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Found 26 papers, 7 papers with code
Brain Foundation Models: A Survey on Advancements in Neural Signal Processing and Brain Discovery
Brain foundation models (BFMs) have emerged as a transformative paradigm in computational neuroscience, offering a revolutionary framework for processing diverse neural signals across different brain-related tasks.
Col-OLHTR: A Novel Framework for Multimodal Online Handwritten Text Recognition
Current approaches usually treat OLHTR as a sequence recognition task, employing either a single trajectory or image encoder, or multi-stream encoders, combined with a CTC or attention-based recognition decoder.
SelectiveFinetuning: Enhancing Transfer Learning in Sleep Staging through Selective Domain Alignment
By finetuning the model with selective source data, our SelectiveFinetuning enhances the model's performance on target domain that exhibits domain shifts compared to the data used for training.
Towards Wireless-Native Big AI Model: Insights into Its Ambitions, Peculiarities and Methodologies
Researches on leveraging big artificial intelligence model (BAIM) technology to drive the intelligent evolution of wireless networks are emerging.
BiT-MamSleep: Bidirectional Temporal Mamba for EEG Sleep Staging
In this paper, we address the challenges in automatic sleep stage classification, particularly the high computational cost, inadequate modeling of bidirectional temporal dependencies, and class imbalance issues faced by Transformer-based models.
DAWN: Dynamic Frame Avatar with Non-autoregressive Diffusion Framework for Talking Head Video Generation
To address these challenges, we present DAWN (Dynamic frame Avatar With Non-autoregressive diffusion), a framework that enables all-at-once generation of dynamic-length video sequences.
See then Tell: Enhancing Key Information Extraction with Vision Grounding
Positioned at the outset of the answer text, the <see> token allows the model to first see--observing the regions of the image related to the input question--and then tell--providing articulated textual responses.
A Comprehensive Survey on EEG-Based Emotion Recognition: A Graph-Based Perspective
In this paper, we present a comprehensive survey of these studies, delivering a systematic review of graph-related methods in this field from a methodological perspective.
NAMER: Non-Autoregressive Modeling for Handwritten Mathematical Expression Recognition
Recently, Handwritten Mathematical Expression Recognition (HMER) has gained considerable attention in pattern recognition for its diverse applications in document understanding.
SRFUND: A Multi-Granularity Hierarchical Structure Reconstruction Benchmark in Form Understanding
Accurately identifying and organizing textual content is crucial for the automation of document processing in the field of form understanding.
SEMv3: A Fast and Robust Approach to Table Separation Line Detection
The `"split-and-merge" paradigm is a pivotal approach to parse table structure, where the table separation line detection is crucial.
EEG-Deformer: A Dense Convolutional Transformer for Brain-computer Interfaces
Effectively learning the temporal dynamics in electroencephalogram (EEG) signals is challenging yet essential for decoding brain activities using brain-computer interfaces (BCIs).
Graph Neural Networks in EEG-based Emotion Recognition: A Survey
Besides, there is neither a comprehensive review nor guidance for constructing GNNs in EEG-based emotion recognition.
Hi-SAM: Marrying Segment Anything Model for Hierarchical Text Segmentation
We use this TS model to iteratively generate the pixel-level text labels in a semi-automatical manner, unifying labels across the four text hierarchies in the HierText dataset.
Ranked #1 on
Hierarchical Text Segmentation
on HierText
Hierarchical Text Segmentation
parameter-efficient fine-tuning
+2
Bidirectional Trained Tree-Structured Decoder for Handwritten Mathematical Expression Recognition
The Handwritten Mathematical Expression Recognition (HMER) task is a critical branch in the field of OCR.
scBeacon: single-cell biomarker extraction via identifying paired cell clusters across biological conditions with contrastive siamese networks
Despite the breakthroughs in biomarker discovery facilitated by differential gene analysis, challenges remain, particularly at the single-cell level.
Multi-stream Cell Segmentation with Low-level Cues for Multi-modality Images
Afterward, we train a separate segmentation model for each category using the images in the corresponding category.
EENED: End-to-End Neural Epilepsy Detection based on Convolutional Transformer
Recently Transformer and Convolution neural network (CNN) based models have shown promising results in EEG signal processing.
EEG-based Sleep Staging with Hybrid Attention
Specifically, we propose a well-designed spatio-temporal attention mechanism to adaptively assign weights to inter-channels and intra-channel EEG segments based on the spatio-temporal relationship of the brain during different sleep stages.
An EEG Channel Selection Framework for Driver Drowsiness Detection via Interpretability Guidance
However, the raw EEG data is inherently noisy and redundant, which is neglected by existing works that just use single-channel EEG data or full-head channel EEG data for model training, resulting in limited performance of driver drowsiness detection.
Interpretable and Robust AI in EEG Systems: A Survey
In this paper, we present the first comprehensive survey and summarize the interpretable and robust AI techniques for EEG systems.
Vision-Language Adaptive Mutual Decoder for OOV-STR
However, in real-world scenarios, out-of-vocabulary (OOV) words are of great importance and SOTA recognition models usually perform poorly on OOV settings.
Video Forgery Detection Using Multiple Cues on Fusion of EfficientNet and Swin Transformer
The first is that capturing the general clue of artifacts is difficult.
Learning without Knowing: Unobserved Context in Continuous Transfer Reinforcement Learning
We assume that this context is not accessible to a learner agent who can only observe the expert data.
Brain Tumor Segmentation Network Using Attention-based Fusion and Spatial Relationship Constraint
Considering that multi-modal MR images can reflect different tumor biological properties, we develop a novel multi-modal tumor segmentation network (MMTSN) to robustly segment brain tumors based on multi-modal MR images.
Multi-Modality Pathology Segmentation Framework: Application to Cardiac Magnetic Resonance Images
The PRSN is designed to segment pathological region based on the result of ASSN, in which a fusion block based on channel attention is proposed to better aggregate multi-modality information from multi-modality CMR images.
