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03/27/2023
Learning Expressive Prompting With Residuals for Vision Transformers
Prompt learning is an efficient approach to adapt transformers by insert...
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03/26/2023
WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation
Visual anomaly classification and segmentation are vital for automating ...
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03/07/2023
Introspective Cross-Attention Probing for Lightweight Transfer of Pre-trained Models
We propose InCA, a lightweight method for transfer learning that cross-a...
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03/02/2023
A Meta-Learning Approach to Predicting Performance and Data Requirements
We propose an approach to estimate the number of samples required for a ...
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09/13/2022
ComplETR: Reducing the cost of annotations for object detection in dense scenes with vision transformers
Annotating bounding boxes for object detection is expensive, time-consum...
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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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07/22/2022
Rethinking Few-Shot Object Detection on a Multi-Domain Benchmark
Most existing works on few-shot object detection (FSOD) focus on a setti...
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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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11/18/2021
DIVA: Dataset Derivative of a Learning Task
We present a method to compute the derivative of a learning task with re...
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08/03/2021
Uniform Sampling over Episode Difficulty
Episodic training is a core ingredient of few-shot learning to train mod...
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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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01/21/2021
Exponential Moving Average Normalization for Self-supervised and Semi-supervised Learning
We present a plug-in replacement for batch normalization (BN) called exp...
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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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12/24/2020
Mixed-Privacy Forgetting in Deep Networks
We show that the influence of a subset of the training samples can be re...
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12/21/2020
LQF: Linear Quadratic Fine-Tuning
Classifiers that are linear in their parameters, and trained by optimizi...
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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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11/28/2019
Unbiased Evaluation of Deep Metric Learning Algorithms
Deep metric learning (DML) is a popular approach for images retrieval, s...
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09/06/2019
A Baseline for Few-Shot Image Classification
Fine-tuning a deep network trained with the standard cross-entropy loss ...
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05/10/2019
Few-Shot Learning with Embedded Class Models and Shot-Free Meta Training
We propose a method for learning embeddings for few-shot learning that i...
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04/07/2019
Meta-Learning with Differentiable Convex Optimization
Many meta-learning approaches for few-shot learning rely on simple base ...
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02/10/2019
