Search Results for author:
Found 7 papers, 2 papers with code
DeCoOp: Robust Prompt Tuning with Out-of-Distribution Detection
In this paper, we explore a problem setting called Open-world Prompt Tuning (OPT), which involves tuning prompts on base classes and evaluating on a combination of base and new classes.
Investigating the Limitation of CLIP Models: The Worst-Performing Categories
Contrastive Language-Image Pre-training (CLIP) provides a foundation model by integrating natural language into visual concepts, enabling zero-shot recognition on downstream tasks.
Long-Tail Learning with Foundation Model: Heavy Fine-Tuning Hurts
The fine-tuning paradigm in addressing long-tail learning tasks has sparked significant interest since the emergence of foundation models.
Ranked #1 on
Long-tail Learning
on iNaturalist 2018
Fine-Grained Image Classification
Long-tail learning with class descriptors
A Survey on Extreme Multi-label Learning
Multi-label learning has attracted significant attention from both academic and industry field in recent decades.
Transfer and Share: Semi-Supervised Learning from Long-Tailed Data
TRAS transforms the imbalanced pseudo-label distribution of a traditional SSL model via a delicate function to enhance the supervisory signals for minority classes.
Prototypical Classifier for Robust Class-Imbalanced Learning
Deep neural networks have been shown to be very powerful methods for many supervised learning tasks.
Robust Long-Tailed Learning under Label Noise
To overcome this limitation, we establish a new prototypical noise detection method by designing a distance-based metric that is resistant to label noise.
Ranked #25 on
Image Classification
on mini WebVision 1.0
