TrajPred: Trajectory-Conditioned Joint Embedding Prediction for Surgical Instrument-Tissue Interaction Recognition in Vision-Language Models

• URL: http://arxiv.org/abs/2603.06999v3
• Date: Sun, 15 Mar 2026 02:05:17 GMT
• Title: TrajPred: Trajectory-Conditioned Joint Embedding Prediction for Surgical Instrument-Tissue Interaction Recognition in Vision-Language Models
• Abstract summary: We propose TrajPred, a framework that encodes instrument trajectories to incorporate temporal motion cues.<n>We also incorporate prompt tuning and a verb-rephrasing technique to enable smooth adaptation to the instrument--tissue interaction recognition task.
• Score: 13.912437629529693
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recognizing instruments' interactions with tissues is essential for building context-aware AI assistants in robotic surgery. Vision-language models (VLMs) have opened a new avenue for surgical perception and achieved better generalization on a wide range of tasks compared to conventional task-specific deep learning approaches. However, their performance on instrument--tissue interaction recognition remains limited, largely due to two challenges: (1) many models do not effectively leverage temporal information, and (2) alignment between vision and text often misses fine-grained action details. To address these issues, we propose TrajPred, a framework that encodes instrument trajectories to incorporate temporal motion cues and, conditioned on these trajectories, introduces a predictor module to generate visual semantic embeddings that better capture fine-grained action details. We further incorporate prompt tuning and a verb-rephrasing technique to enable smooth adaptation to the instrument--tissue interaction recognition task. Extensive experiments on the public laparoscopic benchmark, CholecT50, show that our method improves both Average Precision and Top-K accuracy. We also investigate whether visual embeddings of instrument--tissue interaction regions align better with the corresponding text by visualizing the cosine similarity between visual and textual embeddings. The visualization results indicate that the proposed method improves alignment between relevant visual and textual representations.

Related papers

• LAVIFT: Latent-Action-Guided Vision Fine-Tuning for Surgical Interaction Recognition [13.912437629529691]
  We introduce LAViFiT, an end-to-end latent-action-guided framework for vision-language fine-tuning.<n>An inverse dynamics model captures the visual changes induced by each action, while a forward world model drives the encoder to represent action-relevant regions.<n>A patch-level SIG Regularizer prevents local feature collapse without additional supervision, such as bounding boxes or pseudo-labels.
  arXiv  Detail & Related papers  (2026-07-22T08:20:52Z)
• Contextualized Representation Learning for Effective Human-Object Interaction Detection [17.242400169885453]
  Human-Object Interaction (HOI) detection aims to simultaneously localize human-object pairs and recognize their interactions.<n>We introduce a Contextualized Representation Learning that integrates both affordance-guided reasoning and contextual prompts.<n>Our proposed method demonstrates superior performance on both the HICO-Det and V-COCO datasets in most scenarios.
  arXiv  Detail & Related papers  (2025-09-16T08:03:16Z)
• Open-Vocabulary HOI Detection with Interaction-aware Prompt and Concept Calibration [42.24582981160835]
  Open Human-Object Interaction (HOI) detection aims to detect interactions between humans and objects.<n>Current methods often rely on Vision and Language Models (VLMs) but face challenges due to suboptimal image encoders.<n>We propose INteraction-aware Prompting with Concept (INP-CC), an end-to-end open-vocabulary HOI detector.
  arXiv  Detail & Related papers  (2025-08-05T08:33:58Z)
• Exploring Interactive Semantic Alignment for Efficient HOI Detection with Vision-language Model [3.3772986620114387]
  We introduce ISA-HOI, which extensively leverages knowledge from CLIP, aligning interactive semantics between visual and textual features. Our method achieves competitive results on the HICO-DET and V-COCO benchmarks with much fewer training epochs, and outperforms the state-of-the-art under zero-shot settings.
  arXiv  Detail & Related papers  (2024-04-19T07:24:32Z)
• Instrument-tissue Interaction Detection Framework for Surgical Video Understanding [31.822025965225016]
  We present an Instrument-Tissue Interaction Detection Network (ITIDNet) to detect the quintuple for surgery videos understanding. Specifically, we propose a Snippet Consecutive Feature (SCF) Layer to enhance features by modeling relationships of proposals in the current frame using global context information in the video snippet. To reason relationships between instruments and tissues, a Temporal Graph (TG) Layer is proposed with intra-frame connections to exploit relationships between instruments and tissues in the same frame and inter-frame connections to model the temporal information for the same instance.
  arXiv  Detail & Related papers  (2024-03-30T11:21:11Z)
• Disentangled Interaction Representation for One-Stage Human-Object Interaction Detection [70.96299509159981]
  Human-Object Interaction (HOI) detection is a core task for human-centric image understanding. Recent one-stage methods adopt a transformer decoder to collect image-wide cues that are useful for interaction prediction. Traditional two-stage methods benefit significantly from their ability to compose interaction features in a disentangled and explainable manner.
  arXiv  Detail & Related papers  (2023-12-04T08:02:59Z)
• Enhancing HOI Detection with Contextual Cues from Large Vision-Language Models [56.257840490146]
  ConCue is a novel approach for improving visual feature extraction in HOI detection. We develop a transformer-based feature extraction module with a multi-tower architecture that integrates contextual cues into both instance and interaction detectors.
  arXiv  Detail & Related papers  (2023-11-26T09:11:32Z)
• Re-mine, Learn and Reason: Exploring the Cross-modal Semantic Correlations for Language-guided HOI detection [57.13665112065285]
  Human-Object Interaction (HOI) detection is a challenging computer vision task. We present a framework that enhances HOI detection by incorporating structured text knowledge.
  arXiv  Detail & Related papers  (2023-07-25T14:20:52Z)
• Fine-Grained Semantically Aligned Vision-Language Pre-Training [151.7372197904064]
  Large-scale vision-language pre-training has shown impressive advances in a wide range of downstream tasks. Existing methods mainly model the cross-modal alignment by the similarity of the global representations of images and texts. We introduce LO, a fine-grained semantically aLigned visiOn-langUage PrE-training framework, which learns fine-grained semantic alignment from the novel perspective of game-theoretic interactions.
  arXiv  Detail & Related papers  (2022-08-04T07:51:48Z)
• Vision-Language Pre-Training for Boosting Scene Text Detectors [57.08046351495244]
  We specifically adapt vision-language joint learning for scene text detection. We propose to learn contextualized, joint representations through vision-language pre-training. The pre-trained model is able to produce more informative representations with richer semantics.
  arXiv  Detail & Related papers  (2022-04-29T03:53:54Z)
• Visualizing and Understanding Patch Interactions in Vision Transformer [96.70401478061076]
  Vision Transformer (ViT) has become a leading tool in various computer vision tasks. We propose a novel explainable visualization approach to analyze and interpret the crucial attention interactions among patches for vision transformer.
  arXiv  Detail & Related papers  (2022-03-11T13:48:11Z)

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.