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Found 36 papers, 22 papers with code
Navigating the Designs of Privacy-Preserving Fine-tuning for Large Language Models
Instruction tuning has proven effective in enhancing Large Language Models' (LLMs) performance on downstream tasks.
Scaling Laws for Floating Point Quantization Training
Low-precision training is considered an effective strategy for reducing both training and downstream inference costs.
ETSM: Automating Dissection Trajectory Suggestion and Confidence Map-Based Safety Margin Prediction for Robot-assisted Endoscopic Submucosal Dissection
Robot-assisted Endoscopic Submucosal Dissection (ESD) improves the surgical procedure by providing a more comprehensive view through advanced robotic instruments and bimanual operation, thereby enhancing dissection efficiency and accuracy.
PDZSeg: Adapting the Foundation Model for Dissection Zone Segmentation with Visual Prompts in Robot-assisted Endoscopic Submucosal Dissection
Conclusion: The PDZSeg model effectively utilizes visual prompts to enhance segmentation performance and user experience, supported by the novel ESD-DZSeg dataset as a benchmark for dissection zone segmentation in ESD.
Persistent Homology for Structural Characterization in Disordered Systems
We propose a unified framework based on persistent homology (PH) to characterize both local and global structures in disordered systems.
Lossless KV Cache Compression to 2%
Large language models have revolutionized data processing in numerous domains, with their ability to handle extended context reasoning receiving notable recognition.
Benchmarking Robustness of Endoscopic Depth Estimation with Synthetically Corrupted Data
To tackle this issue, our study presents a benchmark for assessing the robustness of endoscopic depth estimation models.
HMoE: Heterogeneous Mixture of Experts for Language Modeling
To address the imbalance in expert activation, we propose a novel training objective that encourages the frequent activation of smaller experts, enhancing computational efficiency and parameter utilization.
Surgical-VQLA++: Adversarial Contrastive Learning for Calibrated Robust Visual Question-Localized Answering in Robotic Surgery
In particular, surgical VQA can enhance the interpretation of surgical data, aiding in accurate diagnoses, effective education, and clinical interventions.
A Review of 3D Reconstruction Techniques for Deformable Tissues in Robotic Surgery
As a crucial and intricate task in robotic minimally invasive surgery, reconstructing surgical scenes using stereo or monocular endoscopic video holds immense potential for clinical applications.
SAM 2 in Robotic Surgery: An Empirical Evaluation for Robustness and Generalization in Surgical Video Segmentation
The recent Segment Anything Model (SAM) 2 has demonstrated remarkable foundational competence in semantic segmentation, with its memory mechanism and mask decoder further addressing challenges in video tracking and object occlusion, thereby achieving superior results in interactive segmentation for both images and videos.
EndoDAC: Efficient Adapting Foundation Model for Self-Supervised Depth Estimation from Any Endoscopic Camera
We propose Endoscopic Depth Any Camera (EndoDAC) which is an efficient self-supervised depth estimation framework that adapts foundation models to endoscopic scenes.
Building a Japanese Document-Level Relation Extraction Dataset Assisted by Cross-Lingual Transfer
In our proposal, annotators edit relation predictions from a model trained on the transferred dataset.
Adapting SAM for Surgical Instrument Tracking and Segmentation in Endoscopic Submucosal Dissection Videos
Specifically, with a tiny subset of frames for segmentation, we ensure accurate segmentation across the entire surgical video.
An Efficient MLP-based Point-guided Segmentation Network for Ore Images with Ambiguous Boundary
The precise segmentation of ore images is critical to the successful execution of the beneficiation process.
OSSAR: Towards Open-Set Surgical Activity Recognition in Robot-assisted Surgery
Existing algorithms dedicated to surgical activity recognition predominantly cater to pre-defined closed-set paradigms, ignoring the challenges of real-world open-set scenarios.
Learning-Based Difficulty Calibration for Enhanced Membership Inference Attacks
The experiment results show that LDC-MIA can improve TPR at low FPR by up to 4x compared to the other difficulty calibration based MIAs.
SAR-RARP50: Segmentation of surgical instrumentation and Action Recognition on Robot-Assisted Radical Prostatectomy Challenge
Surgical tool segmentation and action recognition are fundamental building blocks in many computer-assisted intervention applications, ranging from surgical skills assessment to decision support systems.
Spatial-wise Dynamic Distillation for MLP-like Efficient Visual Fault Detection of Freight Trains
Existing modeling shortcomings of spatial invariance and pooling layers in conventional CNNs often ignore the neglect of crucial global information, resulting in error localization for fault objection tasks of freight trains.
