Search Results for author:
Found 11 papers, 7 papers with code
Densely Constrained Depth Estimator for Monocular 3D Object Detection
So these methods only use a small number of projection constraints and produce insufficient depth candidates, leading to inaccurate depth estimation.
GIFS: Neural Implicit Function for General Shape Representation
This limitation leads to tedious data processing (converting non-watertight raw data to watertight) as well as the incapability of representing general object shapes in the real world.
Immortal Tracker: Tracklet Never Dies
We employ a simple Kalman filter for trajectory prediction and preserve the tracklet by prediction when the target is not visible.
Online Adaptation for Implicit Object Tracking and Shape Reconstruction in the Wild
Tracking and reconstructing 3D objects from cluttered scenes are the key components for computer vision, robotics and autonomous driving systems.
Unsupervised Object Detection with LiDAR Clues
We further identify another major issue, seldom noticed by the community, that the long-tailed and open-ended (sub-)category distribution should be accommodated.
Revisiting Feature Alignment for One-stage Object Detection
Recently, one-stage object detectors gain much attention due to their simplicity in practice.
Sequence Level Semantics Aggregation for Video Object Detection
In this work, we argue that aggregating features in the full-sequence level will lead to more discriminative and robust features for video object detection.
Ranked #3 on
Video Object Detection
on ImageNet VID
SimpleDet: A Simple and Versatile Distributed Framework for Object Detection and Instance Recognition
A Simple and Versatile Framework for Object Detection and Instance Recognition
Scale-Aware Trident Networks for Object Detection
In this work, we first present a controlled experiment to investigate the effect of receptive fields for scale variation in object detection.
Ranked #79 on
Object Detection
on COCO test-dev
(using extra training data)
Spectral Feature Transformation for Person Re-identification
With the surge of deep learning techniques, the field of person re-identification has witnessed rapid progress in recent years.
DarkRank: Accelerating Deep Metric Learning via Cross Sample Similarities Transfer
have shown that the dark knowledge within a powerful teacher model can significantly help the training of a smaller and faster student network.
