Search Results for author: Yizhao Gao

Found 11 papers, 4 papers with code

COTS: Collaborative Two-Stream Vision-Language Pre-Training Model for Cross-Modal Retrieval

no code implementations CVPR 2022 Haoyu Lu, Nanyi Fei, Yuqi Huo, Yizhao Gao, Zhiwu Lu, Ji-Rong Wen

Under a fair comparison setting, our COTS achieves the highest performance among all two-stream methods and comparable performance (but with 10, 800X faster in inference) w. r. t.

Contrastive Learning Cross-Modal Retrieval +5

Towards artificial general intelligence via a multimodal foundation model

1 code implementation27 Oct 2021 Nanyi Fei, Zhiwu Lu, Yizhao Gao, Guoxing Yang, Yuqi Huo, Jingyuan Wen, Haoyu Lu, Ruihua Song, Xin Gao, Tao Xiang, Hao Sun, Ji-Rong Wen

To overcome this limitation and take a solid step towards artificial general intelligence (AGI), we develop a foundation model pre-trained with huge multimodal data, which can be quickly adapted for various downstream cognitive tasks.

Image Classification Reading Comprehension +2

Contrastive Prototype Learning with Augmented Embeddings for Few-Shot Learning

no code implementations23 Jan 2021 Yizhao Gao, Nanyi Fei, Guangzhen Liu, Zhiwu Lu, Tao Xiang, Songfang Huang

First, data augmentations are introduced to both the support and query sets with each sample now being represented as an augmented embedding (AE) composed of concatenated embeddings of both the original and augmented versions.

Few-Shot Learning

Z-Score Normalization, Hubness, and Few-Shot Learning

no code implementations ICCV 2021 Nanyi Fei, Yizhao Gao, Zhiwu Lu, Tao Xiang

This means that these methods are prone to the hubness problem, that is, a certain class prototype becomes the nearest neighbor of many test instances regardless which classes they belong to.

Few-Shot Learning

Algorithm-hardware Co-design for Deformable Convolution

2 code implementations19 Feb 2020 Qijing Huang, Dequan Wang, Yizhao Gao, Yaohui Cai, Zhen Dong, Bichen Wu, Kurt Keutzer, John Wawrzynek

In this work, we first investigate the overhead of the deformable convolution on embedded FPGA SoCs, and then show the accuracy-latency tradeoffs for a set of algorithm modifications including full versus depthwise, fixed-shape, and limited-range.

Image Classification Instance Segmentation +4

Meta-Learning across Meta-Tasks for Few-Shot Learning

no code implementations11 Feb 2020 Nanyi Fei, Zhiwu Lu, Yizhao Gao, Jia Tian, Tao Xiang, Ji-Rong Wen

In this paper, we argue that the inter-meta-task relationships should be exploited and those tasks are sampled strategically to assist in meta-learning.

Domain Adaptation Few-Shot Learning +1

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