Search Results
Found 43 papers, 21 papers with code
Episodic Multi-Task Learning with Heterogeneous Neural Processes
This paper focuses on the data-insufficiency problem in multi-task learning within an episodic training setup.
Order-preserving Consistency Regularization for Domain Adaptation and Generalization
To alleviate this problem, data augmentation coupled with consistency regularization are commonly adopted to make the model less sensitive to domain-specific attributes.
Learning Cross-Modal Affinity for Referring Video Object Segmentation Targeting Limited Samples
Referring video object segmentation (RVOS), as a supervised learning task, relies on sufficient annotated data for a given scene.
Referring Video Object Segmentation
Semantic Segmentation
+1
Knowledge Graph Embeddings for Multi-Lingual Structured Representations of Radiology Reports
We show the use of this embedding on two tasks namely disease classification of X-ray reports and image classification.
Knowledge-Aware Prompt Tuning for Generalizable Vision-Language Models
Pre-trained vision-language models, e. g., CLIP, working with manually designed prompts have demonstrated great capacity of transfer learning.
Lightweight Improved Residual Network for Efficient Inverse Tone Mapping
But the majority of media images on the internet remain in 8-bit standard dynamic range (SDR) format.
Learning Variational Neighbor Labels for Test-Time Domain Generalization
We formulate the generalization at test time as a variational inference problem by modeling pseudo labels as distributions to consider the uncertainty during generalization and alleviate the misleading signal of inaccurate pseudo labels.
EMO: Episodic Memory Optimization for Few-Shot Meta-Learning
By learning to retain and recall the learning process of past training tasks, EMO nudges parameter updates in the right direction, even when the gradients provided by a limited number of examples are uninformative.
MetaModulation: Learning Variational Feature Hierarchies for Few-Shot Learning with Fewer Tasks
To account for the uncertainty caused by the limited training tasks, we propose a variational MetaModulation where the modulation parameters are treated as latent variables.
CageViT: Convolutional Activation Guided Efficient Vision Transformer
Recently, Transformers have emerged as the go-to architecture for both vision and language modeling tasks, but their computational efficiency is limited by the length of the input sequence.
