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Claim this profileWorks143 from public data
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- Heart sound classification based on improved MFCC features and convolutional recurrent neural networks333
This paper proposes a new heart sound classification method based on improved Mel-frequency cepstrum coefficient (MFCC) features and convolutional recurrent neural networks and comprehensive studies on the performance of different network parameters and different network connection strategies are presented.
- ESSAformer: Efficient Transformer for Hyperspectral Image Super-resolution162
ESSA enlarges the receptive field for features after upsampling without bringing much computation and allows the model to effectively utilize spatial-spectral information from different scales, resulting in the generation of more natural high-resolution images.
- All-in-One Weather-Degraded Image Restoration Via Adaptive Degradation-Aware Self-Prompting Model160
This work develops an adaptive degradation-aware self-prompting model (ADSM) for all-in-one weather-degraded image restoration that employs the contrastive language-image pre-training model (CLIP) to facilitate the training of the proposed latent prompt generators (LPGs).
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- Deep learning based segmentation for automated training of apple trees on trellis wires148
A deep learning-based semantic segmentation method was developed for automatically performing segmenting trunks, branches, and trellis wires for automating apple tree training in orchard environment and showed a promising potential for adopting deep learning.
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- A deep learning, image based approach for automated diagnosis for inflammatory skin diseases126
An artificial intelligence dermatology diagnosis assistant (AIDDA) for Pso, Ecz & AD and healthy skins and the performance was evaluated on experts-confirmed clinical images grouped into 3 different dermatologist-labelled diagnosis classifications.
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- Toward Deeper Understanding of Camouflaged Object Detection115
This paper argues that the binary segmentation setting fails to fully understand the concept of camouflage, and presents the first triple-task learning framework to simultaneously localize, segment, and rank camouflaged objects, indicating the conspicuousness level of camouflage.
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- Audio-Visual Segmentation with Semantics104
A new baseline method is proposed that uses a temporal pixel-wise audio-visual interaction module to inject audio semantics as guidance for the visual segmentation process, and a regularization loss is designed to encourage audio-visual mapping during training.
- MLBF-Net: A Multi-Lead-Branch Fusion Network for Multi-Class Arrhythmia Classification Using 12-Lead ECG88
A novel Multi-Lead-Branch Fusion Network (MLBF-Net) architecture for arrhythmia classification by integrating multi-loss optimization to jointly learning diversity and integrity of multi-lead ECG is proposed.
- Predictive Uncertainty Estimation for Camouflaged Object Detection87
A predictive uncertainty estimation network (PUENet) that consists of a Bayesian conditional variational auto-encoder (BCVAE), and a predictive uncertainty approximation (PUA) module to avoid the expensive sampling process at test-time is presented.
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- Characterizing and Differentiating Brain State Dynamics via Hidden Markov Models83
A novel computational framework to quantitatively characterize the brain state dynamics via hidden Markov models (HMMs) learned from the observations of temporally dynamic functional connectomics, denoted as functional connectome states is proposed.
- Generative Transformer for Accurate and Reliable Salient Object Detection66
This work introduces a latent variable model within the transformer framework, termed the inferential generative adversarial network (iGAN), and applies it to fully supervised salient object detection, explaining that iGAN within the transformer framework leads to both accurate and reliable salient object detection.
- Multimodal Variational Auto-encoder based Audio-Visual Segmentation64
An Explicit Conditional Multimodal Variational Auto-Encoder for audio-visual segmentation (AVS), aiming to segment sound sources in the video sequence, with a new state-of-the-art performance leap on the challenging MS3 subset for multiple sound source segmentation.
- Learning Noise-Aware Encoder-Decoder from Noisy Labels by Alternating Back-Propagation for Saliency Detection64
A noise-aware encoder-decoder framework to disentangle a clean saliency predictor from noisy training examples, where the noisy labels are generated by unsupervised handcrafted feature-based methods is proposed.
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- Toward Real-world Single Image Deraining: A New Benchmark and Beyond56
The experimental results show the difference of representative methods in image restoration performance and model complexity, validate the significance of the proposed datasets for model generalization, and provide useful insights on the superiority of learning from diverse domains and shed lights on the future research on real-world SID.
- Contrastive Conditional Latent Diffusion for Audio-Visual Segmentation55
A contrastive conditional latent diffusion model for audio-visual segmentation (AVS) is proposed to thoroughly investigate the impact of audio, where the correlation between audio and the final segmentation map is modeled to guarantee the strong correlation between them.
- TAVGBench: Benchmarking Text to Audible-Video Generation53
A baseline model for TAVG is presented called TAVDiffusion, which uses a two-stream latent diffusion model to provide a fundamental starting point for further research in this area and proposes an automatic annotation pipeline to ensure each audible video has detailed descriptions for both its audio and video contents.
- Mutual Information Regularization for Weakly-Supervised RGB-D Salient Object Detection52
This paper presents a weakly-supervised RGB-D salient object detection model via scribble supervision, and introduces multimodal variational auto-encoder as stochastic prediction refinement techniques, which takes pseudo labels from the first training stage as supervision and generates refined prediction.
