5 accepted papers
We propose a generalized method for boosting the generalization ability of pre-trained vision-language models (VLMs) while fine-tuning on downstream few-shot tasks. The idea is realized by exploiting out-of-distribution (OOD) detection to predict whether a sample belongs to a base distribution or a…
This paper presents Incremental Vision-Language Object Detection (IVLOD), a novel learning task designed to incrementally adapt pre-trained Vision-Language Object Detection Models (VLODMs) to various specialized domains, while simultaneously preserving their zero-shot generalization capabilities for…
We introduce a new task, called Class Incremental Robotic Pick-and-Place (CIRPAP), which calls for the capacity to learn to pick and place new categories of objects while retaining the skill of dealing with the previously learned ones. CIRPAP faces three challenges: catastrophic forgetting, few-shot
When aggregating local information from neighbors, prevailing 3D instance segmentation backbones only leverage 3D coordinates to find neighboring points without identifying whether these points are from the same object as the query point, which causes the model to gather excessive noisy features. Be
Accurate recognition of human pose and prediction of human motion intention are essential for exoskeleton robots to provide effective assistance. In recent years, inertial measurement units (IMUs) have been widely used to estimate human pose due to their stability and economy. However, physical sens