Learning Deep Representation with Large-Scale Attributes

By Wanli Ouyang, Hongyang Li, Xingyu Zeng, Xiaogang Wang. 2015

23 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Representation & Self-Supervised Learning

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Xiaogang Wang
2. Hongsheng Li
3. Wanli Ouyang
4. Luc Van Gool
5. Ming-Hsuan Yang
6. Dacheng Tao
7. Yi Yang
8. Tao Qin

In Representation & Self-Supervised Learning

1. Wanli Ouyang
2. Tao Qin
3. Jianfeng Lu
4. Nenghai Yu
5. Xiaokang Yang
6. Tie-Yan Liu
7. Xiaogang Wang
8. Hongsheng Li

Citations in the corpus

• MASS: Masked Sequence to Sequence Pre-training for Language Generation (2019, methodology): “Correspondence to: Tao Qin < tao-qin@microsoft.com > . while pre-training has plenty of data (Girshick et al., 2014; Szegedy et al., 2015; Ouyang et al., 2015; Dai & Le, 2015; Howard & Ruder, 2018; Radford et al., 2018; Devlin et al., 2018).”
• DeepID-Net: Object Detection with Deformable Part Based Convolutional Neural Networks (2017, unknown): “Because of the power on learning feature representation, deep models have been widely used for object recognition, detection and other vision tasks [1], [6], [7], [19], [20], [21], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47],”
• Factors in Finetuning Deep Model for Object Detection with Long-Tail Distribution (2016, methodology): “Finetuning from the deep model pretrained on the large-scale ImageNet dataset is found to yield state-of-the-art performance for many vision tasks such as tracking [38], segmentation [12], object detection [35, 25, 22], action recognition [16], and human pose estimation [5].”
• Online Multi-object Tracking Using CNN-Based Single Object Tracker with Spatial-Temporal Attention Mechanism (2017, background): “Since deep convolutional neural networks (CNNs) are shown to be effective in many computer vision applications [27, 15, 36, 35, 57, 54], many works [47, 19, 31, 48] have explored the usage of CNNs to learn strong discriminative appearance model in single object tracking and demonstrated state-of-the”