Search Results for author:
Found 10 papers, 6 papers with code
HallusionBench: An Advanced Diagnostic Suite for Entangled Language Hallucination & 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.
PLAR: Prompt Learning for Action Recognition
We evaluate our approach on datasets consisting of both ground camera videos and aerial videos, and 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 Oxford RobotCar Dataset
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 Cars Hard 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
