Search Results for author:
Found 11 papers, 6 papers with code
HiLo: Exploiting High Low Frequency Relations for Unbiased Panoptic Scene Graph Generation
Existing unbiased methods tackle the long-tail problem by data/loss rebalancing to favor low-frequency relations.
SliceMatch: Geometry-guided Aggregation for Cross-View Pose Estimation
We propose SliceMatch, which consists of ground and aerial feature extractors, feature aggregators, and a pose predictor.
Panoptic nuScenes: A Large-Scale Benchmark for LiDAR Panoptic Segmentation and Tracking
Panoptic scene understanding and tracking of dynamic agents are essential for robots and automated vehicles to navigate in urban environments.
Ranked #1 on
Panoptic Tracking
on Panoptic nuScenes test
NuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles
In this work, we propose the world's first closed-loop ML-based planning benchmark for autonomous driving.
The efficacy of Neural Planning Metrics: A meta-analysis of PKL on nuScenes
A high-performing object detection system plays a crucial role in autonomous driving (AD).
nuScenes: A multimodal dataset for autonomous driving
Most autonomous vehicles, however, carry a combination of cameras and range sensors such as lidar and radar.
Ranked #303 on
3D Object Detection
on nuScenes
(using extra training data)
PointPillars: Fast Encoders for Object Detection from Point Clouds
These benchmarks suggest that PointPillars is an appropriate encoding for object detection in point clouds.
Weakly Supervised Object Localization Using Things and Stuff Transfer
We propose to help weakly supervised object localization for classes where location annotations are not available, by transferring things and stuff knowledge from a source set with available annotations.
Multiple Instance Learning
Weakly Supervised Object Localization
+1
COCO-Stuff: Thing and Stuff Classes in Context
To understand stuff and things in context we introduce COCO-Stuff, which augments all 164K images of the COCO 2017 dataset with pixel-wise annotations for 91 stuff classes.
Ranked #1 on
Semantic Segmentation
on COCO-Stuff
Region-based semantic segmentation with end-to-end training
We propose a novel method for semantic segmentation, the task of labeling each pixel in an image with a semantic class.
Ranked #1 on
Semantic Segmentation
on SIFT-flow
Joint Calibration for Semantic Segmentation
Semantic segmentation is the task of assigning a class-label to each pixel in an image.
Ranked #2 on
Semantic Segmentation
on SIFT-flow
