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Found 10 papers, 4 papers with code
Prompt Customization for Continual Learning
In contrast to conventional methods that employ hard prompt selection, PGM assigns different coefficients to prompts from a fixed-sized pool of prompts and generates tailored prompts.
CricaVPR: Cross-image Correlation-aware Representation Learning for Visual Place Recognition
Over the past decade, most methods in visual place recognition (VPR) have used neural networks to produce feature representations.
Deep Homography Estimation for Visual Place Recognition
Moreover, we design a re-projection error of inliers loss to train the DHE network without additional homography labels, which can also be jointly trained with the backbone network to help it extract the features that are more suitable for local matching.
Enhancing Emotional Generation Capability of Large Language Models via Emotional Chain-of-Thought
In this paper, we propose the Emotional Chain-of-Thought (ECoT), a plug-and-play prompting method that enhances the performance of LLMs on various emotional generation tasks by aligning with human emotional intelligence guidelines.
Benign Shortcut for Debiasing: Fair Visual Recognition via Intervention with Shortcut Features
To this end, we propose \emph{Shortcut Debiasing}, to first transfer the target task's learning of bias attributes from bias features to shortcut features, and then employ causal intervention to eliminate shortcut features during inference.
Strip-MLP: Efficient Token Interaction for Vision MLP
Finally, based on the Strip MLP layer, we propose a novel \textbf{L}ocal \textbf{S}trip \textbf{M}ixing \textbf{M}odule (LSMM) to boost the token interaction power in the local region.
Relate auditory speech to EEG by shallow-deep attention-based network
Electroencephalography (EEG) plays a vital role in detecting how brain responses to different stimulus.
Positional-Spectral-Temporal Attention in 3D Convolutional Neural Networks for EEG Emotion Recognition
The proposed module, denoted by PST-Attention, consists of Positional, Spectral and Temporal Attention modules to explore more discriminative EEG features.
Efficient Spatialtemporal Context Modeling for Action Recognition
Inspired from 2D criss-cross attention used in segmentation task, we propose a recurrent 3D criss-cross attention (RCCA-3D) module to model the dense long-range spatiotemporal contextual information in video for action recognition.
Efficient Convolutional Auto-Encoding via Random Convexification and Frequency-Domain Minimization
The omnipresence of deep learning architectures such as deep convolutional neural networks (CNN)s is fueled by the synergistic combination of ever-increasing labeled datasets and specialized hardware.
