# Focused and Collaborative Feedback Integration for Interactive Image Segmentation

arXiv: https://arxiv.org/abs/2303.11880 · HTML: https://mlsanity.com/paper/2303.11880/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 46 references that are in the map.
- First posted on arXiv 21 Mar 2023.

## Builds on (most cited first, 21 of 21)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [LVIS: A Dataset for Large Vocabulary Instance Segmentation](https://mlsanity.com/paper/1908.03195/) (2019)
- [Segmentation Transformer: Object-Contextual Representations for Semantic Segmentation](https://mlsanity.com/paper/1909.11065/) (2019)
- [High-Resolution Representations for Labeling Pixels and Regions](https://mlsanity.com/paper/1904.04514/) (2019)
- [Reviving Iterative Training with Mask Guidance for Interactive Segmentation](https://mlsanity.com/paper/2102.06583/) (2021)
- [Deep Interactive Object Selection](https://mlsanity.com/paper/1603.04042/) (2016)
- [Deep Extreme Cut: From Extreme Points to Object Segmentation](https://mlsanity.com/paper/1711.09081/) (2017)
- [f-BRS: Rethinking Backpropagating Refinement for Interactive Segmentation](https://mlsanity.com/paper/2001.10331/) (2020)
- [Large-scale interactive object segmentation with human annotators](https://mlsanity.com/paper/1903.10830/) (2019)
- [FocalClick: Towards Practical Interactive Image Segmentation](https://mlsanity.com/paper/2204.02574/) (2022)
- [Extreme clicking for efficient object annotation](https://mlsanity.com/paper/1708.02750/) (2017)
- [AdaptIS: Adaptive Instance Selection Network](https://mlsanity.com/paper/1909.07829/) (2019)
- [Iteratively Trained Interactive Segmentation](https://mlsanity.com/paper/1805.04398/) (2018)
- [EdgeFlow: Achieving Practical Interactive Segmentation with Edge-Guided Flow](https://mlsanity.com/paper/2109.09406/) (2021)
- [Continuous Adaptation for Interactive Object Segmentation by Learning from Corrections](https://mlsanity.com/paper/1911.12709/) (2019)
- [Deep GrabCut for Object Selection](https://mlsanity.com/paper/1707.00243/) (2017)
- [LooseCut: Interactive Image Segmentation with Loosely Bounded Boxes](https://mlsanity.com/paper/1507.03060/) (2015)

## Built on by (most cited first, 17 of 17)

- [Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation](https://mlsanity.com/paper/2304.12620/) (2023)
- [Medical SAM 2: Segment medical images as video via Segment Anything Model 2](https://mlsanity.com/paper/2408.00874/) (2024)
- [AGILE3D: Attention Guided Interactive Multi-object 3D Segmentation](https://mlsanity.com/paper/2306.00977/) (2023)
- [SAM2 for Image and Video Segmentation: A Comprehensive Survey](https://mlsanity.com/paper/2503.12781/) (2025)
- [AdaptiveClick: Clicks-aware Transformer with Adaptive Focal Loss for Interactive Image Segmentation](https://mlsanity.com/paper/2305.04276/) (2023)
- [Interactive Image Segmentation with Cross-Modality Vision Transformers](https://mlsanity.com/paper/2307.02280/) (2023)
- [Refining Segmentation On-the-Fly: An Interactive Framework for Point Cloud Semantic Segmentation](https://mlsanity.com/paper/2403.06401/) (2024)
- [Learning Trimaps via Clicks for Image Matting](https://mlsanity.com/paper/2404.00335/) (2024)
- [DC-TTA: Divide-and-Conquer Framework for Test-Time Adaptation of Interactive Segmentation](https://mlsanity.com/paper/2506.23104/) (2025)
- [PVPUFormer: Probabilistic Visual Prompt Unified Transformer for Interactive Image Segmentation](https://mlsanity.com/paper/2306.06656/) (2023)
- [Leveraging Self-Supervised Vision Transformers for Segmentation-based Transfer Function Design](https://mlsanity.com/paper/2309.01408/) (2023)
- [Towards Efficient Pixel Labeling for Industrial Anomaly Detection and Localization](https://mlsanity.com/paper/2407.03130/) (2024)
- [Breaking the Box: Enhancing Remote Sensing Image Segmentation with Freehand Sketches](https://mlsanity.com/paper/2503.12191/) (2025)
- [RefCut: Interactive Segmentation with Reference Guidance](https://mlsanity.com/paper/2503.17820/) (2025)
- [Promoting SAM for Camouflaged Object Detection via Selective Key Point-based Guidance](https://mlsanity.com/paper/2505.09123/) (2025)
- [Towards Efficient Pixel Labeling for Industrial Anomaly Detection and Localization](https://mlsanity.com/paper/2509.05034/) (2025)
- [Interactive Episodic Memory with User Feedback](https://mlsanity.com/paper/2604.24893/) (2026)

## Abstract (arXiv)

Interactive image segmentation aims at obtaining a segmentation mask for an image using simple user annotations. During each round of interaction, the segmentation result from the previous round serves as feedback to guide the user's annotation and provides dense prior information for the segmentation model, effectively acting as a bridge between interactions. Existing methods overlook the importance of feedback or simply concatenate it with the original input, leading to underutilization of feedback and an increase in the number of required annotations. To address this, we propose an approach called Focused and Collaborative Feedback Integration (FCFI) to fully exploit the feedback for click-based interactive image segmentation. FCFI first focuses on a local area around the new click and corrects the feedback based on the similarities of high-level features. It then alternately and collaboratively updates the feedback and deep features to integrate the feedback into the features. The efficacy and efficiency of FCFI were validated on four benchmarks, namely GrabCut, Berkeley, SBD, and DAVIS. Experimental results show that FCFI achieved new state-of-the-art performance with less computational overhead than previous methods. The source code is available at https://github.com/veizgyauzgyauz/FCFI.