Methods › Computer Vision › Multi-Scale Training › SNIPER

SNIPER

5 papers tagged archive 2025-07-28

Introduced by Bharat Singh et al. in SNIPER: Efficient Multi-Scale Training

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

SNIPER is a multi-scale training approach for instance-level recognition tasks like object detection and instance-level segmentation. Instead of processing all pixels in an image pyramid, SNIPER selectively processes context regions around the ground-truth objects (a.k.a chips). This can help to speed up multi-scale training as it operates on low-resolution chips. Due to its memory-efficient design, SNIPER can benefit from Batch Normalization during training and it makes larger batch-sizes possible for instance-level recognition tasks on a single GPU.

PaperSource

Papers archive 2025-07-28

5 shown of 5, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

10 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Object Detection3
object-detection3
Fault localization1
GPU1
Object1
Region Proposal1
TAG1
Text to Speech1
image-classification1
text-to-speech1

Usage over time archive 2025-07-28

Papers per year tagged with SNIPER: 2018 to 2024, peak 2 2 0 2018: 2 papers 2018 2019: 0 papers 2019 2020: 0 papers 2020 2021: 1 paper 2021 2022: 1 paper 2022 2023: 0 papers 2023 2024: 1 paper 2024
Papers per year the archive tags with this method, by the paper's archive date (5 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Multi-Scale Training

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