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Found 57 papers, 35 papers with code
Constructing and Exploring Intermediate Domains in Mixed Domain Semi-supervised Medical Image Segmentation
To fully utilize the information within the intermediate domain, we propose a symmetric Guidance training strategy (SymGD), which additionally offers direct guidance to unlabeled data by merging pseudo labels from intermediate samples.
DPStyler: Dynamic PromptStyler for Source-Free Domain Generalization
The Style Generation module refreshes all styles at every training epoch, while the Style Removal module eliminates variations in the encoder's output features caused by input styles.
SETA: Semantic-Aware Token Augmentation for Domain Generalization
In this paper, we study the impact of prior CNN-based augmentation methods on token-based models, revealing their performance is suboptimal due to the lack of incentivizing the model to learn holistic shape information.
Concatenate, Fine-tuning, Re-training: A SAM-enabled Framework for Semi-supervised 3D Medical Image Segmentation
Segment Anything Model (SAM) fine-tuning has shown remarkable performance in medical image segmentation in a fully supervised manner, but requires precise annotations.
A SAM-guided Two-stream Lightweight Model for Anomaly Detection
In this paper, considering these two critical factors, we propose a SAM-guided Two-stream Lightweight Model for unsupervised anomaly detection (STLM) that not only aligns with the two practical application requirements but also harnesses the robust generalization capabilities of SAM.
OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems
Notably, the best-performing model, GPT-4V, attains an average score of 17. 23% on OlympiadBench, with a mere 11. 28% in physics, highlighting the benchmark rigor and the intricacy of physical reasoning.
Learning Generalizable Models via Disentangling Spurious and Enhancing Potential Correlations
2) From the feature perspective, the simple Tail Interaction module implicitly enhances potential correlations among all samples from all source domains, facilitating the acquisition of domain-invariant representations across multiple domains for the model.
PG-LBO: Enhancing High-Dimensional Bayesian Optimization with Pseudo-Label and Gaussian Process Guidance
Based on this technique, we assign appropriate training weights to unlabeled data to enhance the construction of a discriminative latent space.
Roll With the Punches: Expansion and Shrinkage of Soft Label Selection for Semi-supervised Fine-Grained Learning
While semi-supervised learning (SSL) has yielded promising results, the more realistic SSL scenario remains to be explored, in which the unlabeled data exhibits extremely high recognition difficulty, e. g., fine-grained visual classification in the context of SSL (SS-FGVC).
Scalable Label Distribution Learning for Multi-Label Classification
Most existing MLC methods are based on the assumption that the correlation of two labels in each label pair is symmetric, which is violated in many real-world scenarios.
DoubleAUG: Single-domain Generalized Object Detector in Urban via Color Perturbation and Dual-style Memory
In this paper, we aim to solve the single-domain generalizable object detection task in urban scenarios, meaning that a model trained on images from one weather condition should be able to perform well on images from any other weather conditions.
Exploring Flat Minima for Domain Generalization with Large Learning Rates
Instead, we observe that leveraging a large learning rate can simultaneously promote weight diversity and facilitate the identification of flat regions in the loss landscape.
Enhancing Sample Utilization through Sample Adaptive Augmentation in Semi-Supervised Learning
Sample adaptive augmentation (SAA) is proposed for this stated purpose and consists of two modules: 1) sample selection module; 2) sample augmentation module.
A Theoretical Explanation of Activation Sparsity through Flat Minima and Adversarial Robustness
Although having attributed it to training dynamics, existing theoretical explanations of activation sparsity are restricted to shallow networks, small training steps and special training, despite its emergence in deep models standardly trained for a large number of steps.
IOMatch: Simplifying Open-Set Semi-Supervised Learning with Joint Inliers and Outliers Utilization
Semi-supervised learning (SSL) aims to leverage massive unlabeled data when labels are expensive to obtain.
DomainDrop: Suppressing Domain-Sensitive Channels for Domain Generalization
Deep Neural Networks have exhibited considerable success in various visual tasks.
DomainAdaptor: A Novel Approach to Test-time Adaptation
To deal with the domain shift between training and test samples, current methods have primarily focused on learning generalizable features during training and ignore the specificity of unseen samples that are also critical during the test.
Generalizable Decision Boundaries: Dualistic Meta-Learning for Open Set Domain Generalization
Domain generalization (DG) is proposed to deal with the issue of domain shift, which occurs when statistical differences exist between source and target domains.
Towards Semi-supervised Learning with Non-random Missing Labels
Semi-supervised learning (SSL) tackles the label missing problem by enabling the effective usage of unlabeled data.
