185 citations · 326 across the 41 of their papers we have counts for
11 papers · 1 filter
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…
Learning to Ground Multi-Agent Communication with Autoencoders
Toru Lin, Minyoung Huh, Chris Stauffer +2
Communication requires having a common language, a lingua franca, between agents. This language could emerge via a consensus process, but it may require many generations of trial a…
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…
When in Doubt: Improving Classification Performance with Alternating Normalization
Menglin Jia, Austin Reiter, Ser-Nam Lim +2
We introduce Classification with Alternating Normalization (CAN), a non-parametric post-processing step for classification. CAN improves classification accuracy for challenging exa…
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…
New Benchmarks for Learning on Non-Homophilous Graphs
Derek Lim, Xiuyu Li, Felix Hohne +1
Much data with graph structures satisfy the principle of homophily, meaning that connected nodes tend to be similar with respect to a specific attribute. As such, ubiquitous datase…
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…