# IvaNet: Learning to jointly detect and segment objets with the help of Local Top-Down Modules

## TL;DR

IvaNet is a multi-task framework that enhances object detection and segmentation by using local top-down modules to better integrate semantic information across layers, improving robustness.

## Contribution

IvaNet introduces local top-down modules for joint detection and segmentation, addressing robustness issues in previous full top-down approaches.

## Key findings

- Demonstrated improved performance on PASCAL VOC
- Achieved competitive results on MS COCO
- Validated effectiveness of local top-down modules

## Abstract

Driven by Convolutional Neural Networks, object detection and semantic segmentation have gained significant improvements. However, existing methods on the basis of a full top-down module have limited robustness in handling those two tasks simultaneously. To this end, we present a joint multi-task framework, termed IvaNet. Different from existing methods, our IvaNet backwards abstract semantic information from higher layers to augment lower layers using local top-down modules. The comparisons against some counterparts on the PASCAL VOC and MS COCO datasets demonstrate the functionality of IvaNet.