9 citations · 14 across the 8 of their papers we have counts for
8 papers
SemiGPC: Distribution-Aware Label Refinement for Imbalanced Semi-Supervised Learning Using Gaussian Processes
Abdelhak Lemkhenter, Manchen Wang, Luca Zancato +3
In this paper we introduce SemiGPC, a distribution-aware label refinement strategy based on Gaussian Processes where the predictions of the model are derived from the labels poster…
A Meta-Learning Approach to Predicting Performance and Data Requirements
Achin Jain, Gurumurthy Swaminathan, Paolo Favaro +8
We propose an approach to estimate the number of samples required for a model to reach a target performance. We find that the power law, the de facto principle to estimate model pe…
ComplETR: Reducing the cost of annotations for object detection in dense scenes with vision transformers
Achin Jain, Kibok Lee, Gurumurthy Swaminathan +4
Annotating bounding boxes for object detection is expensive, time-consuming, and error-prone. In this work, we propose a DETR based framework called ComplETR that is designed to ex…
Rethinking Few-Shot Object Detection on a Multi-Domain Benchmark
Kibok Lee, Hao Yang, Satyaki Chakraborty +4
Most existing works on few-shot object detection (FSOD) focus on a setting where both pre-training and few-shot learning datasets are from a similar domain. However, few-shot algor…
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…
Omni-DETR: Omni-Supervised Object Detection with Transformers
Pei Wang, Zhaowei Cai, Hao Yang +4
We consider the problem of omni-supervised object detection, which can use unlabeled, fully labeled and weakly labeled annotations, such as image tags, counts, points, etc., for ob…
Out-of-the-box channel pruned networks
Ragav Venkatesan, Gurumurthy Swaminathan, Xiong Zhou +1
In the last decade convolutional neural networks have become gargantuan. Pre-trained models, when used as initializers are able to fine-tune ever larger networks on small datasets.…
-SNE: Domain Adaptation using Stochastic Neighborhood Embedding
Xiang Xu, Xiong Zhou, Ragav Venkatesan +2
Deep neural networks often require copious amount of labeled-data to train their scads of parameters. Training larger and deeper networks is hard without appropriate regularization…