11 accepted papers
6-DoF object grasping is a crucial skill for embodied intelligent robots. Previous methods often rely on large-scale networks for feature extraction, followed by grasp pose prediction, which increases the network
Recent advances in Large Language Models (LLMs) have enhanced text-based recommendation by enriching traditional ID-based methods with semantic generalization capabilities. Text-based methods typically encode item textual information via prompt design and generate discrete semantic IDs through item
Object pose estimation is a key task for embodied robots, enabling them to interact with objects effectively. Category-level object pose estimation provides a way for robots to estimate the pose of unknown objects. However, estimating object pose from point clouds alone remains challenging. In this
Category-level object pose estimation is an important task in computer vision. Some prior methods based on assumptions often struggle with drastic changes in object appearance. To address this challenge, we propose a new method for object pose estimation based on object-adaptive keypoints. In this p…
While most current RGB-D-based category-level object pose estimation methods achieve strong performance, they face significant challenges in scenes lacking depth information. In this paper, we propose a novel category-level object pose estimation approach that relies solely on RGB images. This metho
Achieving rapid and accurate dynamic obstacle avoidance is crucial for enhancing the survivability of unmanned aerial vehicles (UAVs) in hazardous conditions. To accomplish dynamic obstacle avoidance, sensors with high temporal resolution and efficient processing models are required. Dynamic vision
Although there has been significant progress in category-level object pose estimation in recent years, there is still considerable room for improvement. In this paper, we propose a novel transformer-based category-level 6D pose estimation method called CatFormer to enhance the accuracy pose estimat…
Knowledge graph integration typically suffers from the widely existing dangling entities that cannot find alignment cross knowledge graphs (KGs). The dangling entity set is unavailable in most real-world scenarios, and manually mining the entity pairs that consist of entities with the same meaning i…
Entities lie in the heart of biomedical natural language understanding, and the biomedical entity linking (EL) task remains challenging due to the fine-grained and diversiform concept names. Generative methods achieve remarkable performances in general domain EL with less memory usage while requirin…
Distantly-supervised named entity recognition (NER) locates and classifies entities using only knowledge bases and unlabeled corpus to mitigate the reliance on human-annotated labels. The distantly annotated data suffer from the noise in labels, and previous works on DSNER have proved the importance…
In this letter, a novel grasp detection neural network Squeeze-and-Excitation ResUNet (SE-ResUNet) is developed, where the residual block with the channel attention is integrated. The proposed framework can not only generate the grasp pose from the RGB-D images, but also predict the quality score of