Natural language understanding (NLU) is integral to various social media applications.
Interactive image segmentation aims at segmenting a target region through a way of human-computer interaction.
In recent years, the rapid development of deep learning has brought great advancements to image and video segmentation methods based on neural networks.
For text recognizer, the base model is replaced from CRNN to SVTR, and we introduce lightweight text recognition network SVTR LCNet, guided training of CTC by attention, data augmentation strategy TextConAug, better pre-trained model by self-supervised TextRotNet, UDML, and UIM to accelerate the model and improve the effect.
3 code implementations • 6 Apr 2022 • Juncai Peng, Yi Liu, Shiyu Tang, Yuying Hao, Lutao Chu, Guowei Chen, Zewu Wu, Zeyu Chen, Zhiliang Yu, Yuning Du, Qingqing Dang, Baohua Lai, Qiwen Liu, Xiaoguang Hu, dianhai yu, Yanjun Ma
Real-world applications have high demands for semantic segmentation methods.
Ranked #4 on Real-Time Semantic Segmentation on Cityscapes val
In this report, we present PP-YOLOE, an industrial state-of-the-art object detector with high performance and friendly deployment.
Ranked #1 on Object Detection on BDD100K val
This work is the first to construct a large-scale video portrait dataset that contains 291 videos from 23 conference scenes with 14K fine-labeled frames and extensions to multi-camera teleconferencing.
Different from the Single Image Super-Resolution(SISR) task, the key for Video Super-Resolution(VSR) task is to make full use of complementary information across frames to reconstruct the high-resolution sequence.
4 code implementations • 1 Nov 2021 • Guanghua Yu, Qinyao Chang, Wenyu Lv, Chang Xu, Cheng Cui, Wei Ji, Qingqing Dang, Kaipeng Deng, Guanzhong Wang, Yuning Du, Baohua Lai, Qiwen Liu, Xiaoguang Hu, dianhai yu, Yanjun Ma
We investigate the applicability of the anchor-free strategy on lightweight object detection models.
Ranked #1 on Object Detection on MSCOCO
In addition, with the proposed method, we develop an efficient interactive segmentation tool for practical data annotation tasks.
Ranked #2 on Interactive Segmentation on PASCAL VOC (NoC@85 metric)
The toolkit aims to help both developers and researchers in the whole process of designing segmentation models, training models, optimizing performance and inference speed, and deploying models.