arXiv subjects

Benny Lo

Publications and source records attributed to Benny Lo.

22 records · Page 2Linked to original sources

arXiv2021

TransAction: ICL-SJTU Submission to EPIC-Kitchens Action Anticipation Challenge 2021

In this report, the technical details of our submission to the EPIC-Kitchens Action Anticipation Challenge 2021 are given. We developed a hierarchical attention model for action anticipation, which leverages Transformer-based attention mechanism to aggregate features across temporal dimension, modalities, symbiotic branches respectively. In terms of Mean Top-5 Recall of action, our submission with team name ICL-SJTU achieved 13.39% for overall testing set, 10.05% for unseen subsets and 11.88% for tailed subsets. Additionally, it is noteworthy that our submission ranked 1st in terms of verb class in all three (sub)sets.

cs.CV↗

arXiv2021

An Intelligent Passive Food Intake Assessment System with Egocentric Cameras

Malnutrition is a major public health concern in low-and-middle-income countries (LMICs). Understanding food and nutrient intake across communities, households and individuals is critical to the development of health policies and interventions. To ease the procedure in conducting large-scale dietary assessments, we propose to implement an intelligent passive food intake assessment system via egocentric cameras particular for households in Ghana and Uganda. Algorithms are first designed to remove redundant images for minimising the storage memory. At run time, deep learning-based semantic segmentation is applied to recognise multi-food types and newly-designed handcrafted features are extracted for further consumed food weight monitoring. Comprehensive experiments are conducted to validate our methods on an in-the-wild dataset captured under the settings which simulate the unique LMIC conditions with participants of Ghanaian and Kenyan origin eating common Ghanaian/Kenyan dishes. To demonstrate the efficacy, experienced dietitians are involved in this research to perform the visual portion size estimation, and their predictions are compared to our proposed method. The promising results have shown that our method is able to reliably monitor food intake and give feedback on users' eating behaviour which provides guidance for dietitians in regular dietary assessment.

cs.CV↗

arXiv2021

Surgical Gesture Recognition Based on Bidirectional Multi-Layer Independently RNN with Explainable Spatial Feature Extraction

Minimally invasive surgery mainly consists of a series of sub-tasks, which can be decomposed into basic gestures or contexts. As a prerequisite of autonomic operation, surgical gesture recognition can assist motion planning and decision-making, and build up context-aware knowledge to improve the surgical robot control quality. In this work, we aim to develop an effective surgical gesture recognition approach with an explainable feature extraction process. A Bidirectional Multi-Layer independently RNN (BML-indRNN) model is proposed in this paper, while spatial feature extraction is implemented via fine-tuning of a Deep Convolutional Neural Network(DCNN) model constructed based on the VGG architecture. To eliminate the black-box effects of DCNN, Gradient-weighted Class Activation Mapping (Grad-CAM) is employed. It can provide explainable results by showing the regions of the surgical images that have a strong relationship with the surgical gesture classification results. The proposed method was evaluated based on the suturing task with data obtained from the public available JIGSAWS database. Comparative studies were conducted to verify the proposed framework. Results indicated that the testing accuracy for the suturing task based on our proposed method is 87.13%, which outperforms most of the state-of-the-art algorithms.

cs.CV↗

arXiv2020

Real-time Surgical Environment Enhancement for Robot-Assisted Minimally Invasive Surgery Based on Super-Resolution

In Robot-Assisted Minimally Invasive Surgery (RAMIS), a camera assistant is normally required to control the position and zooming ratio of the laparoscope, following the surgeon's instructions. However, moving the laparoscope frequently may lead to unstable and suboptimal views, while the adjustment of zooming ratio may interrupt the workflow of the surgical operation. To this end, we propose a multi-scale Generative Adversarial Network (GAN)-based video super-resolution method to construct a framework for automatic zooming ratio adjustment. It can provide automatic real-time zooming for high-quality visualization of the Region Of Interest (ROI) during the surgical operation. In the pipeline of the framework, the Kernel Correlation Filter (KCF) tracker is used for tracking the tips of the surgical tools, while the Semi-Global Block Matching (SGBM) based depth estimation and Recurrent Neural Network (RNN)-based context-awareness are developed to determine the upscaling ratio for zooming. The framework is validated with the JIGSAW dataset and Hamlyn Centre Laparoscopic/Endoscopic Video Datasets, with results demonstrating its practicability.

eess.IV↗