10 accepted papers
Retinal endolaser photocoagulation (REPC) is a repetitive intraocular surgical procedure that could greatly benefit from automation and distance-based control, improving both efficiency and safety. This work presents a robotic system designed for automated REPC, utilizing instrument-integrated optic…
The fragility of ocular tissues combined with the limited surgical workspace demands precise instrument control and focus, making sensor-integrated robotic systems a promising solution. In this paper, we introduce a surgical system for telemanipulated endolaser photocoagulation that leverages instru
The fragility of ocular tissues combined with the limited surgical workspace demands precise instrument control and focus, making sensor-integrated robotic systems a promising solution. In this paper, we introduce a surgical system for telemanipulated endolaser photocoagulation that leverages instru…
The rapid growth of modern training datasets has significantly increased computational cost, motivating dataset pruning(DP) methods which retain only a subset of informative samples to reduce training cost. Existing pruning criteria typically rely on either intrinsic signals that assess samples inde…
Class incremental semantic segmentation (CISS) aims to segment new classes during continual steps while preventing the forgetting of old knowledge. Existing methods alleviate catastrophic forgetting by replaying distributions of previously learned classes using stored prototypes or features. However…
The ever-growing size of training datasets enhances the generalization capability of machine learning models but also incurs exorbitant computational costs. Existing data pruning approaches aim to accelerate training by removing those less important samples. However, they often rely on gradients or…
In real-word scenarios, person re-identification (ReID) expects to identify a person-of-interest via the descriptive query, regardless of whether the query is a single modality or a combination of multiple modalities. However, existing methods and datasets remain constrained to limited modalities, f…
In recent years, semantic segmentation has flourished in various applications. However, the high computational cost remains a significant challenge that hinders its further adoption. The filter pruning method for structured network slimming offers a direct and effective solution for the reduction o…
Open-vocabulary semantic segmentation is a challenging task which requires the model to output semantic masks of an image beyond a close-set vocabulary. Although many efforts have been made to utilize powerful CLIP models to accomplish this task they are still easily overfitting to training classes…
Recent real-time semantic segmentation methods usually adopt an additional semantic branch to pursue rich long-range context. However, the additional branch incurs undesirable computational overhead and slows inference speed. To eliminate this dilemma, we propose SCTNet, a single branch CNN with tra…