185 citations · 326 across the 41 of their papers we have counts for
8 papers · 2 filters
Object-Centric Unsupervised Image Captioning
Zihang Meng, David Yang, Xuefei Cao +2
Image captioning is a longstanding problem in the field of computer vision and natural language processing. To date, researchers have produced impressive state-of-the-art performan…
Unsupervised Domain Adaptation: A Reality Check
Kevin Musgrave, Serge Belongie, Ser-Nam Lim
Interest in unsupervised domain adaptation (UDA) has surged in recent years, resulting in a plethora of new algorithms. However, as is often the case in fast-moving fields, baselin…
AdaViT: Adaptive Vision Transformers for Efficient Image Recognition
Lingchen Meng, Hengduo Li, Bor-Chun Chen +4
Built on top of self-attention mechanisms, vision transformers have demonstrated remarkable performance on a variety of vision tasks recently. While achieving excellent performance…
Differential Motion Evolution for Fine-Grained Motion Deformation in Unsupervised Image Animation
Peirong Liu, Rui Wang, Xuefei Cao +3
Image animation is the task of transferring the motion of a driving video to a given object in a source image. While great progress has recently been made in unsupervised motion tr…
Cross-Modal Retrieval Augmentation for Multi-Modal Classification
Shir Gur, Natalia Neverova, Chris Stauffer +3
Recent advances in using retrieval components over external knowledge sources have shown impressive results for a variety of downstream tasks in natural language processing. Here,…
Multimodal Fusion Refiner Networks
Sethuraman Sankaran, David Yang, Ser-Nam Lim
Tasks that rely on multi-modal information typically include a fusion module that combines information from different modalities. In this work, we develop a Refiner Fusion Network…
Exploring Visual Engagement Signals for Representation Learning
Menglin Jia, Zuxuan Wu, Austin Reiter +3
Visual engagement in social media platforms comprises interactions with photo posts including comments, shares, and likes. In this paper, we leverage such visual engagement clues a…
THAT: Two Head Adversarial Training for Improving Robustness at Scale
Zuxuan Wu, Tom Goldstein, Larry S. Davis +1
Many variants of adversarial training have been proposed, with most research focusing on problems with relatively few classes. In this paper, we propose Two Head Adversarial Traini…