4 citations · 5 across the 6 of their papers we have counts for
6 papers
Robot Manipulation in Salient Vision through Referring Image Segmentation and Geometric Constraints
Chen Jiang, Allie Luo, Martin Jagersand
In this paper, we perform robot manipulation activities in real-world environments with language contexts by integrating a compact referring image segmentation model into the robot…
Revisiting Sparse Rewards for Goal-Reaching Reinforcement Learning
Gautham Vasan, Yan Wang, Fahim Shahriar +3
Many real-world robot learning problems, such as pick-and-place or arriving at a destination, can be seen as a problem of reaching a goal state as soon as possible. These problems,…
CLIPUNetr: Assisting Human-robot Interface for Uncalibrated Visual Servoing Control with CLIP-driven Referring Expression Segmentation
Chen Jiang, Yuchen Yang, Martin Jagersand
The classical human-robot interface in uncalibrated image-based visual servoing (UIBVS) relies on either human annotations or semantic segmentation with categorical labels. Both me…
Deep Semantic Segmentation for Automated Driving: Taxonomy, Roadmap and Challenges
Mennatullah Siam, Sara Elkerdawy, Martin Jagersand +1
Semantic segmentation was seen as a challenging computer vision problem few years ago. Due to recent advancements in deep learning, relatively accurate solutions are now possible f…
Incremental Learning for Robot Perception through HRI
Sepehr Valipour, Camilo Perez, Martin Jagersand
Scene understanding and object recognition is a difficult to achieve yet crucial skill for robots. Recently, Convolutional Neural Networks (CNN), have shown success in this task. H…
Unifying Registration based Tracking: A Case Study with Structural Similarity
Abhineet Singh, Mennatullah Siam, Martin Jagersand
This paper adapts a popular image quality measure called structural similarity for high precision registration based tracking while also introducing a simpler and faster variant of…