently, salience-based channel pruning makes continuous breakthroughs in network compression. In the realization, the salience mechanism is used as a metric of channel salience to guide pruning. Therefore, salience-ba…