Methods › Computer Vision › Image Model Blocks › Res2Net Block

Res2Net Block

25 papers tagged archive 2025-07-28

Introduced by Shang-Hua Gao et al. in Res2Net: A New Multi-scale Backbone Architecture

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

A Res2Net Block is an image model block that constructs hierarchical residual-like connections within one single residual block. It was proposed as part of the Res2Net CNN architecture.

The block represents multi-scale features at a granular level and increases the range of receptive fields for each network layer. The 3 ×3 filters of n channels is replaced with a set of smaller filter groups, each with w channels. These smaller filter groups are connected in a hierarchical residual-like style to increase the number of scales that the output features can represent. Specifically, we divide input feature maps into several groups. A group of filters first extracts features from a group of input feature maps. Output features of the previous group are then sent to the next group of filters along with another group of input feature maps.

This process repeats several times until all input feature maps are processed. Finally, feature maps from all groups are concatenated and sent to another group of 1 ×1 filters to fuse information altogether. Along with any possible path in which input features are transformed to output features, the equivalent receptive field increases whenever it passes a 3 ×3 filter, resulting in many equivalent feature scales due to combination effects.

One way of thinking of these blocks is that they expose a new dimension, scale, alongside the existing dimensions of depth, width, and cardinality.

PaperSourceSee Code · Res2Net/Res2Net-PretrainedModels

Papers archive 2025-07-28

25 shown of 25, 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

20 shown of 45 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
Speaker Verification9
Segmentation3
Face Swapping2
Semantic Segmentation2
Speaker Recognition2
Automatic Speech Recognition1
Binary Classification1
Cancer Classification1
Classification1
Computational Efficiency1
Data Augmentation1
Decoder1
Deep Learning1
DeepFake Detection1
Dimensionality Reduction1
Feature Engineering1
GPU1
Generative Adversarial Network1
Graph Attention1
Image Classification1

Usage over time archive 2025-07-28

Papers per year tagged with Res2Net Block: 2019 to 2025, peak 7 7 0 2019: 2 papers 2019 2020: 3 papers 2020 2021: 3 papers 2021 2022: 6 papers 2022 2023: 7 papers 2023 2024: 3 papers 2024 2025: 1 paper 2025
Papers per year the archive tags with this method, by the paper's archive date (25 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

Image Model BlocksSkip Connection Blocks

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