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- A reinforcement learning approach to job-shop scheduling480
Reinforcement learning methods are applied to learn domain-specific heuristics for job shop scheduling to suggest that reinforcement learning can provide a new method for constructing high-performance scheduling systems.
- Reconstruction Network for Video Captioning358
A reconstruction network with a novel encoder-decoder-reconstructor architecture, which leverages both the forward (video to sentence) and backward (sentence to video) flows for video captioning, and can boost the encoding models and leads to significant gains in video caption accuracy.
- PUGAN: Physical Model-Guided Underwater Image Enhancement Using GAN With Dual-Discriminators351
This paper proposes a physical model-guided GAN model for UIE, referred to as PUGAN, and designs the Dual-Discriminators for the style-content adversarial constraint, promoting the authenticity and visual aesthetics of the results.
- UAV-Human: A Large Benchmark for Human Behavior Understanding with Unmanned Aerial Vehicles282
A fisheye-based action recognition method that mitigates the distortions in fisHEye videos via learning unbounded transformations guided by flat RGB videos is proposed, and experiments show the efficacy of this method on the UAV-Human dataset.
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- Controllable Video Captioning With POS Sequence Guidance Based on Gated Fusion Network190
A gating strategy is proposed to dynamically and adaptively incorporate the global syntactic POS information into the decoder for generating each word, which not only boosts the video captioning performance but also improves the diversity of the generated captions.
- Computerized detection of clustered microcalcifications in digital mammograms using a shift‐invariant artificial neural network184
This study applies a shift-invariant neural network to eliminate false-positive detections reported by the CAD scheme, considerably better than that obtained in the previous study using a conventional three-layer, feed-forward neural network.
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- Frequency Perception Network for Camouflaged Object Detection158
This work proposes a novel learnable and separable frequency perception mechanism driven by the semantic hierarchy in the frequency domain that achieves competitive performance in three popular benchmark datasets both qualitatively and quantitatively.
- High-Performance Job-Shop Scheduling With A Time-Delay TD(λ) Network133
Experimental tests show that this TDNN-TD(λ) network can match the performance of the previous hand-engineered system, and both neural network approaches significantly outperform the best previous (non-learning) solution to this problem.
- An improved shift‐invariant artificial neural network for computerized detection of clustered microcalcifications in digital mammograms130
Modifications were made to improve the performance of the SIANN and the zero-mean-weight constraint and training-free-zone techniques have been developed and a cross-validation training method was also applied to avoid the overtraining problem.
- Head Fusion: Improving the Accuracy and Robustness of Speech Emotion Recognition on the IEMOCAP and RAVDESS Dataset125
This work implemented an attention-based convolutional neural network (ACNN) model and conducted experiments on the Interactive Emotional Dyadic Motion Capture (IEMOCAP) data set and proposed a method called Head Fusion based on the multi-head attention mechanism to improve the accuracy of SER.
- Point-aware Interaction and CNN-induced Refinement Network for RGB-D Salient Object Detection99
This work introduces CNNs-assisted Transformer architecture and proposes a novel RGB-D SOD network with Point-aware Interaction and CNN-induced Refinement (PICR-Net), which achieves competitive results in both quantitative and qualitative comparisons.
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- Optimally weighted wavelet transform based on supervised training for detection of microcalcifications in digital mammograms78
A technique for optimizing the weights at individual scales in the wavelet transform to improve the performance of the CAD scheme based on the supervised learning method is developed.
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- Long-range terrain perception using convolutional neural networks51
A deep convolutional neural network model is integrated with a near-to-far learning strategy to improve the accuracy of terrain segmentation and make it more robust against wild environments.
- Reduction of false positives in computerized detection of lung nodules in chest radiographs using artificial neural networks, discriminant analysis, and a rule-based scheme51
A computer-aided diagnosis (CAD) scheme is being developed to identify image regions considered suspicious for lung nodules in chest radiographs to assist radiologists in making correct diagnoses using an artificial neural network, discriminant analysis, and a rule-based scheme.
- CM-YOLO: Typical Object Detection Method in Remote Sensing Cloud and Mist Scene Images50
A novel target detection method named CM-YOLO that integrates background suppression and semantic context mining, which can achieve accurate detection of targets under different cloud and mist conditions and achieves target feature enhancement.
- Query-Guided Prototype Evolution Network for Few-Shot Segmentation50
The Query-guided Prototype Evolution Network (QPENet) is presented, a new method that integrates query features into the generation process of foreground and background prototypes, thereby yielding customized prototypes attuned to specific queries.
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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 2026-10-12. 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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