Search Results for author:
Found 18 papers, 9 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.
Immune: Improving Safety Against Jailbreaks in Multi-modal LLMs via Inference-Time Alignment
With the widespread deployment of Multimodal Large Language Models (MLLMs) for visual-reasoning tasks, improving their safety has become crucial.
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
LOC-ZSON: Language-driven Object-Centric Zero-Shot Object Retrieval and Navigation
In this paper, we present LOC-ZSON, a novel Language-driven Object-Centric image representation for object navigation task within complex scenes.
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.
Large Language Models and Causal Inference in Collaboration: A Comprehensive Survey
Causal inference has shown potential in enhancing the predictive accuracy, fairness, robustness, and explainability of Natural Language Processing (NLP) models by capturing causal relationships among variables.
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
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.
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
CrossLoc3D: Aerial-Ground Cross-Source 3D Place Recognition
We present CrossLoc3D, a novel 3D place recognition method that solves a large-scale point matching problem in a cross-source setting.
Ranked #1 on
3D Place Recognition
on CS-Campus3D
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
VINet: Visual and Inertial-based Terrain Classification and Adaptive Navigation over Unknown Terrain
We present a visual and inertial-based terrain classification network (VINet) for robotic navigation over different traversable surfaces.
FAR: Fourier Aerial Video Recognition
Our formulation uses a novel Fourier object disentanglement method to innately separate out the human agent (which is typically small) from the background.
Ranked #1 on
Action Recognition
on UAV Human
M3DeTR: Multi-representation, Multi-scale, Mutual-relation 3D Object Detection with Transformers
We present a novel architecture for 3D object detection, M3DeTR, which combines different point cloud representations (raw, voxels, bird-eye view) with different feature scales based on multi-scale feature pyramids.
Ranked #1 on
3D Object Detection
on KITTI Cyclist Moderate val
GANav: Efficient Terrain Segmentation for Robot Navigation in Unstructured Outdoor Environments
We interface GANav with a deep reinforcement learning-based navigation algorithm and highlight its benefits in terms of navigation in real-world unstructured terrains.
Ranked #1 on
Semantic Segmentation
on RUGD
Forecasting Trajectory and Behavior of Road-Agents Using Spectral Clustering in Graph-LSTMs
In practice, our approach reduces the average prediction error by more than 54% over prior algorithms and achieves a weighted average accuracy of 91. 2% for behavior prediction.
Ranked #1 on
Trajectory Prediction
on ApolloScape
Robotics
