The existing works on object-level language grounding with 3D objects mostly focus on improving performance by utilizing the off-the-shelf pre-trained models to capture features, such as viewpoint selection or geometric priors. However, they have failed to consider exploring the cross-modal ...
The zero-shot capabilities of LERF leads to potential use cases in robotics, analyzing vision- language models, and interacting with 3D scenes. Code and data is available at https://lerf.io. 2. Related Work Open-Vocabulary Object Detection A number of ap- proaches study detecting objects in...
Our dynamics model learns not just what objects are but also what they do: glass cups break when thrown, plastic ones don't. We then use it as the interface to our language model, giving us a unified model of linguistic form and grounded meaning. PIGLeT~can read a sentence, simulate ...
It enables computers to recognize and interpret objects, actions, and scenes depicted in images or videos, facilitating meaningful interactions with users. By grounding visual information, computers can understand and respond to user queries, instructions, and gestures, opening up new possibilities for ...
🔥🔥🔥Freeze-Omni: A Smart and Low Latency Speech-to-speech Dialogue Model with Frozen LLM Project Page|Paper|GitHub A speech-to-speech dialogue model with both low-latency and high intelligence while the training process is based on a frozen LLM. ✨ ...
3D visual grounding is a critical skill for household robots, enabling them to navigate, manipulate objects, and answer questions based on their environment. While existing approaches often rely on extensive labeled data or exhibit limitations in handling complex language queries, we propose LLM-Grounde...
3D Points ScanQA "ScanQA: 3D Question Answering for Spatial Scene Understanding". Azuma D, Miyanishi T, Kurita S, et al.. CVPR 2022. [Paper] [Github]. ScanReason "ScanReason: Empowering 3D Visual Grounding with Reasoning Capabilities". Zhu C, Wang T, Zhang W, et al.. arXiv 2024. ...
The VR group was engaged in 3D virtual environments in which the learners could dynamically view or play with the objects in an interactive manner. Third, the right SMG is shown to be more activated in simulated partner-based learning than individual-based learning of word meanings, indicating ...
The naming game was developed with a population of 50 agents having the same sensory, motor and linguistic configurations, and an environment with 8 types of abstract objects, described by a set of modal features. Each feature is composed by 2 quality dimensions, with random values in the inte...
This assumption, that semantic knowledge exists entirely in objects and independent of the subject domain, has been proved false. Though human-like explanations were generated, many questions remain (Lester and Porter, 1996): Is English prose the most effective way to communicate about knowledge of...