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- From heavy rain removal to detail restoration: A faster and better network45
A simple dual-stage progressive enhancement network, denoted as DPENet, aiming to achieve effective deraining while preserving the structural accuracy of rain-free images and employs a comprehensive loss function that accentuates both the marginal and regional accuracy of rain-free images.
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- Siamese Network with Interactive Transformer for Video Object Segmentation43
A novel Siamese network with a specifically designed interactive transformer, called SITVOS, to enable effective context propagation from historical to current frames and to extract backbone features of both past and current frames, which enables feature reuse and is more efficient than existing methods.
- Deep time–frequency representation and progressive decision fusion for ECG classification43
A novel ECG classification method is proposed by learning deep time-frequency representation and progressive decision fusion at different temporal scales in an end-to-end manner to fuse decisions from the scale-specific models into a more accurate and stable one.
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- Haptics in computer-mediated simulation: Training in vertebroplasty surgery37
The authors describe a PC-based system for the simulation of the vertebroplasty procedure and show that with virtual reality gaming technologies, the surgical simulator can become a virtual trainer for a potentially risky spinal interventional procedure.
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- Learning Saliency From Single Noisy Labelling: A Robust Model Fitting Perspective31
This paper calls on the theory of robust model fitting and formulate deep saliency prediction from a single noisy labelling as robust network learning and exploit model consistency across iterations to identify inliers and outliers.
- Transmission-Guided Bayesian Generative Model for Smoke Segmentation30
A transmission-guided local coherence loss is designed to guide the network to learn pair-wise relationships based on pixel distance and the transmission feature to effectively model the two types of uncertainty in smoke segmentation.
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- Encoder-Minimal and Decoder-Minimal Framework for Remote Sensing Image Dehazing25
This work develops an innovative module called intra-level transposed fusion module (ITFM) that employs adaptive transposed self-attention to capture comprehensive context-aware information, facilitating the robust context-aware feature fusion.
- Weakly-Supervised Contrastive Learning for Unsupervised Object Discovery23
A semantic-guided self-supervised learning model to extract high-level semantic features from images is designed by fine-tuning the feature encoder of a self-supervised model, namely DINO, via WCL, and Principal Component Analysis (PCA) is introduced to localize object regions.
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- Unsupervised Conditional Consensus Adversarial Network for Brain Disease Identification with Structural MRI20
Experimental results on 1, 506 subjects from ADNI1 and ADNI2 have demonstrated the effectiveness of the proposed UCAN method in brain disease identification, compared with state-of-the-art approaches.
- Inferring the Class Conditional Response Map for Weakly Supervised Semantic Segmentation19
The class conditional inference-time approach is presented to separately and iteratively reveal the classification network’s hidden object activation to generate more complete response maps and introduce a novel way to exploit saliency maps during segmentation training and refine the foreground object masks without suppressing background objects.
- Measuring and Modeling Uncertainty Degree for Monocular Depth Estimation15
This paper proposes to model the uncertainty of MDE models from the perspective of the inherent probability distributions originating from the depth probability volume and its extensions, and to assess it more fairly with more comprehensive metrics.
- Energy-Based Generative Cooperative Saliency Prediction15
A generative cooperative saliency prediction framework, where a conditional latent variable model and a conditional energy-based model are jointly trained to predict salient objects in a cooperative manner, which can produce a set of diverse and plausible saliency maps of an image.
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- Rethinking Polyp Segmentation From An Out-of-distribution Perspective14
This work rethink colorectal polyp segmentation from an out-of-distribution perspective with a simple but effective self-supervised learning approach that has excellent segmentation performance and generalises across datasets.
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- Model Calibration in Dense Classification with Adaptive Label Perturbation7
Adaptive Stochastic Label Perturbation (ASLP) is proposed which learns a unique label perturbation level for each training image and improves model calibration degree by minimising the gap between the prediction accuracy and expected confidence of the target training label.
- Decompose Semantic Shifts for Composed Image Retrieval7
A Semantic Shift network (SSN) is proposed that explicitly decomposes the semantic shifts into two steps: from the reference image to the visual prototype and from theVisual prototype to the target image.
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- Generalised Co-Salient Object Detection6
A novel random sampling based Generalised CoSOD Training (GCT) strategy to distill the awareness of inter-image absence of co-salient objects into coSOD models by allowing the presence of "noisy images" which do not show the shared co-Salient object.
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- An Energy-Based Prior for Generative Saliency3
Experimental results show that the proposed generative saliency model with an energy-based prior can achieve not only accurate saliency predictions but also reliable uncertainty maps that are consistent with human perception.
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- Learning Gaussian Representation for Eye Fixation Prediction1
This work proposes to model the eye fixation map as a mixture of probability distributions, namely a Gaussian Mixture Model, and designs a framework upon some lightweight backbones to achieve real-time fixation prediction.
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- An unsupervised medical image registration network for intelligent medical education1
A cross-view interactive network (CVIN) is proposed, which utilizes alternate stages with information interaction modules to extract data features, which can use unlabeled medical data to train the model and introduces an InfoMax module to learn the mutual information between registered images.
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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 2026-10-16. 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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