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Found 14 papers, 7 papers with code
TSI: an Ad Text Strength Indicator using Text-to-CTR and Semantic-Ad-Similarity
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.
Powering COVID-19 community Q&A with Curated Side Information
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).
Match Them Up: Visually Explainable Few-shot Image Classification
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)
Automated Grading System of Retinal Arterio-venous Crossing Patterns: A Deep Learning Approach Replicating Ophthalmologist's Diagnostic Process of Arteriolosclerosis
Our severity grading method was able to validate crossing points with precision and recall of 96. 3% and 96. 3%, respectively.
SCOUTER: Slot Attention-based Classifier for Explainable Image Recognition
Explainable artificial intelligence has been gaining attention in the past few years.
Learning to Create Better Ads: Generation and Ranking Approaches for Ad Creative Refinement
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.
One word at a time: adversarial attacks on retrieval models
In this work, we present a systematic approach of leveraging adversarial examples to measure the robustness of popular ranking models.
Depthwise Spatio-Temporal STFT Convolutional Neural Networks for Human Action Recognition
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.
Joint Learning of Vessel Segmentation and Artery/Vein Classification with Post-processing
Retinal imaging serves as a valuable tool for diagnosis of various diseases.
Yoga-82: A New Dataset for Fine-grained Classification of Human Poses
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.
Recommending Themes for Ad Creative Design via Visual-Linguistic Representations
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.
Clusters in Explanation Space: Inferring disease subtypes from model explanations
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.
IterNet: Retinal Image Segmentation Utilizing Structural Redundancy in Vessel Networks
Retinal vessel segmentation is of great interest for diagnosis of retinal vascular diseases.
Ranked #5 on
Retinal Vessel Segmentation
on CHASE_DB1
LBVCNN: Local Binary Volume Convolutional Neural Network for Facial Expression Recognition from Image Sequences
Recognizing facial expressions is one of the central problems in computer vision.
Facial Expression Recognition
Facial Expression Recognition (FER)
