MICPL: Motion-Inspired Cross-Pattern Learning for Small-Object Detection in Satellite Videos.

Temporal Adaptive Learned Surveillance Video Compression.

Semi-Supervised Multiview Prototype Learning With Motion Reconstruction for Moving Infrared Small Target Detection.

Spatial-temporal-channel collaborative feature learning with transformers for infrared small target detection.

WSOE: Weakly Supervised Outlier Exposure for Object-level Out-of-distribution detection.

Moving infrared dim and small target detection by mixed spatio-temporal encoding.

Illumination Distribution-Aware Thermal Pedestrian Detection.

Multi-Reference-Based Cross-Scale Feature Fusion for Compressed Video Super Resolution.

Multi-Level Alignments for Compressed Video Super-Resolution.

Stable Viewport-Based Unsupervised Compressed 360° Video Quality Enhancement.

Pseudo-Bag Mixup Augmentation for Multiple Instance Learning-Based Whole Slide Image Classification.

Multi-scale LBP fusion with the contours from deep CellNNs for texture classification.

AdvMIL: Adversarial multiple instance learning for the survival analysis on whole-slide images.

Shared Coupling-Bridge Scheme for Weakly Supervised Local Feature Learning.

Triple-Domain Feature Learning With Frequency-Aware Memory Enhancement for Moving Infrared Small Target Detection.

SSTNet: Sliced Spatio-Temporal Network With Cross-Slice ConvLSTM for Moving Infrared Dim-Small Target Detection.

Toward Dense Moving Infrared Small Target Detection: New Datasets and Baseline.

TMP: Temporal Motion Perception with spatial auxiliary enhancement for moving Infrared dim-small target detection.

Spatio-temporal fusion with motion masks for the moving small target detection from remote-sensing videos.

LGU-SLAM: Learnable Gaussian Uncertainty Matching with Deformable Correlation Sampling for Deep Visual SLAM.

Interpretable Vision-Language Survival Analysis with Ordinal Inductive Bias for Computational Pathology.

Deformable Feature Alignment and Refinement for Moving Infrared Dim-small Target Detection.

ProDiv: Prototype-driven consistent pseudo-bag division for whole-slide image classification.

DSCA: A dual-stream network with cross-attention on whole-slide image pyramids for cancer prognosis.

GraphLSurv: A scalable survival prediction network with adaptive and sparse structure learning for histopathological whole-slide images.

ProtoDiv: Prototype-guided Division of Consistent Pseudo-bags for Whole-slide Image Classification.

Prototype-Based Multisource Domain Adaptation.

Speech Emotion Recognition on Small Sample Learning by Hybrid WGAN-LSTM Networks.

Geometry Attention Transformer with position-aware LSTMs for image captioning.

Shared Coupling-bridge for Weakly Supervised Local Feature Learning.

Dual-Stream Transformer with Cross-Attention on Whole-Slide Image Pyramids for Cancer Prognosis.

Multi-view subspace clustering via partition fusion.

Recurrent convolutions of binary-constraint Cellular Neural Network for texture recognition.

Training-Based Gradient LBP Feature Models for Multiresolution Texture Classification.

Median local ternary patterns optimized with rotation-invariant uniform-three mapping for noisy texture classification.

Computing k shortest paths from a source node to each other node.

Facial expression recognition from image sequences using twofold random forest classifier.

Computing k shortest paths using modified pulse-coupled neural network.

One-dimensional pairwise CNN for the global alignment of two DNA sequences.

Parameter selection of support vector machines and genetic algorithm based on change area search.

Feature selection and parameter optimization for support vector machines: A new approach based on genetic algorithm with feature chromosomes.

Constrained ZIP code segmentation by a PCNN-based thinning algorithm.

Fingerprint orientation field estimation using ridge projection.

An improved pulse coupled neural network for image processing.

A mixed noise image filtering method using weighted-linking PCNNs.

Binary Fingerprint Image Thinning Using Template-Based PCNNs.

Binary Image Thinning Using Autowaves Generated by PCNN.