63 citations · 71 across the 5 of their papers we have counts for
5 papers · 1 filter
Omni-DETR: Omni-Supervised Object Detection with Transformers
Pei Wang, Zhaowei Cai, Hao Yang +4
We consider the problem of omni-supervised object detection, which can use unlabeled, fully labeled and weakly labeled annotations, such as image tags, counts, points, etc., for ob…
Rethinking the Hyperparameters for Fine-tuning
Hao Li, Pratik Chaudhari, Hao Yang +4
Fine-tuning from pre-trained ImageNet models has become the de-facto standard for various computer vision tasks. Current practices for fine-tuning typically involve selecting an ad…
Detecting 11K Classes: Large Scale Object Detection without Fine-Grained Bounding Boxes
Hao Yang, Hao Wu, Hao Chen
Recent advances in deep learning greatly boost the performance of object detection. State-of-the-art methods such as Faster-RCNN, FPN and R-FCN have achieved high accuracy in chall…
Exploiting Web Images for Weakly Supervised Object Detection
Qingyi Tao, Hao Yang, Jianfei Cai
In recent years, the performance of object detection has advanced significantly with the evolving deep convolutional neural networks. However, the state-of-the-art object detection…
MIML-FCN+: Multi-instance Multi-label Learning via Fully Convolutional Networks with Privileged Information
Hao Yang, Joey Tianyi Zhou, Jianfei Cai +1
Multi-instance multi-label (MIML) learning has many interesting applications in computer visions, including multi-object recognition and automatic image tagging. In these applicati…