Optimizing Dense Visual Predictions Through Multi-Task Coherence and Prioritization

• URL: http://arxiv.org/abs/2412.03179v1
• Date: Wed, 04 Dec 2024 10:05:47 GMT
• Title: Optimizing Dense Visual Predictions Through Multi-Task Coherence and Prioritization
• Authors: Maxime Fontana, Michael Spratling, Miaojing Shi,
• Abstract summary: Multi-Task Learning (MTL) involves the concurrent training of multiple tasks. We propose an advanced MTL model specifically designed for dense vision tasks.
• Score: 7.776434991976473
• License:
• Abstract: Multi-Task Learning (MTL) involves the concurrent training of multiple tasks, offering notable advantages for dense prediction tasks in computer vision. MTL not only reduces training and inference time as opposed to having multiple single-task models, but also enhances task accuracy through the interaction of multiple tasks. However, existing methods face limitations. They often rely on suboptimal cross-task interactions, resulting in task-specific predictions with poor geometric and predictive coherence. In addition, many approaches use inadequate loss weighting strategies, which do not address the inherent variability in task evolution during training. To overcome these challenges, we propose an advanced MTL model specifically designed for dense vision tasks. Our model leverages state-of-the-art vision transformers with task-specific decoders. To enhance cross-task coherence, we introduce a trace-back method that improves both cross-task geometric and predictive features. Furthermore, we present a novel dynamic task balancing approach that projects task losses onto a common scale and prioritizes more challenging tasks during training. Extensive experiments demonstrate the superiority of our method, establishing new state-of-the-art performance across two benchmark datasets. The code is available at:https://github.com/Klodivio355/MT-CP

Related papers

• Tint Your Models Task-wise for Improved Multi-task Model Merging [17.496018757317824]
  We propose Model Tinting, a test-time approach that introduces a single task-specific layer for each task as trainable adjustments. Our method jointly trains merging coefficients and task-specific layers, which effectively reduces task conflicts with minimal additional costs. Our method achieves state-of-the-art performance across both computer vision and natural language processing tasks.
  arXiv  Detail & Related papers  (2024-12-26T07:42:06Z)
• Cross-Task Affinity Learning for Multitask Dense Scene Predictions [5.939164722752263]
  Multitask learning (MTL) has become prominent for its ability to predict multiple tasks jointly. We introduce the Cross-Task Affinity Learning (CTAL) module, a lightweight framework that enhances task refinement in multitask networks. Our results demonstrate state-of-the-art MTL performance for both CNN and transformer backbones, using significantly fewer parameters than single-task learning.
  arXiv  Detail & Related papers  (2024-01-20T05:31:47Z)
• Contrastive Multi-Task Dense Prediction [11.227696986100447]
  A core objective in design is how to effectively model cross-task interactions to achieve a comprehensive improvement on different tasks. We introduce feature-wise contrastive consistency into modeling the cross-task interactions for multi-task dense prediction. We propose a novel multi-task contrastive regularization method based on the consistency to effectively boost the representation learning of the different sub-tasks.
  arXiv  Detail & Related papers  (2023-07-16T03:54:01Z)
• Effective Adaptation in Multi-Task Co-Training for Unified Autonomous Driving [103.745551954983]
  In this paper, we investigate the transfer performance of various types of self-supervised methods, including MoCo and SimCLR, on three downstream tasks. We find that their performances are sub-optimal or even lag far behind the single-task baseline. We propose a simple yet effective pretrain-adapt-finetune paradigm for general multi-task training.
  arXiv  Detail & Related papers  (2022-09-19T12:15:31Z)
• Sparsely Activated Mixture-of-Experts are Robust Multi-Task Learners [67.5865966762559]
  We study whether sparsely activated Mixture-of-Experts (MoE) improve multi-task learning. We devise task-aware gating functions to route examples from different tasks to specialized experts. This results in a sparsely activated multi-task model with a large number of parameters, but with the same computational cost as that of a dense model.
  arXiv  Detail & Related papers  (2022-04-16T00:56:12Z)
• Task Adaptive Parameter Sharing for Multi-Task Learning [114.80350786535952]
  Adaptive Task Adapting Sharing (TAPS) is a method for tuning a base model to a new task by adaptively modifying a small, task-specific subset of layers. Compared to other methods, TAPS retains high accuracy on downstream tasks while introducing few task-specific parameters. We evaluate our method on a suite of fine-tuning tasks and architectures (ResNet, DenseNet, ViT) and show that it achieves state-of-the-art performance while being simple to implement.
  arXiv  Detail & Related papers  (2022-03-30T23:16:07Z)
• In Defense of the Unitary Scalarization for Deep Multi-Task Learning [121.76421174107463]
  We present a theoretical analysis suggesting that many specialized multi-tasks can be interpreted as forms of regularization. We show that, when coupled with standard regularization and stabilization techniques, unitary scalarization matches or improves upon the performance of complex multitasks.
  arXiv  Detail & Related papers  (2022-01-11T18:44:17Z)
• Cross-Task Consistency Learning Framework for Multi-Task Learning [9.991706230252708]
  We propose a new learning framework for 2-task MTL problem. We define two new loss terms inspired by cycle-consistency loss and contrastive learning. We theoretically prove that both losses help the model learn more efficiently and that cross-task consistency loss is better in terms of alignment with the straight-forward predictions.
  arXiv  Detail & Related papers  (2021-11-28T11:55:19Z)
• Multi-Task Learning with Sequence-Conditioned Transporter Networks [67.57293592529517]
  We aim to solve multi-task learning through the lens of sequence-conditioning and weighted sampling. We propose a new suite of benchmark aimed at compositional tasks, MultiRavens, which allows defining custom task combinations. Second, we propose a vision-based end-to-end system architecture, Sequence-Conditioned Transporter Networks, which augments Goal-Conditioned Transporter Networks with sequence-conditioning and weighted sampling.
  arXiv  Detail & Related papers  (2021-09-15T21:19:11Z)
• Knowledge Distillation for Multi-task Learning [38.20005345733544]
  Multi-task learning (MTL) is to learn one single model that performs multiple tasks for achieving good performance on all tasks and lower cost on computation. Learning such a model requires to jointly optimize losses of a set of tasks with different difficulty levels, magnitudes, and characteristics. We propose a knowledge distillation based method in this work to address the imbalance problem in multi-task learning.
  arXiv  Detail & Related papers  (2020-07-14T08:02:42Z)
• Multi-Task Learning for Dense Prediction Tasks: A Survey [87.66280582034838]
  Multi-task learning (MTL) techniques have shown promising results w.r.t. performance, computations and/or memory footprint. We provide a well-rounded view on state-of-the-art deep learning approaches for MTL in computer vision.
  arXiv  Detail & Related papers  (2020-04-28T09:15:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.