Shrinking Class Space for Enhanced Certainty in Semi-Supervised Learning
To mitigate potentially incorrect pseudo labels, recent frameworks mostly set a fixed confidence threshold to discard uncertain samples.
A Novel Cross-Perturbation for Single Domain Generalization
Horizontally, it applies image-level and feature-level perturbations to enhance the diversity of the training data, mitigating the issue of limited diversity in single-source domains.
3D Medical Image Segmentation with Sparse Annotation via Cross-Teaching between 3D and 2D Networks
Our experimental results on the MMWHS dataset demonstrate that our method outperforms the state-of-the-art (SOTA) semi-supervised segmentation methods.
NormAUG: Normalization-guided Augmentation for Domain Generalization
Our method includes two paths: the main path and the auxiliary (augmented) path.
Generalizable Metric Network for Cross-domain Person Re-identification
Additionally, we introduce the Dropout-based Perturbation (DP) module to enhance the generalization capability of the metric network by enriching the sample-pair diversity.
Patch-aware Batch Normalization for Improving Cross-domain Robustness
By exploiting the differences between local patches of an image, our proposed PBN can effectively enhance the robustness of the model's parameters.
Orthogonal Annotation Benefits Barely-supervised Medical Image Segmentation
Subsequently, by introducing unlabeled volumes, we propose a dual-network paradigm named Dense-Sparse Co-training (DeSCO) that exploits dense pseudo labels in early stage and sparse labels in later stage and meanwhile forces consistent output of two networks.
ALOFT: A Lightweight MLP-like Architecture with Dynamic Low-frequency Transform for Domain Generalization
However, the local operation of the convolution kernel makes the model focus too much on local representations (e. g., texture), which inherently causes the model more prone to overfit to the source domains and hampers its generalization ability.
eVAE: Evolutionary Variational Autoencoder
VAEs still suffer from uncertain tradeoff learning. We propose a novel evolutionary variational autoencoder (eVAE) building on the variational information bottleneck (VIB) theory and integrative evolutionary neural learning.
Revisiting Weak-to-Strong Consistency in Semi-Supervised Semantic Segmentation
In this work, we revisit the weak-to-strong consistency framework, popularized by FixMatch from semi-supervised classification, where the prediction of a weakly perturbed image serves as supervision for its strongly perturbed version.
Semi-supervised Change Detection
Semi-supervised Medical Image Segmentation
+1
MultiMatch: Multi-task Learning for Semi-supervised Domain Generalization
To address the task, we first analyze the theory of the multi-domain learning, which highlights that 1) mitigating the impact of domain gap and 2) exploiting all samples to train the model can effectively reduce the generalization error in each source domain so as to improve the quality of pseudo-labels.
RDA: Reciprocal Distribution Alignment for Robust Semi-supervised Learning
In this work, we propose Reciprocal Distribution Alignment (RDA) to address semi-supervised learning (SSL), which is a hyperparameter-free framework that is independent of confidence threshold and works with both the matched (conventionally) and the mismatched class distributions.
Generalizable Medical Image Segmentation via Random Amplitude Mixup and Domain-Specific Image Restoration
We demonstrate the performance of our method on two public generalizable segmentation benchmarks in medical images, which validates our method could achieve the state-of-the-art performance.
Label Distribution Learning for Generalizable Multi-source Person Re-identification
Besides, for the label distribution of each class, we further revise it to give more and equal attention to the other domains that the class does not belong to, which can effectively reduce the domain gap across different domains and obtain the domain-invariant feature.
MutexMatch: Semi-Supervised Learning with Mutex-Based Consistency Regularization
The core issue in semi-supervised learning (SSL) lies in how to effectively leverage unlabeled data, whereas most existing methods tend to put a great emphasis on the utilization of high-confidence samples yet seldom fully explore the usage of low-confidence samples.
A Novel Mix-normalization Method for Generalizable Multi-source Person Re-identification
Different from the conventional data augmentation, the proposed domain-aware mix-normalization to enhance the diversity of features during training from the normalization view of the neural network, which can effectively alleviate the model overfitting to the source domains, so as to boost the generalization capability of the model in the unseen domain.
DC-SSL: Addressing Mismatched Class Distribution in Semi-Supervised Learning
Consistency-based Semi-supervised learning (SSL) has achieved promising performance recently.
MVDG: A Unified Multi-view Framework for Domain Generalization
Beyond the training stage, overfitting could also cause unstable prediction in the test stage.
