# BriNet: Towards Bridging the Intra-class and Inter-class Gaps in One-Shot Segmentation

arXiv: https://arxiv.org/abs/2008.06226 · HTML: https://mlsanity.com/paper/2008.06226/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Aug 2020.

## Built on by (most cited first, 28 of 28)

- [Adaptive Prototype Learning and Allocation for Few-Shot Segmentation](https://mlsanity.com/paper/2104.01893/) (2021)
- [Self-Support Few-Shot Semantic Segmentation](https://mlsanity.com/paper/2207.11549/) (2022)
- [Feature-Proxy Transformer for Few-Shot Segmentation](https://mlsanity.com/paper/2210.06908/) (2022)
- [Integrative Few-Shot Learning for Classification and Segmentation](https://mlsanity.com/paper/2203.15712/) (2022)
- [Learning Expressive Prompting With Residuals for Vision Transformers](https://mlsanity.com/paper/2303.15591/) (2023)
- [Rethinking Prior Information Generation with CLIP for Few-Shot Segmentation](https://mlsanity.com/paper/2405.08458/) (2024)
- [Visual Semantic Segmentation Based on Few/Zero-Shot Learning: An Overview](https://mlsanity.com/paper/2211.08352/) (2022)
- [Visual Prompting for Generalized Few-shot Segmentation: A Multi-scale Approach](https://mlsanity.com/paper/2404.11732/) (2024)
- [Not Just Learning from Others but Relying on Yourself: A New Perspective on Few-Shot Segmentation in Remote Sensing](https://mlsanity.com/paper/2310.12452/) (2023)
- [MFNet: Multi-class Few-shot Segmentation Network with Pixel-wise Metric Learning](https://mlsanity.com/paper/2111.00232/) (2021)
- [Adaptive FSS: A Novel Few-Shot Segmentation Framework via Prototype Enhancement](https://mlsanity.com/paper/2312.15731/) (2023)
- [Harmonizing Base and Novel Classes: A Class-Contrastive Approach for Generalized Few-Shot Segmentation](https://mlsanity.com/paper/2303.13724/) (2023)
- [A Self-Distillation Embedded Supervised Affinity Attention Model for Few-Shot Segmentation](https://mlsanity.com/paper/2108.06600/) (2021)
- [Temporal Transductive Inference for Few-Shot Video Object Segmentation](https://mlsanity.com/paper/2203.14308/) (2022)
- [AgMTR: Agent Mining Transformer for Few-shot Segmentation in Remote Sensing](https://mlsanity.com/paper/2409.17453/) (2024)
- [DARNet: Bridging Domain Gaps in Cross-Domain Few-Shot Segmentation with Dynamic Adaptation](https://mlsanity.com/paper/2312.04813/) (2023)
- [Few Shot Semantic Segmentation: a review of methodologies, benchmarks, and open challenges](https://mlsanity.com/paper/2304.05832/) (2023)
- [Group-On: Boosting One-Shot Segmentation with Supportive Query](https://mlsanity.com/paper/2404.11871/) (2024)
- [Elimination of Non-Novel Segments at Multi-Scale for Few-Shot Segmentation](https://mlsanity.com/paper/2211.02300/) (2022)
- [The Ikshana Hypothesis of Human Scene Understanding](https://mlsanity.com/paper/2101.10837/) (2021)
- [EAA-Net: Rethinking the Autoencoder Architecture with Intra-class Features for Medical Image Segmentation](https://mlsanity.com/paper/2208.09197/) (2022)
- [Few-Shot Medical Image Segmentation with Large Kernel Attention](https://mlsanity.com/paper/2407.19148/) (2024)
- [AFANet: Adaptive Frequency-Aware Network for Weakly-Supervised Few-Shot Semantic Segmentation](https://mlsanity.com/paper/2412.17601/) (2024)
- [Balancing Conservatism and Aggressiveness: Prototype-Affinity Hybrid Network for Few-Shot Segmentation](https://mlsanity.com/paper/2507.19140/) (2025)
- [Unbiased Semantic Decoding with Vision Foundation Models for Few-shot Segmentation](https://mlsanity.com/paper/2511.15118/) (2025)
- [CobNet: Cross Attention on Object and Background for Few-Shot Segmentation](https://mlsanity.com/paper/2210.11968/) (2022)
- [A Joint Framework Towards Class-aware and Class-agnostic Alignment for Few-shot Segmentation](https://mlsanity.com/paper/2211.01310/) (2022)
- [ISAR: A Benchmark for Single- and Few-Shot Object Instance Segmentation and Re-Identification](https://mlsanity.com/paper/2311.02734/) (2023)

## Abstract (arXiv)

Few-shot segmentation focuses on the generalization of models to segment unseen object instances with limited training samples. Although tremendous improvements have been achieved, existing methods are still constrained by two factors. (1) The information interaction between query and support images is not adequate, leaving intra-class gap. (2) The object categories at the training and inference stages have no overlap, leaving the inter-class gap. Thus, we propose a framework, BriNet, to bridge these gaps. First, more information interactions are encouraged between the extracted features of the query and support images, i.e., using an Information Exchange Module to emphasize the common objects. Furthermore, to precisely localize the query objects, we design a multi-path fine-grained strategy which is able to make better use of the support feature representations. Second, a new online refinement strategy is proposed to help the trained model adapt to unseen classes, achieved by switching the roles of the query and the support images at the inference stage. The effectiveness of our framework is demonstrated by experimental results, which outperforms other competitive methods and leads to a new state-of-the-art on both PASCAL VOC and MSCOCO dataset.