Uni-OVSeg: A Step Towards Efficient and Bias-Resilient Vision Systems

Table of Links

Abstract and 1. Introduction

2. Related works

3. Method and 3.1. Problem definition

3.2. Baseline and 3.3. Uni-OVSeg framework

4. Experiments

4.1. Implementation details

4.2. Main results

4.3. Ablation study

5. Conclusion

6. Broader impacts and References

A. Framework details

B. Promptable segmentation

C. Visualisation

6. Broader impacts

The Uni-OVSeg framework represents a significant advancement in open-vocabulary segmentation by reducing the dependency on labour-intensive image-mask-text triplet annotations. This innovation has the potential to democratise access to cutting-edge vision perception systems, offering substantial benefits across various sectors, such as medical imaging and autonomous vehicles. The development of a more efficient and accurate vision perception system contributes to the community, potentially leading to more innovative applications and research in machine learning, computer vision, and related areas. As with any AI model, there is a risk of bias in the data used for training. Efforts must be made to ensure that the datasets are diverse and representative to avoid perpetuating or amplifying biases.

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