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Found 4 papers, 1 papers with code
Improving Image Clustering with Artifacts Attenuation via Inference-Time Attention Engineering
The goal of this paper is to improve the performance of pretrained Vision Transformer (ViT) models, particularly DINOv2, in image clustering task without requiring re-training or fine-tuning.
Ranked #1 on
Image Clustering
on Tiny-ImageNet
(using extra training data)
Revisiting Relevance Feedback for CLIP-based Interactive Image Retrieval
However, metric learning cannot handle differences in users' preferences, and requires data to train an image encoder.
Revisiting a kNN-based Image Classification System with High-capacity Storage
Moreover, users cannot verify the validity of inference results or evaluate the contribution of knowledge to the results.
Ranked #1 on
Incremental Learning
on ImageNet - 10 steps
(using extra training data)
Deploying Image Deblurring across Mobile Devices: A Perspective of Quality and Latency
To the best of our knowledge, this is the first paper that addresses all the deployment issues of image deblurring task across mobile devices.
