Search Results for author:
Found 7 papers, 3 papers with code
Understanding and Mitigating Human-Labelling Errors in Supervised Contrastive Learning
In this paper, we show that human-labelling errors not only differ significantly from synthetic label errors, but also pose unique challenges in SCL, different to those in traditional supervised learning methods.
CLCE: An Approach to Refining Cross-Entropy and Contrastive Learning for Optimized Learning Fusion
State-of-the-art pre-trained image models predominantly adopt a two-stage approach: initial unsupervised pre-training on large-scale datasets followed by task-specific fine-tuning using Cross-Entropy loss~(CE).
Foveation in the Era of Deep Learning
In this paper, we tackle the challenge of actively attending to visual scenes using a foveated sensor.
Elucidating and Overcoming the Challenges of Label Noise in Supervised Contrastive Learning
However, while the detrimental effects of noisy labels in supervised learning are well-researched, their influence on SCL remains largely unexplored.
RoboLLM: Robotic Vision Tasks Grounded on Multimodal Large Language Models
Robotic vision applications often necessitate a wide range of visual perception tasks, such as object detection, segmentation, and identification.
MultiWay-Adapater: Adapting large-scale multi-modal models for scalable image-text retrieval
As Multimodal Large Language Models (MLLMs) grow in size, adapting them to specialized tasks becomes increasingly challenging due to high computational and memory demands.
When hard negative sampling meets supervised contrastive learning
State-of-the-art image models predominantly follow a two-stage strategy: pre-training on large datasets and fine-tuning with cross-entropy loss.
