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Found 13 papers, 6 papers with code
Toward Robust Early Detection of Alzheimer's Disease via an Integrated Multimodal Learning Approach
Alzheimer's Disease (AD) is a complex neurodegenerative disorder marked by memory loss, executive dysfunction, and personality changes.
VIPeR: Visual Incremental Place Recognition with Adaptive Mining and Lifelong Learning
Then, to prevent catastrophic forgetting in lifelong learning, we draw inspiration from human memory systems and design a novel memory bank for our VIPeR.
Infrared Image Super-Resolution via Lightweight Information Split Network
A key innovation within this model is the introduction of the Lightweight Information Split Block (LISB) for deep feature extraction.
GEOcc: Geometrically Enhanced 3D Occupancy Network with Implicit-Explicit Depth Fusion and Contextual Self-Supervision
Nevertheless, current models still encounter two main challenges: modeling depth accurately in the 2D-3D view transformation stage, and overcoming the lack of generalizability issues due to sparse LiDAR supervision.
VQ-NeRV: A Vector Quantized Neural Representation for Videos
This block incorporates a codebook mechanism to discretize the network's shallow residual features and inter-frame residual information effectively.
LKFormer: Large Kernel Transformer for Infrared Image Super-Resolution
Given the broad application of infrared technology across diverse fields, there is an increasing emphasis on investigating super-resolution techniques for infrared images within the realm of deep learning.
Accurate Leukocyte Detection Based on Deformable-DETR and Multi-Level Feature Fusion for Aiding Diagnosis of Blood Diseases
To address these issues, this paper proposes an innovative method of leukocyte detection: the Multi-level Feature Fusion and Deformable Self-attention DETR (MFDS-DETR).
AEGIS-Net: Attention-guided Multi-Level Feature Aggregation for Indoor Place Recognition
We evaluate our AEGIS-Net on the ScanNetPR dataset and compare its performance with a pre-deep-learning feature-based method and five state-of-the-art deep-learning-based methods.
Deep Model Fusion: A Survey
Specifically, we categorize existing deep model fusion methods as four-fold: (1) "Mode connectivity", which connects the solutions in weight space via a path of non-increasing loss, in order to obtain better initialization for model fusion; (2) "Alignment" matches units between neural networks to create better conditions for fusion; (3) "Weight average", a classical model fusion method, averages the weights of multiple models to obtain more accurate results closer to the optimal solution; (4) "Ensemble learning" combines the outputs of diverse models, which is a foundational technique for improving the accuracy and robustness of the final model.
FedHiSyn: A Hierarchical Synchronous Federated Learning Framework for Resource and Data Heterogeneity
As the efficiency of training in the ring topology prefers devices with homogeneous resources, the classification based on the computing capacity mitigates the impact of straggler effects.
Recurrent Neural Network from Adder's Perspective: Carry-lookahead RNN
The recurrent network architecture is a widely used model in sequence modeling, but its serial dependency hinders the computation parallelization, which makes the operation inefficient.
Cascade Attentive Dropout for Weakly Supervised Object Detection
Weakly supervised object detection (WSOD) aims to classify and locate objects with only image-level supervision.
Image-based reconstruction for the impact problems by using DPNNs
Therefore, in this study, an improved ReConNN method is proposed to address the mentioned weaknesses.
