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Found 16 papers, 7 papers with code
Towards Accurate Binary Spiking Neural Networks: Learning with Adaptive Gradient Modulation Mechanism
Binary Spiking Neural Networks (BSNNs) inherit the eventdriven paradigm of SNNs, while also adopting the reduced storage burden of binarization techniques.
Milmer: a Framework for Multiple Instance Learning based Multimodal Emotion Recognition
Ablation studies further validate the contributions of each module, highlighting the significance of advanced feature extraction and fusion strategies in enhancing emotion recognition performance.
Binary Event-Driven Spiking Transformer
In this paper, we integrate binarization techniques into Transformer-based SNNs and propose the Binary Event-Driven Spiking Transformer, i. e. BESTformer.
Unsupervised Modality Adaptation with Text-to-Image Diffusion Models for Semantic Segmentation
To address this issue, we propose Modality Adaptation with text-to-image Diffusion Models (MADM) for semantic segmentation task which utilizes text-to-image diffusion models pre-trained on extensive image-text pairs to enhance the model's cross-modality capabilities.
Towards Natural Image Matting in the Wild via Real-Scenario Prior
For training objectives, the proposed regularization and trimap loss aim to retain the prior from the pre-trained model and push the matting logits extracted from the mask decoder to contain trimap-based semantic information.
Vision-fused Attack: Advancing Aggressive and Stealthy Adversarial Text against Neural Machine Translation
However, existing studies on adversarial attacks are insufficient in both attacking ability and human imperceptibility due to their sole focus on the scope of language.
Low Saturation Confidence Distribution-based Test-Time Adaptation for Cross-Domain Remote Sensing Image Classification
By effectively combining these losses, LSCD-TTA enables rapid and accurate adaptation to the target domain for RS image classification.
Q-SNNs: Quantized Spiking Neural Networks
Brain-inspired Spiking Neural Networks (SNNs) leverage sparse spikes to represent information and process them in an asynchronous event-driven manner, offering an energy-efficient paradigm for the next generation of machine intelligence.
GraphRARE: Reinforcement Learning Enhanced Graph Neural Network with Relative Entropy
To address this limitation, this paper presents GraphRARE, a general framework built upon node relative entropy and deep reinforcement learning, to strengthen the expressive capability of GNNs.
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Can LSH (Locality-Sensitive Hashing) Be Replaced by Neural Network?
With the rapid development of GPU (Graphics Processing Unit) technologies and neural networks, we can explore more appropriate data structures and algorithms.
MixBCT: Towards Self-Adapting Backward-Compatible Training
Instance-based methods like L2 regression take into account the distribution of old features but impose strong constraints on the performance of the new model itself.
CLGT: A Graph Transformer for Student Performance Prediction in Collaborative Learning
Based on this student interaction graph, we present an extended graph transformer framework for collaborative learning (CLGT) for evaluating and predicting the performance of students.
Key frames assisted hybrid encoding for photorealistic compressive video sensing
Snapshot compressive imaging (SCI) encodes high-speed scene video into a snapshot measurement and then computationally makes reconstructions, allowing for efficient high-dimensional data acquisition.
Visualizing the Finer Cluster Structure of Large-Scale and High-Dimensional Data
Dimension reduction and visualization of high-dimensional data have become very important research topics because of the rapid growth of large databases in data science.
Adaptive Generation of Phantom Limbs Using Visible Hierarchical Autoencoders
This paper proposed a hierarchical visible autoencoder in the adaptive phantom limbs generation according to the kinetic behavior of functional body-parts, which are measured by heterogeneous kinetic sensors.