Generalizable Cross-modality Medical Image Segmentation via Style Augmentation and Dual Normalization
For medical image segmentation, imagine if a model was only trained using MR images in source domain, how about its performance to directly segment CT images in target domain?
PLACE dropout: A Progressive Layer-wise and Channel-wise Dropout for Domain Generalization
Particularly, the proposed method can generate a variety of data variants to better deal with the overfitting issue.
Unsupervised Domain Generalization for Person Re-identification: A Domain-specific Adaptive Framework
A significance of our work lies in that it shows the potential of unsupervised domain generalization for person ReID and sets a strong baseline for the further research on this topic.
Inconsistency-aware Uncertainty Estimation for Semi-supervised Medical Image Segmentation
In semi-supervised medical image segmentation, most previous works draw on the common assumption that higher entropy means higher uncertainty.
Better Pseudo-label: Joint Domain-aware Label and Dual-classifier for Semi-supervised Domain Generalization
Also, considering inconsistent goals between generalization and pseudo-labeling: former prevents overfitting on all source domains while latter might overfit the unlabeled source domains for high accuracy, we employ a dual-classifier to independently perform pseudo-labeling and domain generalization in the training process.
Crosslink-Net: Double-branch Encoder Segmentation Network via Fusing Vertical and Horizontal Convolutions
Together, the above two schemes give rise to a novel double-branch encoder segmentation framework for medical image segmentation, namely Crosslink-Net.
Learngene: From Open-World to Your Learning Task
Moreover, the learngene, i. e., the gene for learning initialization rules of the target model, is proposed to inherit the meta-knowledge from the collective model and reconstruct a lightweight individual model on the target task.
ST++: Make Self-training Work Better for Semi-supervised Semantic Segmentation
In this work, we first construct a strong baseline of self-training (namely ST) for semi-supervised semantic segmentation via injecting strong data augmentations (SDA) on unlabeled images to alleviate overfitting noisy labels as well as decouple similar predictions between the teacher and student.
Feature-based Style Randomization for Domain Generalization
As a recent noticeable topic, domain generalization (DG) aims to first learn a generic model on multiple source domains and then directly generalize to an arbitrary unseen target domain without any additional adaption.
Mining Latent Classes for Few-shot Segmentation
Our method aims to alleviate this problem and enhance the feature embedding on latent novel classes.
Ranked #41 on
Few-Shot Semantic Segmentation
on PASCAL-5i (5-Shot)
A novel multiple instance learning framework for COVID-19 severity assessment via data augmentation and self-supervised learning
How to fast and accurately assess the severity level of COVID-19 is an essential problem, when millions of people are suffering from the pandemic around the world.
Deep Symmetric Adaptation Network for Cross-modality Medical Image Segmentation
These typical methods usually utilize a translation network to transform images from the source domain to target domain or train the pixel-level classifier merely using translated source images and original target images.
Generalizable Model-agnostic Semantic Segmentation via Target-specific Normalization
Semantic segmentation in a supervised learning manner has achieved significant progress in recent years.
Differentiable Meta-learning Model for Few-shot Semantic Segmentation
To address the annotation scarcity issue in some cases of semantic segmentation, there have been a few attempts to develop the segmentation model in the few-shot learning paradigm.
Defensive Few-shot Learning
(2) how to narrow the distribution gap between clean and adversarial examples under the few-shot setting?
Progressive Cross-camera Soft-label Learning for Semi-supervised Person Re-identification
In this paper, we focus on the semi-supervised person re-identification (Re-ID) case, which only has the intra-camera (within-camera) labels but not inter-camera (cross-camera) labels.
GreyReID: A Two-stream Deep Framework with RGB-grey Information for Person Re-identification
Moreover, in the training process, we adopt the joint learning scheme to simultaneously train each branch by the independent loss function, which can enhance the generalization ability of each branch.
Adversarial Camera Alignment Network for Unsupervised Cross-camera Person Re-identification
To achieve the camera alignment, we develop a Multi-Camera Adversarial Learning (MCAL) to map images of different cameras into a shared subspace.
A Novel Unsupervised Camera-aware Domain Adaptation Framework for Person Re-identification
For the first issue, we highlight the presence of camera-level sub-domains as a unique characteristic of person Re-ID, and develop camera-aware domain adaptation to reduce the discrepancy not only between source and target domains but also across these sub-domains.
Ranked #19 on
Unsupervised Domain Adaptation
on Market to Duke
MaskReID: A Mask Based Deep Ranking Neural Network for Person Re-identification
Lastly, considering person retrieval is a special image retrieval task, we propose a novel ranking loss to optimize the whole network.
