MEJO: MLLM-Engaged Surgical Triplet Recognition via Inter- and Intra-Task Joint Optimization

• URL: http://arxiv.org/abs/2509.12893v1
• Date: Tue, 16 Sep 2025 09:48:52 GMT
• Title: MEJO: MLLM-Engaged Surgical Triplet Recognition via Inter- and Intra-Task Joint Optimization
• Authors: Yiyi Zhang, Yuchen Yuan, Ying Zheng, Jialun Pei, Jinpeng Li, Zheng Li, Pheng-Ann Heng,
• Abstract summary: We propose a framework that empowers both inter- and intra-task optimization for surgical triplet recognition.<n>For inter-task optimization, we introduce the Shared-Specific-Disentangled (S$2$D) learning scheme that decomposes representations into task-shared and task-specific components.<n>For intra-task optimization conflicts, we develop a Coordinated Gradient Learning (CGL) strategy, which dissects and rebalances the positive-negative ambiguities.
• Score: 52.149337961205624
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Surgical triplet recognition, which involves identifying instrument, verb, target, and their combinations, is a complex surgical scene understanding challenge plagued by long-tailed data distribution. The mainstream multi-task learning paradigm benefiting from cross-task collaborative promotion has shown promising performance in identifying triples, but two key challenges remain: 1) inter-task optimization conflicts caused by entangling task-generic and task-specific representations; 2) intra-task optimization conflicts due to class-imbalanced training data. To overcome these difficulties, we propose the MLLM-Engaged Joint Optimization (MEJO) framework that empowers both inter- and intra-task optimization for surgical triplet recognition. For inter-task optimization, we introduce the Shared-Specific-Disentangled (S$^2$D) learning scheme that decomposes representations into task-shared and task-specific components. To enhance task-shared representations, we construct a Multimodal Large Language Model (MLLM) powered probabilistic prompt pool to dynamically augment visual features with expert-level semantic cues. Additionally, comprehensive task-specific cues are modeled via distinct task prompts covering the temporal-spatial dimensions, effectively mitigating inter-task ambiguities. To tackle intra-task optimization conflicts, we develop a Coordinated Gradient Learning (CGL) strategy, which dissects and rebalances the positive-negative gradients originating from head and tail classes for more coordinated learning behaviors. Extensive experiments on the CholecT45 and CholecT50 datasets demonstrate the superiority of our proposed framework, validating its effectiveness in handling optimization conflicts.

