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08/11/2022
Semi-supervised Vision Transformers at Scale
We study semi-supervised learning (SSL) for vision transformers (ViT), a...
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08/03/2022
Masked Vision and Language Modeling for Multi-modal Representation Learning
In this paper, we study how to use masked signal modeling in vision and ...
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04/12/2022
X-DETR: A Versatile Architecture for Instance-wise Vision-Language Tasks
In this paper, we study the challenging instance-wise vision-language ta...
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04/07/2022
Class-Incremental Learning with Strong Pre-trained Models
Class-incremental learning (CIL) has been widely studied under the setti...
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03/30/2022
Task Adaptive Parameter Sharing for Multi-Task Learning
Adapting pre-trained models with broad capabilities has become standard ...
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07/16/2021
Representation Consolidation for Training Expert Students
Traditionally, distillation has been used to train a student model to em...
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01/29/2021
A linearized framework and a new benchmark for model selection for fine-tuning
Fine-tuning from a collection of models pre-trained on different domains...
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01/26/2021
Revisiting Contrastive Learning for Few-Shot Classification
Instance discrimination based contrastive learning has emerged as a lead...
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01/17/2021
Estimating informativeness of samples with Smooth Unique Information
We define a notion of information that an individual sample provides to ...
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08/28/2020
Predicting Training Time Without Training
We tackle the problem of predicting the number of optimization steps tha...
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02/13/2020
Continual Universal Object Detection
Object detection has improved significantly in recent years on multiple ...
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02/11/2020
Incremental Learning for Metric-Based Meta-Learners
Majority of the modern meta-learning methods for few-shot classification...
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05/10/2019