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Found 8 papers, 4 papers with code
SemiGPC: Distribution-Aware Label Refinement for Imbalanced Semi-Supervised Learning Using Gaussian Processes
We show that SemiGPC improves performance when paired with different Semi-Supervised methods such as FixMatch, ReMixMatch, SimMatch and FreeMatch and different pre-training strategies including MSN and Dino.
A Meta-Learning Approach to Predicting Performance and Data Requirements
The PPL improves the performance estimation on average by 37% across 16 classification and 33% across 10 detection datasets, compared to the power law.
ComplETR: Reducing the cost of annotations for object detection in dense scenes with vision transformers
Combined with a matching loss, it can effectively find objects that are similar to the input patch and complete the missing annotations.
Rethinking Few-Shot Object Detection on a Multi-Domain Benchmark
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.
Class-Incremental Learning with Strong Pre-trained Models
We hypothesize that a strong base model can provide a good representation for novel classes and incremental learning can be done with small adaptations.
Omni-DETR: Omni-Supervised Object Detection with Transformers
This is enabled by a unified architecture, Omni-DETR, based on the recent progress on student-teacher framework and end-to-end transformer based object detection.
Ranked #14 on
Semi-Supervised Object Detection
on COCO 2% labeled data
Out-of-the-box channel pruned networks
We then demonstrate that if we found the profiles using a mid-sized dataset such as Cifar10/100, we are able to transfer them to even a large dataset such as Imagenet.
$d$-SNE: Domain Adaptation using Stochastic Neighborhood Embedding
Deep neural networks often require copious amount of labeled-data to train their scads of parameters.
