most citedA General-Purpose Transferable Predictor for Neural Architecture Search

4 citations · 12 across the 9 of their papers we have counts for

collaborators

7 papers

cs.CV2023

TCR: Short Video Title Generation and Cover Selection with Attention Refinement

Yakun Yu, Jiuding Yang, Weidong Guo +3

With the widespread popularity of user-generated short videos, it becomes increasingly challenging for content creators to promote their content to potential viewers. Automatically…

cs.CL20232 cited

CEIL: A General Classification-Enhanced Iterative Learning Framework for Text Clustering

Mingjun Zhao, Mengzhen Wang, Yinglong Ma +2

Text clustering, as one of the most fundamental challenges in unsupervised learning, aims at grouping semantically similar text segments without relying on human annotations. With…

cs.CV20232 cited

Search-Map-Search: A Frame Selection Paradigm for Action Recognition

Mingjun Zhao, Yakun Yu, Xiaoli Wang +2

Despite the success of deep learning in video understanding tasks, processing every frame in a video is computationally expensive and often unnecessary in real-time applications. F…

cs.CV20231 cited

LA3: Efficient Label-Aware AutoAugment

Mingjun Zhao, Shan Lu, Zixuan Wang +2

Automated augmentation is an emerging and effective technique to search for data augmentation policies to improve generalizability of deep neural network training. Most existing wo…

cs.LG2023

Reparameterization through Spatial Gradient Scaling

Alexander Detkov, Mohammad Salameh, Muhammad Fetrat Qharabagh +4

Reparameterization aims to improve the generalization of deep neural networks by transforming convolutional layers into equivalent multi-branched structures during training. Howeve…

cs.CV20233 cited

GlyphDraw: Seamlessly Rendering Text with Intricate Spatial Structures in Text-to-Image Generation

Jian Ma, Mingjun Zhao, Chen Chen +4

Recent breakthroughs in the field of language-guided image generation have yielded impressive achievements, enabling the creation of high-quality and diverse images based on user i…