no code implementations • 18 Aug 2021 • Shaunak Mishra, Changwei Hu, Manisha Verma, Kevin Yen, Yifan Hu, Maxim Sviridenko
To realize this opportunity, we propose an ad text strength indicator (TSI) which: (i) predicts the click-through-rate (CTR) for an input ad text, (ii) fetches similar existing ads to create a neighborhood around the input ad, (iii) and compares the predicted CTRs in the neighborhood to declare whether the input ad is strong or weak.
no code implementations • 27 Jan 2021 • Manisha Verma, Kapil Thadani, Shaunak Mishra
In this work, we demonstrate the effectiveness of different attention based neural models that can directly exploit side information available in technical documents or verified forums (e. g., research publications on COVID-19 or WHO website).
1 code implementation • 25 Nov 2020 • Bowen Wang, Liangzhi Li, Manisha Verma, Yuta Nakashima, Ryo Kawasaki, Hajime Nagahara
Few-shot learning (FSL) approaches are usually based on an assumption that the pre-trained knowledge can be obtained from base (seen) categories and can be well transferred to novel (unseen) categories.
Ranked #34 on Few-Shot Image Classification on CIFAR-FS 5-way (5-shot)
1 code implementation • 7 Nov 2020 • Liangzhi Li, Manisha Verma, Bowen Wang, Yuta Nakashima, Hajime Nagahara, Ryo Kawasaki
Our severity grading method was able to validate crossing points with precision and recall of 96. 3% and 96. 3%, respectively.
1 code implementation • ICCV 2021 • Liangzhi Li, Bowen Wang, Manisha Verma, Yuta Nakashima, Ryo Kawasaki, Hajime Nagahara
Explainable artificial intelligence has been gaining attention in the past few years.
no code implementations • 17 Aug 2020 • Shaunak Mishra, Manisha Verma, Yichao Zhou, Kapil Thadani, Wei Wang
Since major ad platforms typically run A/B tests for multiple advertisers in parallel, we explore the possibility of collaboratively learning ad creative refinement via A/B tests of multiple advertisers.
no code implementations • 5 Aug 2020 • Nisarg Raval, Manisha Verma
In this work, we present a systematic approach of leveraging adversarial examples to measure the robustness of popular ranking models.
no code implementations • 22 Jul 2020 • Sudhakar Kumawat, Manisha Verma, Yuta Nakashima, Shanmuganathan Raman
To address these issues, we propose spatio-temporal short term Fourier transform (STFT) blocks, a new class of convolutional blocks that can serve as an alternative to the 3D convolutional layer and its variants in 3D CNNs.
1 code implementation • MIDL 2019 • Liangzhi Li, Manisha Verma, Yuta Nakashima, Ryo Kawasaki, Hajime Nagahara
Retinal imaging serves as a valuable tool for diagnosis of various diseases.
1 code implementation • 22 Apr 2020 • Manisha Verma, Sudhakar Kumawat, Yuta Nakashima, Shanmuganathan Raman
To handle more variety in human poses, we propose the concept of fine-grained hierarchical pose classification, in which we formulate the pose estimation as a classification task, and propose a dataset, Yoga-82, for large-scale yoga pose recognition with 82 classes.
1 code implementation • 20 Jan 2020 • Yichao Zhou, Shaunak Mishra, Manisha Verma, Narayan Bhamidipati, Wei Wang
There is a perennial need in the online advertising industry to refresh ad creatives, i. e., images and text used for enticing online users towards a brand.
no code implementations • 18 Dec 2019 • Marc-Andre Schulz, Matt Chapman-Rounds, Manisha Verma, Danilo Bzdok, Konstantinos Georgatzis
The distribution of instances in the explanation space of our diagnostic classifier amplifies the different reasons for belonging to the same class - resulting in a representation that is uniquely useful for discovering latent subtypes.
2 code implementations • 12 Dec 2019 • Liangzhi Li, Manisha Verma, Yuta Nakashima, Hajime Nagahara, Ryo Kawasaki
Retinal vessel segmentation is of great interest for diagnosis of retinal vascular diseases.
Ranked #5 on Retinal Vessel Segmentation on CHASE_DB1
no code implementations • 16 Apr 2019 • Sudhakar Kumawat, Manisha Verma, Shanmuganathan Raman
Recognizing facial expressions is one of the central problems in computer vision.
Facial Expression Recognition Facial Expression Recognition (FER)