Related papers

• UV-M3TL: A Unified and Versatile Multimodal Multi-Task Learning Framework for Assistive Driving Perception [71.19234323863314]
  We propose a framework to simultaneously recognize driver behavior, driver emotion, vehicle behavior, and traffic context.<n>Our framework incorporates two core components: dual-branch spatial channel multimodal embedding and adaptive feature-decoupled multi-task loss.<n>We evaluate our method on the AIDE dataset, and the experimental results demonstrate that UV-M3TL achieves state-of-the-art performance across all four tasks.
  arXiv  Detail & Related papers  (2026-02-02T03:35:24Z)
• Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning [27.472039054277644]
  Rep-MTL exploits the representation-level task saliency to quantify interactions between task-specific optimization and shared representation learning.<n>Rep-MTL aims to mitigate negative transfer by maintaining the effective training of individual tasks instead pure conflict-solving.
  arXiv  Detail & Related papers  (2025-07-28T17:59:28Z)
• Robust-Multi-Task Gradient Boosting [6.718184400443239]
  Multi-task learning (MTL) has shown effectiveness in exploiting shared information across tasks to improve generalization.<n>We propose Robust-Multi-Task Gradient Boosting (R-MTGB), a novel boosting framework that explicitly models and adapts to task heterogeneity during training.<n>R-MTGB structures the learning process into three blocks: (1) learning shared patterns, (2) partitioning sequential tasks into outliers and non-outliers with regularized parameters, and (3) fine-tuning task-specific predictors.
  arXiv  Detail & Related papers  (2025-07-15T15:31:12Z)
• InterroGate: Learning to Share, Specialize, and Prune Representations for Multi-task Learning [17.66308231838553]
  We propose a novel multi-task learning (MTL) architecture designed to mitigate task interference while optimizing inference computational efficiency. We employ a learnable gating mechanism to automatically balance the shared and task-specific representations while preserving the performance of all tasks.
  arXiv  Detail & Related papers  (2024-02-26T18:59:52Z)
• Pre-training Multi-task Contrastive Learning Models for Scientific Literature Understanding [52.723297744257536]
  Pre-trained language models (LMs) have shown effectiveness in scientific literature understanding tasks. We propose a multi-task contrastive learning framework, SciMult, to facilitate common knowledge sharing across different literature understanding tasks.
  arXiv  Detail & Related papers  (2023-05-23T16:47:22Z)
• Semantics-Depth-Symbiosis: Deeply Coupled Semi-Supervised Learning of Semantics and Depth [83.94528876742096]
  We tackle the MTL problem of two dense tasks, ie, semantic segmentation and depth estimation, and present a novel attention module called Cross-Channel Attention Module (CCAM) In a true symbiotic spirit, we then formulate a novel data augmentation for the semantic segmentation task using predicted depth called AffineMix, and a simple depth augmentation using predicted semantics called ColorAug. Finally, we validate the performance gain of the proposed method on the Cityscapes dataset, which helps us achieve state-of-the-art results for a semi-supervised joint model based on depth and semantic
  arXiv  Detail & Related papers  (2022-06-21T17:40:55Z)
• DenseMTL: Cross-task Attention Mechanism for Dense Multi-task Learning [18.745373058797714]
  We propose a novel multi-task learning architecture that leverages pairwise cross-task exchange through correlation-guided attention and self-attention. We conduct extensive experiments across three multi-task setups, showing the advantages of our approach compared to competitive baselines in both synthetic and real-world benchmarks.
  arXiv  Detail & Related papers  (2022-06-17T17:59:45Z)
• Task-Agnostic Continual Reinforcement Learning: Gaining Insights and Overcoming Challenges [27.474011433615317]
  Continual learning (CL) enables the development of models and agents that learn from a sequence of tasks. We investigate the factors that contribute to the performance differences between task-agnostic CL and multi-task (MTL) agents.
  arXiv  Detail & Related papers  (2022-05-28T17:59:00Z)
• Variational Multi-Task Learning with Gumbel-Softmax Priors [105.22406384964144]
  Multi-task learning aims to explore task relatedness to improve individual tasks. We propose variational multi-task learning (VMTL), a general probabilistic inference framework for learning multiple related tasks.
  arXiv  Detail & Related papers  (2021-11-09T18:49:45Z)
• Learning to Relate Depth and Semantics for Unsupervised Domain Adaptation [87.1188556802942]
  We present an approach for encoding visual task relationships to improve model performance in an Unsupervised Domain Adaptation (UDA) setting. We propose a novel Cross-Task Relation Layer (CTRL), which encodes task dependencies between the semantic and depth predictions. Furthermore, we propose an Iterative Self-Learning (ISL) training scheme, which exploits semantic pseudo-labels to provide extra supervision on the target domain.
  arXiv  Detail & Related papers  (2021-05-17T13:42:09Z)
• Task-Feature Collaborative Learning with Application to Personalized Attribute Prediction [166.87111665908333]
  We propose a novel multi-task learning method called Task-Feature Collaborative Learning (TFCL) Specifically, we first propose a base model with a heterogeneous block-diagonal structure regularizer to leverage the collaborative grouping of features and tasks. As a practical extension, we extend the base model by allowing overlapping features and differentiating the hard tasks.
  arXiv  Detail & Related papers  (2020-04-29T02:32:04Z)

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.