Search Results for author: Yiming Ma

Found 8 papers, 4 papers with code

CLIP-EBC: CLIP Can Count Accurately through Enhanced Blockwise Classification

1 code implementation14 Mar 2024 Yiming Ma, Victor Sanchez, Tanaya Guha

The CLIP (Contrastive Language-Image Pretraining) model has exhibited outstanding performance in recognition problems, such as zero-shot image classification and object detection.

Crowd Counting

Inference via robust optimal transportation: theory and methods

no code implementations16 Jan 2023 Yiming Ma, Hang Liu, Davide La Vecchia, Metthieu Lerasle

Fourth, we use $W^{(\lambda)}$ to define minimum distance estimators, we provide their statistical guarantees and we illustrate how to apply the derived concentration inequalities for a data driven selection of $\lambda$.

Domain Adaptation

Inception-Based Crowd Counting -- Being Fast while Remaining Accurate

3 code implementations18 Oct 2022 Yiming Ma

Recent sophisticated CNN-based algorithms have demonstrated their extraordinary ability to automate counting crowds from images, thanks to their structures which are designed to address the issue of various head scales.

Crowd Counting

FusionCount: Efficient Crowd Counting via Multiscale Feature Fusion

1 code implementation28 Feb 2022 Yiming Ma, Victor Sanchez, Tanaya Guha

Then, to account for perspective distortion, the highest-level feature map is fed to extra components to extract multiscale features, which are the input to the decoder to generate crowd densities.

Crowd Counting

Realization of epitaxial thin films of the superconductor K-doped BaFe$_\text{2}$As$_\text{2}$

no code implementations26 Dec 2020 Dongyi Qin, Kazumasa Iida, Takafumi Hatano, Hikaru Saito, Yiming Ma, Chao Wang, Satoshi Hata, Michio Naito, Akiyasu Yamamoto

The iron-based superconductor Ba$_{1-x}$K$_x$Fe$_\text{2}$As$_\text{2}$ is emerging as a key material for high magnetic field applications owing to the recent developments in superconducting wires and bulk permanent magnets.


Entity Personalized Talent Search Models with Tree Interaction Features

no code implementations25 Feb 2019 Cagri Ozcaglar, Sahin Geyik, Brian Schmitz, Prakhar Sharma, Alex Shelkovnykov, Yiming Ma, Erik Buchanan

Talent Search systems aim to recommend potential candidates who are a good match to the hiring needs of a recruiter expressed in terms of the recruiter's search query or job posting.


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