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- Joint Geometrical and Statistical Alignment for Visual Domain Adaptation598
A unified framework that reduces the shift between domains both statistically and geometrically, referred to as Joint Geometrical and Statistical Alignment (JGSA) is proposed.
- GMFlow: Learning Optical Flow via Global Matching583
A GMFlow framework is proposed, which consists of three main components: a customized Transformer for feature enhancement, a correlation and softmax layer for global feature matching, and a self-attention layer for flow propagation, which outperforms 31-refinements RAFT on the challenging Sintel benchmark, while using only one refinement and running faster.
- Importance Weighted Adversarial Nets for Partial Domain Adaptation461
This paper proposes a novel adversarial nets-based partial domain adaptation method to identify the source samples that are potentially from the outlier classes and, at the same time, reduce the shift of shared classes between domains.
- A deep learning algorithm using a fully connected sparse autoencoder neural network for landslide susceptibility prediction435
The asymmetric and unsupervised FC-SAE can extract optimal non-linear features from environmental factors successfully, outperforms some conventional machine learning methods, and is promising for LSP.
- UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders396
Inspired by the saliency data labeling process, a probabilistic RGB-D saliency detection network via conditional variational autoencoders to model human annotation uncertainty and generate multiple saliency maps for each input image by sampling in the latent space is proposed.
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- RGB-D-based action recognition datasets: A survey282
A comprehensive review of the most commonly used action recognition related RGB-D video datasets, including 27 single-view datasets, 10 multi- view datasets, and 7 multi-person datasets, resulting in a number of recommendations for collection of new datasets and use of evaluation protocols.
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- Deep Unsupervised Saliency Detection: A Multiple Noisy Labeling Perspective207
This work presents a novel perspective to unsupervised saliency detection through learning from multiple noisy labeling generated by "weak" and "noisy" unsuper supervised handcrafted saliency methods.
- VDM-DA: Virtual Domain Modeling for Source Data-Free Domain Adaptation146
This work deals with the source data-free unsupervised domain adaptation problem and proposes a novel approach referred to as Virtual Domain Modeling for Domain Adaptation (VDM-DA), in which the virtual domain acts as a bridge between the source and target domains.
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- Dynamic functional connectomics signatures for characterization and differentiation of PTSD patients144
Based on the structural connectomes constructed from diffusion tensor imaging data, FCs are derived from resting‐state fMRI (R‐fMRI) data and are then temporally divided into quasi‐stable segments via a sliding time window approach, suggesting that dynamic functional connectomics signatures can effectively characterize and differentiate PTSD patients.
- Real-Time Hand Gesture Recognition Using Finger Segmentation134
This work presents a novel real-time method for hand gesture recognition in which the hand region is extracted from the background with the background subtraction method and the palm and fingers are segmented so as to detect and recognize the fingers.
- RGB-D Saliency Detection via Cascaded Mutual Information Minimization131
This paper introduces a novel multistage cascaded learning framework via mutual information minimization to explicitly model the multi-modal information between RGB image and depth data.
- Recent Advances in Transfer Learning for Cross-Dataset Visual Recognition119
This survey categorises the cross-dataset recognition into 17 problems based on a set of carefully chosen data and label attributes to provide a systematic approach and a reference for a machine-learning practitioner to categorise a real problem and to look up for a possible solution accordingly.
- Diffusion Model is Secretly a Training-Free Open Vocabulary Semantic Segmenter115
The potential of generative text-to-image diffusion models as highly efficient open-vocabulary semantic segmenters are uncovered, and a novel training-free approach named DiffSegmenter is introduced that achieves impressive results for open-vocabulary semantic segmentation.
- ConvNets-Based Action Recognition from Depth Maps through Virtual Cameras and Pseudocoloring103
Through transferring learning the models originally trained over ImageNet for image classification, the three ConvNets are trained independently on the color-coded DMMs constructed in three orthogonal planes, achieving state-of-the-art results on these datasets.
- Weakly Supervised Video Salient Object Detection89
An "Appearance-motion fusion module" and bidirectional ConvLSTM based framework are proposed to achieve effective multi-modal learning and long-term temporal context modeling based on the first weakly supervised video salient object detection model based on relabeled “fixation guided scribble annotations”.
- Application of a common spatial pattern-based algorithm for an fNIRS-based motor imagery brain‐computer interface79
This is the first study demonstrating the effectiveness of the CSP method for fNIRS-based motor imagery BCIs, and it outperformed two representative channel-wise methods for classifying the two imagery statuses.
- Apple Tree Trunk and Branch Segmentation for Automatic Trellis Training Using Convolutional Neural Network Based Semantic Segmentation64
Assessments show the potential of the deep learning-based semantic segmentation for automated branch detection in the orchard environment, which provides a foundation for developing automated tree training systems.
- Aspect Composition in the Motorola Aspect-Oriented Modeling Weaver.64
This paper describes how aspect precedence can be specified explicitly at the modeling level in order to derive a correct composition order and therefore reduce the aspect interference problem in Aspect-Oriented Modeling (AOM).
- Emotion Feature Analysis and Recognition Based on Reconstructed EEG Sources60
It is demonstrated that the features from selected source regions can improve the classification accuracy by extensive experiments on the DEAP and TYUT 2.0 EEG-based datasets.
- Source Data-free Unsupervised Domain Adaptation for Semantic Segmentation58
This paper identifies and addresses a more practical but challenging problem of UDA for semantic segmentation, where access to the original source domain data is forbidden and only the pre-trained source model and unlabelled target domain data are available for adaptation.
- A comparative study of dimensionality reduction methods for large-scale image retrieval56
Through a series of experiments, it can be concluded that dimensionality reduction method of LLE and LPP can effectively reduce computational complexity of image retrieval, while maintaining high retrieval performance.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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