Search Results for author: Xiaomeng Dong

Found 6 papers, 2 papers with code

3DAxiesPrompts: Unleashing the 3D Spatial Task Capabilities of GPT-4V

no code implementations15 Dec 2023 Dingning Liu, Xiaomeng Dong, Renrui Zhang, Xu Luo, Peng Gao, Xiaoshui Huang, Yongshun Gong, Zhihui Wang

In this work, we present a new visual prompting method called 3DAxiesPrompts (3DAP) to unleash the capabilities of GPT-4V in performing 3D spatial tasks.

3D Object Detection object-detection +1

Adversarial Focal Loss: Asking Your Discriminator for Hard Examples

no code implementations15 Jul 2022 Chen Liu, Xiaomeng Dong, Michael Potter, Hsi-Ming Chang, Ravi Soni

In this paper, we propose a novel adaptation of Focal Loss for keypoint detection tasks, called Adversarial Focal Loss (AFL).

Keypoint Detection

Optimizing Data Augmentation Policy Through Random Unidimensional Search

1 code implementation16 Jun 2021 Xiaomeng Dong, Michael Potter, Gaurav Kumar, Yun-chan Tsai, V. Ratna Saripalli, Theodore Trafalis

It is no secret amongst deep learning researchers that finding the optimal data augmentation strategy during training can mean the difference between state-of-the-art performance and a run-of-the-mill result.

Data Augmentation

Impact of Inference Accelerators on hardware selection

no code implementations7 Oct 2019 Dibyajyoti Pati, Caroline Favart, Purujit Bahl, Vivek Soni, Yun-chan Tsai, Michael Potter, Jiahui Guan, Xiaomeng Dong, V. Ratna Saripalli

As opportunities for AI-assisted healthcare grow steadily, model deployment faces challenges due to the specific characteristics of the industry.

FastEstimator: A Deep Learning Library for Fast Prototyping and Productization

no code implementations7 Oct 2019 Xiaomeng Dong, Jun-Pyo Hong, Hsi-Ming Chang, Michael Potter, Aritra Chowdhury, Purujit Bahl, Vivek Soni, Yun-chan Tsai, Rajesh Tamada, Gaurav Kumar, Caroline Favart, V. Ratna Saripalli, Gopal Avinash

As the complexity of state-of-the-art deep learning models increases by the month, implementation, interpretation, and traceability become ever-more-burdensome challenges for AI practitioners around the world.

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