Exploring Relational Context for Multi-Task Dense Prediction (2104.13874v2)

Abstract: The timeline of computer vision research is marked with advances in learning and utilizing efficient contextual representations. Most of them, however, are targeted at improving model performance on a single downstream task. We consider a multi-task environment for dense prediction tasks, represented by a common backbone and independent task-specific heads. Our goal is to find the most efficient way to refine each task prediction by capturing cross-task contexts dependent on tasks' relations. We explore various attention-based contexts, such as global and local, in the multi-task setting and analyze their behavior when applied to refine each task independently. Empirical findings confirm that different source-target task pairs benefit from different context types. To automate the selection process, we propose an Adaptive Task-Relational Context (ATRC) module, which samples the pool of all available contexts for each task pair using neural architecture search and outputs the optimal configuration for deployment. Our method achieves state-of-the-art performance on two important multi-task benchmarks, namely NYUD-v2 and PASCAL-Context. The proposed ATRC has a low computational toll and can be used as a drop-in refinement module for any supervised multi-task architecture.

• The paper introduces the Adaptive Task-Relational Context (ATRC) module, which explores various relational contexts and attention mechanisms to capture inter- and intra-task relationships for multi-task dense prediction.
• An adaptive context type selection method, leveraging neural architecture search with Gumbel-Softmax, identifies the optimal relational context for specific task interactions.
• The ATRC module achieves state-of-the-art results on benchmarks like NYUD-v2 and PASCAL-Context for tasks like semantic segmentation and depth estimation with minimal computational overhead, improving upon existing multi-task frameworks.

Exploring Relational Context for Multi-Task Dense Prediction

This paper introduces an innovative approach to multi-task learning (MTL) for dense prediction tasks by developing an Adaptive Task-Relational Context (ATRC) module. The ATRC utilizes relational contexts to refine predictions in a multi-task environment, taking advantage of task-related interdependencies to optimize performance across several dense prediction tasks. The paper highlights the strengths of the ATRC in providing a sophisticated context extraction mechanism that captures both inter- and intra-task relationships using attention-based strategies.

Key Contributions

1. Relational Contexts for Multi-Task Learning: The authors explore a set of relational contexts that incorporate spatial and feature similarities between tasks. They extend the concept of attention mechanisms to MTL, developing four major types of contexts: global, local, T-label, and S-label. These contexts guide how information from one task (source) can enrich the predictions of another task (target).
2. Adaptive Context Type Selection: A neural architecture search (NAS) is repurposed to efficiently select the optimal context type for each source-target task pair. The Gumbel-Softmax estimator is employed to enable a differentiable search space, allowing efficient gradient-based search that can identify which relational context is most beneficial for specific task interactions.
3. Integration and Performance: ATRC can augment any existing supervised multi-task architecture with little computational overhead. It acts as a drop-in module to improve dense prediction tasks across multiple benchmarks, notably NYUD-v2 and PASCAL-Context, achieving state-of-the-art results.

Empirical Results

The empirical evaluation uses HRNet and ResNet backbones across renowned datasets, including NYUD-v2 and PASCAL-Context. The results indicate substantial performance improvements over both single-task baselines and comparative multi-task frameworks, particularly MTI-Net. Specifically, ATRC provides notable gains in semantic segmentation, depth estimation, and surface normal prediction tasks without significant resource overhead.

Implications and Future Directions

The ATRC's ability to tackle multiple dense prediction tasks suggests broader implications for the design of task-efficient and modular deep learning systems. Its adaptation mechanism marks a step toward more intelligent multi-task learning frameworks that can dynamically adjust to specific task requirements, thereby optimizing computational resources.

For future research, exploring the integration of ATRC with various backbone architectures and investigating its potential to scale with even more complex task dictionaries are promising directions. Additionally, refining the context selection process could enhance the dynamic adaptability of multi-task architectures, resulting in more generalized and robust predictive models.

Conclusion

The ATRC represents an advancement in the field of dense prediction, offering nuanced insight into how neural networks can simultaneously tackle multiple tasks using shared and task-specific knowledge. Its novel approach to distillation and task relational contexts provides a foundation for future innovations in multi-task learning, contributing to more resource-efficient and effective predictive technologies.