# Enhancing Mamba Decoder with Bidirectional Interaction in Multi-Task Dense Prediction

## TL;DR

This paper introduces a Bidirectional Interaction Mamba (BIM) model that enhances multi-task dense prediction by efficiently capturing cross-task interactions through novel scanning mechanisms, balancing accuracy and computational efficiency.

## Contribution

The work proposes a new BIM framework with BI-Scan and MS-Scan mechanisms, enabling effective multi-granularity scene modeling with linear complexity.

## Key findings

- Outperforms state-of-the-art on NYUD-V2 and PASCAL-Context benchmarks.
- Efficiently balances interaction completeness and computational cost.
- Demonstrates superior multi-task dense prediction performance.

## Abstract

Sufficient cross-task interaction is crucial for success in multi-task dense prediction. However, sufficient interaction often results in high computational complexity, forcing existing methods to face the trade-off between interaction completeness and computational efficiency. To address this limitation, this work proposes a Bidirectional Interaction Mamba (BIM), which incorporates novel scanning mechanisms to adapt the Mamba modeling approach for multi-task dense prediction. On the one hand, we introduce a novel Bidirectional Interaction Scan (BI-Scan) mechanism, which constructs task-specific representations as bidirectional sequences during interaction. By integrating task-first and position-first scanning modes within a unified linear complexity architecture, BI-Scan efficiently preserves critical cross-task information. On the other hand, we employ a Multi-Scale Scan~(MS-Scan) mechanism to achieve multi-granularity scene modeling. This design not only meets the diverse granularity requirements of various tasks but also enhances nuanced cross-task feature interactions. Extensive experiments on two challenging benchmarks, \emph{i.e.}, NYUD-V2 and PASCAL-Context, show the superiority of our BIM vs its state-of-the-art competitors.