Search Results for author:
Found 10 papers, 8 papers with code
Open-Set Image Tagging with Multi-Grained Text Supervision
Specifically, for predefined commonly used tag categories, RAM++ showcases 10. 2 mAP and 15. 4 mAP enhancements over CLIP on OpenImages and ImageNet.
Recognize Anything: A Strong Image Tagging Model
We are releasing the RAM at \url{https://recognize-anything. github. io/} to foster the advancements of large models in computer vision.
Knowledge Distillation from Single to Multi Labels: an Empirical Study
Knowledge distillation (KD) has been extensively studied in single-label image classification.
Tag2Text: Guiding Vision-Language Model via Image Tagging
This paper presents Tag2Text, a vision language pre-training (VLP) framework, which introduces image tagging into vision-language models to guide the learning of visual-linguistic features.
IDEA: Increasing Text Diversity via Online Multi-Label Recognition for Vision-Language Pre-training
However, the image-text pairs co-occurrent on the Internet typically lack explicit alignment information, which is suboptimal for VLP.
Simple and Robust Loss Design for Multi-Label Learning with Missing Labels
Multi-label learning in the presence of missing labels (MLML) is a challenging problem.
Towards Communication-Efficient and Privacy-Preserving Federated Representation Learning
This paper investigates the feasibility of federated representation learning under the constraints of communication cost and privacy protection.
On the Efficacy of Small Self-Supervised Contrastive Models without Distillation Signals
It is a consensus that small models perform quite poorly under the paradigm of self-supervised contrastive learning.
Prime-Aware Adaptive Distillation
With ten teacher-student combinations on six datasets, PAD promotes the performance of existing distillation methods and outperforms recent state-of-the-art methods.
Relating the structure of dark matter halos to their assembly and environment
Using decision trees built with the random ensemble method, we find that the correlation between halo concentration and assembly history is tight: more than 60% of the variance in halo concentration can be explained by assembly history alone.
Astrophysics of Galaxies Cosmology and Nongalactic Astrophysics
