33 citations · 80 across the 14 of their papers we have counts for
15 papers
Artificial Foveated Perception for Mitigating Shortcut Learning in Robotic Foundation Models
Xiatao Sun, Yuan Zhuang, Mateo Sanchez Lopez Negrete +9
Robotic foundation models still need task-specific fine-tuning before deployment, and the fine-tuned policies often break under modest changes in scene layout, lighting, or nearby…
Dr.Occ: Depth- and Region-Guided 3D Occupancy from Surround-View Cameras for Autonomous Driving
Xubo Zhu, Haoyang Zhang, Fei He +4
3D semantic occupancy prediction is crucial for autonomous driving perception, offering comprehensive geometric scene understanding and semantic recognition. However, existing meth…
AlignDrive: Aligned Lateral-Longitudinal Planning for End-to-End Autonomous Driving
Yanhao Wu, Haoyang Zhang, Fei He +6
Practical autonomous driving requires models that generalize by reasoning through spatial-temporal possibilities to exclude unsafe outcomes. While state-of-the-art (SOTA) methods u…
Generating Multimodal Driving Scenes via Next-Scene Prediction
Yanhao Wu, Haoyang Zhang, Tianwei Lin +6
Generative models in Autonomous Driving (AD) enable diverse scene creation, yet existing methods fall short by only capturing a limited range of modalities, restricting the capabil…
Symphonize 3D Semantic Scene Completion with Contextual Instance Queries
Haoyi Jiang, Tianheng Cheng, Naiyu Gao +4
`3D Semantic Scene Completion (SSC) has emerged as a nascent and pivotal undertaking in autonomous driving, aiming to predict voxel occupancy within volumetric scenes. However, pre…
InsPro: Propagating Instance Query and Proposal for Online Video Instance Segmentation
Fei He, Haoyang Zhang, Naiyu Gao +4
Video instance segmentation (VIS) aims at segmenting and tracking objects in videos. Prior methods typically generate frame-level or clip-level object instances first and then asso…
MovieCLIP: Visual Scene Recognition in Movies
Digbalay Bose, Rajat Hebbar, Krishna Somandepalli +5
Longform media such as movies have complex narrative structures, with events spanning a rich variety of ambient visual scenes. Domain specific challenges associated with visual sce…
PanopticDepth: A Unified Framework for Depth-aware Panoptic Segmentation
Naiyu Gao, Fei He, Jian Jia +4
This paper presents a unified framework for depth-aware panoptic segmentation (DPS), which aims to reconstruct 3D scene with instance-level semantics from one single image. Prior w…
Evaluating the Impact of Semantic Segmentation and Pose Estimation on Dense Semantic SLAM
Suman Raj Bista, David Hall, Ben Talbot +3
Recent Semantic SLAM methods combine classical geometry-based estimation with deep learning-based object detection or semantic segmentation. In this paper we evaluate the quality o…
SWA Object Detection
Haoyang Zhang, Ying Wang, Feras Dayoub +1
Do you want to improve 1.0 AP for your object detector without any inference cost and any change to your detector? Let us tell you such a recipe. It is surprisingly simple: train y…
The Robotic Vision Scene Understanding Challenge
David Hall, Ben Talbot, Suman Raj Bista +4
Being able to explore an environment and understand the location and type of all objects therein is important for indoor robotic platforms that must interact closely with humans. H…
BenchBot: Evaluating Robotics Research in Photorealistic 3D Simulation and on Real Robots
Ben Talbot, David Hall, Haoyang Zhang +4
We introduce BenchBot, a novel software suite for benchmarking the performance of robotics research across both photorealistic 3D simulations and real robot platforms. BenchBot pro…
VarifocalNet: An IoU-aware Dense Object Detector
Haoyang Zhang, Ying Wang, Feras Dayoub +1
Accurately ranking the vast number of candidate detections is crucial for dense object detectors to achieve high performance. Prior work uses the classification score or a combinat…
The Probabilistic Object Detection Challenge
John Skinner, David Hall, Haoyang Zhang +2
We introduce a new challenge for computer and robotic vision, the first ACRV Robotic Vision Challenge, Probabilistic Object Detection. Probabilistic object detection is a new varia…
Probabilistic Object Detection: Definition and Evaluation
David Hall, Feras Dayoub, John Skinner +6
We introduce Probabilistic Object Detection, the task of detecting objects in images and accurately quantifying the spatial and semantic uncertainties of the detections. Given the…