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Found 6 papers, 2 papers with code
Universal Noise Annotation: Unveiling the Impact of Noisy annotation on Object Detection
For object detection task with noisy labels, it is important to consider not only categorization noise, as in image classification, but also localization noise, missing annotations, and bogus bounding boxes.
Misalign, Contrast then Distill: Rethinking Misalignments in Language-Image Pretraining
Contrastive Language-Image Pretraining has emerged as a prominent approach for training vision and text encoders with uncurated image-text pairs from the web.
Expediting Contrastive Language-Image Pretraining via Self-distilled Encoders
Based on the unified text embedding space, ECLIPSE compensates for the additional computational cost of the momentum image encoder by expediting the online image encoder.
Misalign, Contrast then Distill: Rethinking Misalignments in Language-Image Pre-training
Contrastive Language-Image Pretraining has emerged as a prominent approach for training vision and text encoders with uncurated image-text pairs from the web.
Rethinking Deep Image Prior for Denoising
Deep image prior (DIP) serves as a good inductive bias for diverse inverse problems.
Incremental Learning with Maximum Entropy Regularization: Rethinking Forgetting and Intransigence
Incremental learning suffers from two challenging problems; forgetting of old knowledge and intransigence on learning new knowledge.
