63 citations · 176 across the 13 of their papers we have counts for
14 papers
Semi-supervised Vision Transformers at Scale
Zhaowei Cai, Avinash Ravichandran, Paolo Favaro +5
We study semi-supervised learning (SSL) for vision transformers (ViT), an under-explored topic despite the wide adoption of the ViT architectures to different tasks. To tackle this…
Masked Vision and Language Modeling for Multi-modal Representation Learning
Gukyeong Kwon, Zhaowei Cai, Avinash Ravichandran +3
In this paper, we study how to use masked signal modeling in vision and language (V+L) representation learning. Instead of developing masked language modeling (MLM) and masked imag…
X-DETR: A Versatile Architecture for Instance-wise Vision-Language Tasks
Zhaowei Cai, Gukyeong Kwon, Avinash Ravichandran +4
In this paper, we study the challenging instance-wise vision-language tasks, where the free-form language is required to align with the objects instead of the whole image. To addre…
Class-Incremental Learning with Strong Pre-trained Models
Tz-Ying Wu, Gurumurthy Swaminathan, Zhizhong Li +4
Class-incremental learning (CIL) has been widely studied under the setting of starting from a small number of classes (base classes). Instead, we explore an understudied real-world…
Task Adaptive Parameter Sharing for Multi-Task Learning
Matthew Wallingford, Hao Li, Alessandro Achille +4
Adapting pre-trained models with broad capabilities has become standard practice for learning a wide range of downstream tasks. The typical approach of fine-tuning different models…
Representation Consolidation for Training Expert Students
Zhizhong Li, Avinash Ravichandran, Charless Fowlkes +3
Traditionally, distillation has been used to train a student model to emulate the input/output functionality of a teacher. A more useful goal than emulation, yet under-explored, is…