Search Results for author:
Found 8 papers, 6 papers with code
RoboSpatial: Teaching Spatial Understanding to 2D and 3D Vision-Language Models for Robotics
Spatial understanding is a crucial capability for robots to make grounded decisions based on their environment.
VisualAgentBench: Towards Large Multimodal Models as Visual Foundation Agents
Large Multimodal Models (LMMs) have ushered in a new era in artificial intelligence, merging capabilities in both language and vision to form highly capable Visual Foundation Agents.
Dual-View Visual Contextualization for Web Navigation
Automatic web navigation aims to build a web agent that can follow language instructions to execute complex and diverse tasks on real-world websites.
BioCLIP: A Vision Foundation Model for the Tree of Life
We then develop BioCLIP, a foundation model for the tree of life, leveraging the unique properties of biology captured by TreeOfLife-10M, namely the abundance and variety of images of plants, animals, and fungi, together with the availability of rich structured biological knowledge.
LLM-Planner: Few-Shot Grounded Planning for Embodied Agents with Large Language Models
In this work, we propose a novel method, LLM-Planner, that harnesses the power of large language models to do few-shot planning for embodied agents.
One Step at a Time: Long-Horizon Vision-and-Language Navigation with Milestones
We study the problem of developing autonomous agents that can follow human instructions to infer and perform a sequence of actions to complete the underlying task.
Improving Neural Named Entity Recognition with Gazetteers
The goal of this work is to improve the performance of a neural named entity recognition system by adding input features that indicate a word is part of a name included in a gazetteer.
Using Chinese Glyphs for Named Entity Recognition
In this paper for Chinese NER systems, we do not use these traditional features but we use lexicographic features of Chinese characters.
