Search Results for author:
Found 10 papers, 6 papers with code
BSS-Bench: Towards Reproducible and Effective Band Selection Search
The key technology to overcome the drawbacks of hyperspectral imaging (expensive, high capture delay, and low spatial resolution) and make it widely applicable is to select only a few representative bands from hundreds of bands.
A Solution to Co-occurrence Bias: Attributes Disentanglement via Mutual Information Minimization for Pedestrian Attribute Recognition
Recent studies on pedestrian attribute recognition progress with either explicit or implicit modeling of the co-occurrence among attributes.
One-shot neural band selection for spectral recovery
To solve this ill-posed inverse problem, most band selection methods adopt hand-crafted priors or exploit clustering or sparse regularization constraints to find most prominent bands.
Continuous Copy-Paste for One-Stage Multi-Object Tracking and Segmentation
Current one-step multi-object tracking and segmentation (MOTS) methods lag behind recent two-step methods.
Revealing the Reciprocal Relations Between Self-Supervised Stereo and Monocular Depth Estimation
Second, we introduce an occlusion-aware distillation (OA Distillation) module, which leverages the predicted depths from StereoNet in non-occluded regions to train our monocular depth estimation network named SingleNet.
Segment as Points for Efficient Online Multi-Object Tracking and Segmentation
The resulting online MOTS framework, named PointTrack, surpasses all the state-of-the-art methods including 3D tracking methods by large margins (5. 4% higher MOTSA and 18 times faster over MOTSFusion) with the near real-time speed (22 FPS).
Multi-Object Tracking
Multi-Object Tracking and Segmentation
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PointTrack++ for Effective Online Multi-Object Tracking and Segmentation
In this work, we present PointTrack++, an effective on-line framework for MOTS, which remarkably extends our recently proposed PointTrack framework.
Associate-3Ddet: Perceptual-to-Conceptual Association for 3D Point Cloud Object Detection
Object detection from 3D point clouds remains a challenging task, though recent studies pushed the envelope with the deep learning techniques.
ZoomNet: Part-Aware Adaptive Zooming Neural Network for 3D Object Detection
The pipeline of ZoomNet begins with an ordinary 2D object detection model which is used to obtain pairs of left-right bounding boxes.
Towards End-to-End License Plate Detection and Recognition: A Large Dataset and Baseline
Most current license plate (LP) detection and recognition approaches are evaluated on a small and usually unrepresentative dataset since there are no publicly available large diverse datasets.
