Human Object Interaction
Human-object interaction (HOI) research focuses on understanding and modeling how humans interact with objects in images and videos, aiming to accurately detect, classify, and even generate these interactions. Current research emphasizes developing robust models, often leveraging transformer architectures and diffusion models, to handle challenges like occlusion, diverse object categories, and limited training data, particularly in zero-shot and few-shot learning scenarios. This field is crucial for advancing computer vision, robotics, and human-computer interaction, with applications ranging from improved activity recognition and virtual/augmented reality to more intuitive human-robot collaboration and assistive technologies. The development of large-scale, high-quality datasets with detailed annotations is also a significant area of focus.
Papers
Understanding Spatio-Temporal Relations in Human-Object Interaction using Pyramid Graph Convolutional Network
Hao Xing, Darius Burschka
G$^{2}$TR: Generalized Grounded Temporal Reasoning for Robot Instruction Following by Combining Large Pre-trained Models
Riya Arora, Niveditha Narendranath, Aman Tambi, Sandeep S. Zachariah, Souvik Chakraborty, Rohan Paul