Search Results for author:
Found 8 papers, 7 papers with code
A Survey of State of the Art Large Vision Language Models: Alignment, Benchmark, Evaluations and Challenges
Multimodal Vision Language Models (VLMs) have emerged as a transformative topic at the intersection of computer vision and natural language processing, enabling machines to perceive and reason about the world through both visual and textual modalities.
SOAR: Self-supervision Optimized UAV Action Recognition with Efficient Object-Aware Pretraining
We introduce SOAR, a novel Self-supervised pretraining algorithm for aerial footage captured by Unmanned Aerial Vehicles (UAVs).
AutoHallusion: Automatic Generation of Hallucination Benchmarks for Vision-Language Models
This motivates the development of AutoHallusion, the first automated benchmark generation approach that employs several key strategies to create a diverse range of hallucination examples.
Ranked #1 on
Visual Question Answering (VQA)
on AutoHallusion
AGL-NET: Aerial-Ground Cross-Modal Global Localization with Varying Scales
We present AGL-NET, a novel learning-based method for global localization using LiDAR point clouds and satellite maps.
On the Vulnerability of LLM/VLM-Controlled Robotics
In this work, we highlight vulnerabilities in robotic systems integrating large language models (LLMs) and vision-language models (VLMs) due to input modality sensitivities.
HallusionBench: An Advanced Diagnostic Suite for Entangled Language Hallucination and Visual Illusion in Large Vision-Language Models
Our comprehensive case studies within HallusionBench shed light on the challenges of hallucination and illusion in LVLMs.
Ranked #1 on
Visual Question Answering (VQA)
on HallusionBench
iPLAN: Intent-Aware Planning in Heterogeneous Traffic via Distributed Multi-Agent Reinforcement Learning
Our approach for intent-aware planning, iPLAN, allows agents to infer nearby drivers' intents solely from their local observations.
FireCommander: An Interactive, Probabilistic Multi-agent Environment for Heterogeneous Robot Teams
The FireCommander environment can be useful for research topics spanning a wide range of applications from Reinforcement Learning (RL) and Learning from Demonstration (LfD), to Coordination, Psychology, Human-Robot Interaction (HRI) and Teaming.
