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Claim this profileWorks143 from public data
- 342
- 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).
- 160
- 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.
- 133
- 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.
- 120
- 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.
- 110
- 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.
- 83
- 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.
- 61
- 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.
- 51
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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