Mmap: Multi-modal alignment prompt for cross-domain multi-task learning
Multi-Task Learning (MTL) is designed to train multiple correlated tasks simultaneously,
thereby enhancing the performance of individual tasks. Typically, a multi-task network …
thereby enhancing the performance of individual tasks. Typically, a multi-task network …
Prompt learning in computer vision: a survey
Prompt learning has attracted broad attention in computer vision since the large pre-trained
vision-language models (VLMs) exploded. Based on the close relationship between vision …
vision-language models (VLMs) exploded. Based on the close relationship between vision …
Deep model fusion: A survey
Deep model fusion/merging is an emerging technique that merges the parameters or
predictions of multiple deep learning models into a single one. It combines the abilities of …
predictions of multiple deep learning models into a single one. It combines the abilities of …
Dualcoop++: Fast and effective adaptation to multi-label recognition with limited annotations
Multi-label image recognition in the low-label regime is a task of great challenge and
practical significance. Previous works have focused on learning the alignment between …
practical significance. Previous works have focused on learning the alignment between …
Global and local prompts cooperation via optimal transport for federated learning
Prompt learning in pretrained visual-language models has shown remarkable flexibility
across various downstream tasks. Leveraging its inherent lightweight nature recent research …
across various downstream tasks. Leveraging its inherent lightweight nature recent research …
Freestyleret: Retrieving images from style-diversified queries
Image Retrieval aims to retrieve corresponding images based on a given query. In
application scenarios, users intend to express their retrieval intent through various query …
application scenarios, users intend to express their retrieval intent through various query …
AAPL: Adding Attributes to Prompt Learning for Vision-Language Models
Recent advances in large pre-trained vision-language models have demonstrated
remarkable performance on zero-shot downstream tasks. Building upon this recent studies …
remarkable performance on zero-shot downstream tasks. Building upon this recent studies …
ArGue: Attribute-Guided Prompt Tuning for Vision-Language Models
Although soft prompt tuning is effective in efficiently adapting Vision-Language (V&L)
models for downstream tasks it shows limitations in dealing with distribution shifts. We …
models for downstream tasks it shows limitations in dealing with distribution shifts. We …
A comprehensive survey on meta-learning: Applications, advances, and challenges
J Wang - Authorea Preprints, 2024 - techrxiv.org
Meta-learning, or" learning to learn", enables machines to acquire general priors with
minimal supervision and rapidly adapt to new tasks. Unlike traditional AI methods that …
minimal supervision and rapidly adapt to new tasks. Unlike traditional AI methods that …
One Size Fits All for Semantic Shifts: Adaptive Prompt Tuning for Continual Learning
In real-world continual learning scenarios, tasks often exhibit intricate and unpredictable
semantic shifts, posing challenges for fixed prompt management strategies. We identify the …
semantic shifts, posing challenges for fixed prompt management strategies. We identify the …