Search Results for author:
Found 10 papers, 6 papers with code
DAVE: Diverse Atomic Visual Elements Dataset with High Representation of Vulnerable Road Users in Complex and Unpredictable Environments
Addressing this gap, we present a new dataset, DAVE, designed for evaluating perception methods with high representation of Vulnerable Road Users (VRUs: e. g. pedestrians, animals, motorbikes, and bicycles) in complex and unpredictable environments.
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.
Highlighting the Safety Concerns of Deploying LLMs/VLMs in Robotics
In this paper, we highlight the critical issues of robustness and safety associated with integrating large language models (LLMs) and vision-language models (VLMs) into robotics applications.
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
SCP: Soft Conditional Prompt Learning for Aerial Video Action Recognition
In practice, we observe a 3. 17-10. 2% accuracy improvement on the aerial video datasets (Okutama, NECDrone), which consist of scenes with single-agent and multi-agent actions.
Ranked #1 on
Action Recognition
on Okutama-Action
PMI Sampler: Patch Similarity Guided Frame Selection for Aerial Action Recognition
Our algorithm utilizes the motion bias within aerial videos, which enables the selection of motion-salient frames.
Ranked #1 on
Action Recognition
on UAV-Human
MITFAS: Mutual Information based Temporal Feature Alignment and Sampling for Aerial Video Action Recognition
We present a novel approach for action recognition in UAV videos.
Ranked #2 on
Action Recognition
on UAV-Human
AZTR: Aerial Video Action Recognition with Auto Zoom and Temporal Reasoning
We propose a novel approach for aerial video action recognition.
Ranked #1 on
Action Recognition
on RoCoG-v2
