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Found 39 papers, 16 papers with code
How Well Does GPT-4V(ision) Adapt to Distribution Shifts? A Preliminary Investigation
We further investigate its adaptability to controlled data perturbations and examine the efficacy of in-context learning as a tool to enhance its adaptation.
DiffAIL: Diffusion Adversarial Imitation Learning
To address this issue, we propose a method named diffusion adversarial imitation learning (DiffAIL), which introduces the diffusion model into the AIL framework.
Improving Generalization in Meta-Learning via Meta-Gradient Augmentation
The key idea is to first break the rote memories by network pruning to address memorization overfitting in the inner loop, and then the gradients of pruned sub-networks naturally form the high-quality augmentation of the meta-gradient to alleviate learner overfitting in the outer loop.
Recent Advances of Local Mechanisms in Computer Vision: A Survey and Outlook of Recent Work
We hope that this survey can shed light on future research in the computer vision field.
Towards Understanding Generalization of Macro-AUC in Multi-label Learning
We theoretically identify a critical factor of the dataset affecting the generalization bounds: \emph{the label-wise class imbalance}.
MetaViewer: Towards A Unified Multi-View Representation
To overcome them, we propose a novel bi-level-optimization-based multi-view learning framework, where the representation is learned in a uniform-to-specific manner.
Fine-Grained Classification with Noisy Labels
To this end, we propose a novel framework called stochastic noise-tolerated supervised contrastive learning (SNSCL) that confronts label noise by encouraging distinguishable representation.
MHPL: Minimum Happy Points Learning for Active Source Free Domain Adaptation
Source free domain adaptation (SFDA) aims to transfer a trained source model to the unlabeled target domain without accessing the source data.
Topological Structure Learning for Weakly-Supervised Out-of-Distribution Detection
Based on limited ID labeled data and sufficient unlabeled data, we define a new setting called Weakly-Supervised Out-of-Distribution Detection (WSOOD).
Self-Filtering: A Noise-Aware Sample Selection for Label Noise with Confidence Penalization
Sample selection is an effective strategy to mitigate the effect of label noise in robust learning.
DRNet: Decomposition and Reconstruction Network for Remote Physiological Measurement
Besides, a plug-and-play Spatial Attention Block (SAB) is proposed to enhance features along with the spatial location information.
Ranked #1 on
Heart rate estimation
on VIPL-HR
(using extra training data)
Heart rate estimation
Photoplethysmography (PPG) heart rate estimation
Neural Network Compression via Effective Filter Analysis and Hierarchical Pruning
Guided by that maximum rate, a novel and efficient hierarchical network pruning algorithm is developed to maximally condense the neuronal network structure without sacrificing network performance.
Active Source Free Domain Adaptation
We then propose minimum happy points learning (MHPL) to actively explore and exploit MH points.
Exploring Linear Feature Disentanglement For Neural Networks
Non-linear activation functions, e. g., Sigmoid, ReLU, and Tanh, have achieved great success in neural networks (NNs).
Series Photo Selection via Multi-view Graph Learning
Series photo selection (SPS) is an important branch of the image aesthetics quality assessment, which focuses on finding the best one from a series of nearly identical photos.
Sequential Multi-task Learning with Task Dependency for Appeal Judgment Prediction
SMAJudge utilizes two sequential components to model the complete proceeding from the lower court to the appellate court and employs an attention mechanism to make the prediction more explainable, which handles the challenges of AJP effectively.
A Graph Matching Perspective With Transformers on Video Instance Segmentation
Video Instance Segmentation (VIS) needs to automatically track and segment multiple objects in videos that rely on modeling the spatial-temporal interactions of the instances.
Exploring Domain-Invariant Parameters for Source Free Domain Adaptation
In contrast, we provide a fascinating insight: rather than attempting to learn domain-invariant representations, it is better to explore the domain-invariant parameters of the source model.
Safe-Student for Safe Deep Semi-Supervised Learning With Unseen-Class Unlabeled Data
To take advantage of these unseen-class data and ensure performance, we propose a safe SSL method called SAFE-STUDENT from the teacher-student view.
Learning to Rectify for Robust Learning with Noisy Labels
In this paper, we propose warped probabilistic inference (WarPI) to achieve adaptively rectifying the training procedure for the classification network within the meta-learning scenario.
Learning Transferable Parameters for Unsupervised Domain Adaptation
However, the learning processes of domain-invariant features and source hypothesis inevitably involve domain-specific information that would degrade the generalizability of UDA models on the target domain.
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.
Temporal-Relational Hypergraph Tri-Attention Networks for Stock Trend Prediction
Extensive experiments on real-world data demonstrate the effectiveness of our approach.
Tri-Branch Convolutional Neural Networks for Top-$k$ Focused Academic Performance Prediction
Academic performance prediction aims to leverage student-related information to predict their future academic outcomes, which is beneficial to numerous educational applications, such as personalized teaching and academic early warning.
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.
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.
A Survey on Natural Language Video Localization
Natural language video localization (NLVL), which aims to locate a target moment from a video that semantically corresponds to a text query, is a novel and challenging task.
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 Binary Semantic Embedding for Histology Image Classification and Retrieval
With the development of medical imaging technology and machine learning, computer-assisted diagnosis which can provide impressive reference to pathologists, attracts extensive research interests.
Attention Model Enhanced Network for Classification of Breast Cancer Image
In this study, we propose a novel method named Attention Model Enhanced Network (AMEN), which is formulated in a multi-branch fashion with pixel-wised attention model and classification submodular.
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.
Unifying Neural Learning and Symbolic Reasoning for Spinal Medical Report Generation
In this paper, we propose the neural-symbolic learning (NSL) framework that performs human-like learning by unifying deep neural learning and symbolic logical reasoning for the spinal medical report generation.
Towards Accurate and Robust Domain Adaptation under Noisy Environments
In non-stationary environments, learning machines usually confront the domain adaptation scenario where the data distribution does change over time.
Reinforcing Short-Length Hashing
Due to the compelling efficiency in retrieval and storage, similarity-preserving hashing has been widely applied to approximate nearest neighbor search in large-scale image retrieval.
PISEP^2: Pseudo Image Sequence Evolution based 3D Pose Prediction
Specifically, a skeletal representation is proposed by transforming the joint coordinate sequence into an image sequence, which can model the different correlations of different joints.
Ranked #1 on
Pose Prediction
on Filtered NTU RGB+D
Mutual Linear Regression-based Discrete Hashing
Label information is widely used in hashing methods because of its effectiveness of improving the precision.
Fusion Hashing: A General Framework for Self-improvement of Hashing
In this paper, in contrast to existing hashing methods, we propose a novel generalized framework called fusion hashing (FH) to improve the precision of existing hashing methods without adding new constraints or penalty terms.
Data Structures and Algorithms Multimedia
Learning Deep Match Kernels for Image-Set Classification
Image-set classification has recently generated great popularity due to its widespread applications in computer vision.
Comprehensive Feature-based Robust Video Fingerprinting Using Tensor Model
Therefore, in the present study, we mine the assistance and consensus among different features based on tensor model, and present a new comprehensive feature to fully use them in the proposed video fingerprinting framework.
