Deep Learning
CAP: Correlation-Aware Pruning for Highly-Accurate Sparse Vision Models
To further showcase CAP's accuracy and scalability,
Semi-Supervised Domain Generalization with Known and Unknown Classes
Semi-Supervised Domain Generalization (SSDG) aims to learn a model that is generalizable to an unseen target domain with only a few labels, and most existing SSDG methods assume that unlabeled training and testing samples are all known classes. However, a more realistic scenario is that known classes may be mixed with some unknown classes in unlabeled training and testing data.
Supplementary Material for Text Promptable Surgical Instrument Segmentation with Vision-Language Models Zijian Zhou
They are used in our experiments section. OpenAI GPT -4 based prompts The input template for OpenAI GPT -4 is defined as: Please describe the appearance of [class_name] in endoscopic surgery, and change the description to a phrase with subject, and not use colons. The dataset consists of both training and test cases. Each video is recorded at 25 FPS and has annotations for instruments and operation phases. For EndoVis2019, the results are shown in Tab. 1, our method (input size 448) notably surpasses the competition's top performers, with +3% increase in DSC and +2% enhancement in NSD, which demonstrates the superiority of our method.
Text Promptable Surgical Instrument Segmentation with Vision-Language Models
Despite this recognised importance, existing automatic surgical instrument segmentation faces significant challenges. First, with fast-paced advances in MIS, there is a surge in the variety of surgical instruments from different vendors. This is however compounded with the lack of a comprehensive and large-scale dataset dedicated to the learning of surgical instrument segmentation.
Networks are Slacking Off: Understanding Generalization Problem in Image Deraining
We call this dilemma the generalization problem.
