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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.
SuperDisco: Super-Class Discovery Improves Visual Recognition for the Long-Tail
Modern image classifiers perform well on populated classes, while degrading considerably on tail classes with only a few instances.
Implicit Diffusion Models for Continuous Super-Resolution
IDM integrates an implicit neural representation and a denoising diffusion model in a unified end-to-end framework, where the implicit neural representation is adopted in the decoding process to learn continuous-resolution representation.
Ranked #1 on
Image Super-Resolution
on CelebA-HQ 128x128
Parameter-Free Channel Attention for Image Classification and Super-Resolution
The channel attention mechanism is a useful technique widely employed in deep convolutional neural networks to boost the performance for image processing tasks, eg, image classification and image super-resolution.
Meta Learning to Bridge Vision and Language Models for Multimodal Few-Shot Learning
Existing methods are trying to communicate visual concepts as prompts to frozen language models, but rely on hand-engineered task induction to reduce the hypothesis space.
Energy-Based Test Sample Adaptation for Domain Generalization
In this paper, we propose energy-based sample adaptation at test time for domain generalization.
CK-Transformer: Commonsense Knowledge Enhanced Transformers for Referring Expression Comprehension
The task of multimodal referring expression comprehension (REC), aiming at localizing an image region described by a natural language expression, has recently received increasing attention within the research comminity.
Variational Model Perturbation for Source-Free Domain Adaptation
Our model perturbation provides a new probabilistic way for domain adaptation which enables efficient adaptation to target domains while maximally preserving knowledge in source models.
Probabilistic Integration of Object Level Annotations in Chest X-ray Classification
This shows how two-stage learning of labels from coarse to fine-grained, in particular with object level annotations, is an effective method for more optimal annotation usage.
Association Graph Learning for Multi-Task Classification with Category Shifts
To generalize to such test data, it is crucial for individual tasks to leverage knowledge from related tasks.
LifeLonger: A Benchmark for Continual Disease Classification
Task and class incremental learning of diseases address the issue of classifying new samples without re-training the models from scratch, while cross-domain incremental learning addresses the issue of dealing with datasets originating from different institutions while retaining the previously obtained knowledge.
FCNet: A Convolutional Neural Network for Arbitrary-Length Exposure Estimation
For image exposure enhancement, the tasks of Single-Exposure Correction (SEC) and Multi-Exposure Fusion (MEF) are widely studied in the image processing community.
Learning to Generalize across Domains on Single Test Samples
We leverage a meta-learning paradigm to learn our model to acquire the ability of adaptation with single samples at training time so as to further adapt itself to each single test sample at test time.
Ranked #1 on
Domain Adaptation
on PACS
Generative Kernel Continual learning
Kernel continual learning by \citet{derakhshani2021kernel} has recently emerged as a strong continual learner due to its non-parametric ability to tackle task interference and catastrophic forgetting.
Hierarchical Variational Memory for Few-shot Learning Across Domains
To explore and exploit the importance of different semantic levels, we further propose to learn the weights associated with the prototype at each level in a data-driven way, which enables the model to adaptively choose the most generalizable features.
Learning to Learn Dense Gaussian Processes for Few-Shot Learning
In contrast to sparse Gaussian processes, we define a set of dense inducing variables to be of a much larger size than the support set in each task, which collects prior knowledge from experienced tasks.
Multi-Task Neural Processes
Our multi-task neural processes methodologically expand the scope of vanilla neural processes and provide a new way of exploring task relatedness in function spaces for multi-task learning.
Variational Multi-Task Learning with Gumbel-Softmax Priors
Multi-task learning aims to explore task relatedness to improve individual tasks, which is of particular significance in the challenging scenario that only limited data is available for each task.
Seminar Learning for Click-Level Weakly Supervised Semantic Segmentation
Annotation burden has become one of the biggest barriers to semantic segmentation.
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
Variational Topic Inference for Chest X-Ray Report Generation
The topics are inferred in a conditional variational inference framework, with each topic governing the generation of a sentence in the report.
Kernel Continual Learning
We further introduce variational random features to learn a data-driven kernel for each task.
Meta-Learning with Variational Semantic Memory for Word Sense Disambiguation
A critical challenge faced by supervised word sense disambiguation (WSD) is the lack of large annotated datasets with sufficient coverage of words in their diversity of senses.
Attentional Prototype Inference for Few-Shot Segmentation
We define a global latent variable to represent the prototype of each object category, which we model as a probabilistic distribution.
A Bit More Bayesian: Domain-Invariant Learning with Uncertainty
Domain generalization is challenging due to the domain shift and the uncertainty caused by the inaccessibility of target domain data.
MetaKernel: Learning Variational Random Features with Limited Labels
Specifically, we propose learning variational random features in a data-driven manner to obtain task-specific kernels by leveraging the shared knowledge provided by related tasks in a meta-learning setting.
Variational Knowledge Distillation for Disease Classification in Chest X-Rays
It is challenging to incorporate this information into disease classification due to the high reliance on clinician input in EHRs, limiting the possibility for automated diagnosis.
MetaNorm: Learning to Normalize Few-Shot Batches Across Domains
Batch normalization plays a crucial role when training deep neural networks.
Variational Multi-Task Learning
Multi-task learning aims to improve the overall performance of a set of tasks by leveraging their relatedness.
Variational Invariant Learning for Bayesian Domain Generalization
In the probabilistic modeling framework, we introduce a domain-invariant principle to explore invariance across domains in a unified way.
Direct Estimation of Spinal Cobb Angles by Structured Multi-Output Regression
The Cobb angle that quantitatively evaluates the spinal curvature plays an important role in the scoliosis diagnosis and treatment.
Learning to Learn Variational Semantic Memory
The variational semantic memory accrues and stores semantic information for the probabilistic inference of class prototypes in a hierarchical Bayesian framework.
Learning to Learn Kernels with Variational Random Features
We propose meta variational random features (MetaVRF) to learn adaptive kernels for the base-learner, which is developed in a latent variable model by treating the random feature basis as the latent variable.
Towards Direct Medical Image Analysis without Segmentation
Direct methods have recently emerged as an effective and efficient tool in automated medical image analysis and become a trend to solve diverse challenging tasks in clinical practise.
Supervised Descriptor Learning for Multi-Output Regression
In this paper, we propose a novel supervised descriptor learning (SDL) algorithm to establish a discriminative and compact feature representation for multi-output regression.
