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Found 12 papers, 3 papers with code
SNAP: Self-Supervised Neural Maps for Visual Positioning and Semantic Understanding
Semantic 2D maps are commonly used by humans and machines for navigation purposes, whether it's walking or driving.
TUSK: Task-Agnostic Unsupervised Keypoints
Existing unsupervised methods for keypoint learning rely heavily on the assumption that a specific keypoint type (e. g. elbow, digit, abstract geometric shape) appears only once in an image.
Efficient Large Scale Inlier Voting for Geometric Vision Problems
Outlier rejection and equivalently inlier set optimization is a key ingredient in numerous applications in computer vision such as filtering point-matches in camera pose estimation or plane and normal estimation in point clouds.
COTR: Correspondence Transformer for Matching Across Images
We propose a novel framework for finding correspondences in images based on a deep neural network that, given two images and a query point in one of them, finds its correspondence in the other.
Ranked #1 on
Dense Pixel Correspondence Estimation
on KITTI 2012
Dense Pixel Correspondence Estimation
Optical Flow Estimation
Learning non-maximum suppression
Object detectors have hugely profited from moving towards an end-to-end learning paradigm: proposals, features, and the classifier becoming one neural network improved results two-fold on general object detection.
Simple Does It: Weakly Supervised Instance and Semantic Segmentation
Semantic labelling and instance segmentation are two tasks that require particularly costly annotations.
Ranked #1 on
Semantic Segmentation
on PASCAL VOC 2012 val
(Mean IoU metric)
How Far are We from Solving Pedestrian Detection?
We enable our analysis by creating a human baseline for pedestrian detection (over the Caltech dataset), and by manually clustering the recurrent errors of a top detector.
A convnet for non-maximum suppression
Non-maximum suppression (NMS) is used in virtually all state-of-the-art object detection pipelines.
What makes for effective detection proposals?
Current top performing object detectors employ detection proposals to guide the search for objects, thereby avoiding exhaustive sliding window search across images.
Taking a Deeper Look at Pedestrians
In this paper we study the use of convolutional neural networks (convnets) for the task of pedestrian detection.
Ranked #31 on
Pedestrian Detection
on Caltech
Ten Years of Pedestrian Detection, What Have We Learned?
Paper-by-paper results make it easy to miss the forest for the trees. We analyse the remarkable progress of the last decade by discussing the main ideas explored in the 40+ detectors currently present in the Caltech pedestrian detection benchmark.
How good are detection proposals, really?
Current top performing Pascal VOC object detectors employ detection proposals to guide the search for objects thereby avoiding exhaustive sliding window search across images.
