Latent-Action-Guided Video-Language Feature Learning for Surgical Instrument-Tissue Interaction Recognition

arXiv:2607.19889 · cs.CV · Submitted 2026-07-22 · Read on arXiv

cs.CV

Submitted: 2026-07-22

Updated: 2026-10-06

Project page: https://marginlab.github.io/AI-for-healthcare/lavifit

Terminology

• Fine-Tuning — 40× in this paper · explained in 5 episodes
• Grounding — 18× in this paper · explained in 3 episodes
• Ablation — 15× in this paper · explained in 1 episode
• World Model — 15× in this paper · explained in 7 episodes
• Alignment — 14× in this paper · explained in 1 episode
• Transformer — 10× in this paper · explained in 2 episodes
• Benchmark — 7× in this paper · explained in 2 episodes
• Distillation — 7× in this paper · explained in 2 episodes
• Foundation Model — 7× in this paper · explained in 3 episodes
• Vision-language models — 7× in this paper · explained in 2 episodes
• Ablation study — 6× in this paper · explained in 1 episode
• Computer Vision And Pattern Recognition — 6× in this paper
• Adaptation — 5× in this paper · explained in 1 episode
• Foundation Models — 5× in this paper · explained in 7 episodes
• International Conference On Learning Representations — 5× in this paper
• Self-Distillation — 5× in this paper · explained in 7 episodes
• Semantic Alignment — 5× in this paper · explained in 3 episodes
• Downstream Tasks — 4× in this paper · explained in 2 episodes
• Embeddings — 4× in this paper · explained in 2 episodes
• Regularization — 4× in this paper · explained in 3 episodes
• Benchmarking — 3× in this paper · explained in 3 episodes
• Pseudo-labels — 3× in this paper · explained in 2 episodes
• Representation Learning — 3× in this paper · explained in 2 episodes
• Self-Supervised Learning — 3× in this paper · explained in 8 episodes
• Vision-Language Models (VLMs) — 3× in this paper · explained in 6 episodes
• Aggregation — 2× in this paper · explained in 1 episode
• Attention Mechanism — 2× in this paper · explained in 3 episodes
• Contrastive Learning — 2× in this paper · explained in 5 episodes
• Diffusion Model — 2× in this paper · explained in 8 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Action Space — 1× in this paper · explained in 1 episode
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Explainability — 1× in this paper · explained in 3 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Latent World Models — 1× in this paper · explained in 2 episodes
• Parameter Efficiency — 1× in this paper · explained in 3 episodes
• Quantization — 1× in this paper · explained in 12 episodes
• Transferability — 1× in this paper · explained in 4 episodes

Sources

• CLIP4Clip: An Empirical Study of CLIP for End to End Video Clip Retrieval
• SurgMotion: A Video-Native Foundation Model for Universal Understanding of Surgical Videos
• fine-CLIP: Enhancing Zero-Shot Fine-Grained Surgical Action Recognition with Vision-Language Models
• EmbeddingGemma: Powerful and Lightweight Text Representations
• DINOv2: Learning Robust Visual Features without Supervision
• V-JEPA 2.1: Unlocking Dense Features in Video Self-Supervised Learning
• V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
• UniVLA: Learning to Act Anywhere with Task-centric Latent Actions
• VLA-JEPA: Enhancing Vision-Language-Action Model with Latent World Model
• CholecSeg8k: A Semantic Segmentation Dataset for Laparoscopic Cholecystectomy Based on Cholec80
• LeJEPA: Provable and Scalable Self-Supervised Learning Without the Heuristics
• SurgMAE: Masked Autoencoders for Long Surgical Video Analysis
• Whether and When does Endoscopy Domain Pretraining Make Sense?
• SurgVLM: A Large Vision-Language Model and Systematic Evaluation Benchmark for Surgical Intelligence
• Data Splits and Metrics for Method Benchmarking on Surgical Action Triplet Datasets
• Surgical Triplet Recognition via Diffusion Model
• TrajPred: Trajectory-Conditioned Joint Embedding Prediction for Surgical Instrument-Tissue Interaction Recognition in Vision-Language Models
• Delta-JEPA: Learning Action-Sensitive World Models via Latent Difference Decoding
• Do Egocentric Video-Language Models Truly Understand Hand-Object Interactions?
• Latent Action Learning Requires Supervision in the Presence of Distractors

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