The paper introduces the Hybrid Task Cascade (HTC), a novel framework for instance segmentation that integrates detection and segmentation tasks through a cascaded multi-stage processing. Unlike traditional approaches that separately refine bounding boxes and masks, HTC interweaves these tasks to leverage their reciprocal relationship, enhancing the information flow and improving overall performance. Key contributions include: 1. **Interleaved Execution**: The box and mask branches are executed in an interleaved manner, allowing the mask branch to benefit from updated bounding box predictions. 2. **Mask Information Flow**: A direct path is introduced to reinforce the information flow between mask branches, enabling progressive refinement of masks. 3. **Semantic Segmentation Branch**: An additional branch for semantic segmentation is added to provide spatial context, aiding in distinguishing foreground objects from background clutter. The HTC framework is trained end-to-end and achieves state-of-the-art performance on the COCO dataset, outperforming existing methods by 1.5% to 1.1% in terms of mask AP. The system also ranks first in the COCO 2018 Challenge Object Detection Task, achieving 48.6 mask AP on the test-challenge split. The paper includes extensive ablation studies and a detailed analysis of the effectiveness of each component, demonstrating the robustness and efficiency of the proposed approach.The paper introduces the Hybrid Task Cascade (HTC), a novel framework for instance segmentation that integrates detection and segmentation tasks through a cascaded multi-stage processing. Unlike traditional approaches that separately refine bounding boxes and masks, HTC interweaves these tasks to leverage their reciprocal relationship, enhancing the information flow and improving overall performance. Key contributions include: 1. **Interleaved Execution**: The box and mask branches are executed in an interleaved manner, allowing the mask branch to benefit from updated bounding box predictions. 2. **Mask Information Flow**: A direct path is introduced to reinforce the information flow between mask branches, enabling progressive refinement of masks. 3. **Semantic Segmentation Branch**: An additional branch for semantic segmentation is added to provide spatial context, aiding in distinguishing foreground objects from background clutter. The HTC framework is trained end-to-end and achieves state-of-the-art performance on the COCO dataset, outperforming existing methods by 1.5% to 1.1% in terms of mask AP. The system also ranks first in the COCO 2018 Challenge Object Detection Task, achieving 48.6 mask AP on the test-challenge split. The paper includes extensive ablation studies and a detailed analysis of the effectiveness of each component, demonstrating the robustness and efficiency of the proposed approach.