SAM Meets Robotic Surgery: An Empirical Study on Generalization, Robustness and Adaptation
Our extensive evaluation results reveal that although SAM shows remarkable zero-shot generalization ability with bounding box prompts, it struggles to segment the whole instrument with point-based prompts and unprompted settings.
Landmark Detection using Transformer Toward Robot-assisted Nasal Airway Intubation
This paper proposes a transformer-based landmark detection solution with deformable DeTR and the semantic-aligned-matching module for detecting landmarks in robot-assisted intubation.
Semi-supervised Learning for Segmentation of Bleeding Regions in Video Capsule Endoscopy
Notably, we develop the segmentation annotations for this dataset ourselves.
Rectifying Noisy Labels with Sequential Prior: Multi-Scale Temporal Feature Affinity Learning for Robust Video Segmentation
Therefore, Temporal Feature Affinity Learning (TFAL) is devised to indicate possible noisy labels by evaluating the affinity between pixels in two adjacent frames.
Ranked #2 on
Video Polyp Segmentation
on SUN-SEG-Easy
LLCaps: Learning to Illuminate Low-Light Capsule Endoscopy with Curved Wavelet Attention and Reverse Diffusion
Given the exuberant development of the denoising diffusion probabilistic model (DDPM) in computer vision, we introduce a WCE LLIE framework based on the multi-scale convolutional neural network (CNN) and reverse diffusion process.
Generative Data Augmentation for Aspect Sentiment Quad Prediction
Aspect sentiment quad prediction (ASQP) analyzes the aspect terms, opinion terms, sentiment polarity, and aspect categories in a text.
Ranked #3 on
Aspect-Based Sentiment Analysis (ABSA)
on ASQP
Generalizing Surgical Instruments Segmentation to Unseen Domains with One-to-Many Synthesis
In this work, we mitigate data-related issues by efficiently leveraging minimal source images to generate synthetic surgical instrument segmentation datasets and achieve outstanding generalization performance on unseen real domains.
Curriculum-Based Augmented Fourier Domain Adaptation for Robust Medical Image Segmentation
Accurate and robust medical image segmentation is fundamental and crucial for enhancing the autonomy of computer-aided diagnosis and intervention systems.
S$^2$ME: Spatial-Spectral Mutual Teaching and Ensemble Learning for Scribble-supervised Polyp Segmentation
Furthermore, to produce reliable mixed pseudo labels, which enhance the effectiveness of ensemble learning, we introduce a novel adaptive pixel-wise fusion technique based on the entropy guidance from the spatial and spectral branches.
Surgical tool classification and localization: results and methods from the MICCAI 2022 SurgToolLoc challenge
Unfortunately, obtaining the annotations needed to train machine learning models to identify and localize surgical tools is a difficult task.
SAM Meets Robotic Surgery: An Empirical Study in Robustness Perspective
In this empirical study, we investigate the robustness and zero-shot generalizability of the SAM in the domain of robotic surgery in various settings of (i) prompted vs. unprompted; (ii) bounding box vs. points-based prompt; (iii) generalization under corruptions and perturbations with five severity levels; and (iv) state-of-the-art supervised model vs. SAM.
DREEAM: Guiding Attention with Evidence for Improving Document-Level Relation Extraction
First, we propose DREEAM, a memory-efficient approach that adopts evidence information as the supervisory signal, thereby guiding the attention modules of the DocRE system to assign high weights to evidence.
Ranked #1 on
Relation Extraction
on ReDocRED
Rethinking Surgical Instrument Segmentation: A Background Image Can Be All You Need
Our empirical analysis suggests that without the high cost of data collection and annotation, we can achieve decent surgical instrument segmentation performance.
Semi-Supervised Formality Style Transfer with Consistency Training
In this work, we propose a simple yet effective semi-supervised framework to better utilize source-side unlabeled sentences based on consistency training.
Ranked #1 on
Formality Style Transfer
on GYAFC
(using extra training data)
Towards Effective Multi-Task Interaction for Entity-Relation Extraction: A Unified Framework with Selection Recurrent Network
Entity-relation extraction aims to jointly solve named entity recognition (NER) and relation extraction (RE).
Attention-based Relational Graph Convolutional Network for Target-Oriented Opinion Words Extraction
It aims to extract the corresponding opinion words for a given opinion target in a review sentence.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+2
On the Performance of Generative Adversarial Network (GAN) Variants: A Clinical Data Study
Generative Adversarial Network (GAN) is a useful type of Neural Networks in various types of applications including generative models and feature extraction.
