Search Results for author: Ze Liu

Found 8 papers, 7 papers with code

Syntactically Diverse Adversarial Network for Knowledge-Grounded Conversation Generation

no code implementations Findings (EMNLP) 2021 Fuwei Cui, Hui Di, Hongjie Ren, Kazushige Ouchi, Ze Liu, Jinan Xu

Generative conversation systems tend to produce meaningless and generic responses, which significantly reduce the user experience.

Swin Transformer V2: Scaling Up Capacity and Resolution

5 code implementations18 Nov 2021 Ze Liu, Han Hu, Yutong Lin, Zhuliang Yao, Zhenda Xie, Yixuan Wei, Jia Ning, Yue Cao, Zheng Zhang, Li Dong, Furu Wei, Baining Guo

Our techniques are generally applicable for scaling up vision models, which has not been widely explored as that of NLP language models, partly due to the following difficulties in training and applications: 1) vision models often face instability issues at scale and 2) many downstream vision tasks require high resolution images or windows and it is not clear how to effectively transfer models pre-trained at low resolutions to higher resolution ones.

 Ranked #1 on Object Detection on COCO test-dev (using extra training data)

Action Classification Image Classification +3

Video Swin Transformer

9 code implementations24 Jun 2021 Ze Liu, Jia Ning, Yue Cao, Yixuan Wei, Zheng Zhang, Stephen Lin, Han Hu

The vision community is witnessing a modeling shift from CNNs to Transformers, where pure Transformer architectures have attained top accuracy on the major video recognition benchmarks.

Ranked #8 on Action Classification on Kinetics-600 (using extra training data)

Action Classification Action Recognition +3

Group-Free 3D Object Detection via Transformers

2 code implementations ICCV 2021 Ze Liu, Zheng Zhang, Yue Cao, Han Hu, Xin Tong

Instead of grouping local points to each object candidate, our method computes the feature of an object from all the points in the point cloud with the help of an attention mechanism in the Transformers \cite{vaswani2017attention}, where the contribution of each point is automatically learned in the network training.

3D Object Detection

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

43 code implementations ICCV 2021 Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo

This paper presents a new vision Transformer, called Swin Transformer, that capably serves as a general-purpose backbone for computer vision.

Ranked #3 on Semantic Segmentation on FoodSeg103 (using extra training data)

Image Classification Instance Segmentation +2

RGB-D Salient Object Detection via 3D Convolutional Neural Networks

1 code implementation25 Jan 2021 Qian Chen, Ze Liu, Yi Zhang, Keren Fu, Qijun Zhao, Hongwei Du

The proposed model, named RD3D, aims at pre-fusion in the encoder stage and in-depth fusion in the decoder stage to effectively promote the full integration of RGB and depth streams.

RGB-D Salient Object Detection Salient Object Detection

EF-Net: A novel enhancement and fusion network for RGB-D saliency detection

1 code implementation4 Nov 2020 Qian Chen, Keren Fu, Ze Liu, Geng Chen, Hongwei Du, Bensheng Qiu, LingShao

Finally, we propose an effective layer-wise aggregation module to fuse the features extracted from the enhanced depth maps and RGB images for the accurate detection of salient objects.

Object Detection Saliency Detection +1

A Closer Look at Local Aggregation Operators in Point Cloud Analysis

1 code implementation ECCV 2020 Ze Liu, Han Hu, Yue Cao, Zheng Zhang, Xin Tong

Our investigation reveals that despite the different designs of these operators, all of these operators make surprisingly similar contributions to the network performance under the same network input and feature numbers and result in the state-of-the-art accuracy on standard benchmarks.

3D Semantic Segmentation

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