33 citations · 80 across the 14 of their papers we have counts for
9 papers · 1 filter
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…
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…
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…
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…
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…