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Found 10 papers, 6 papers with code
Uncertainty-aware Bridge based Mobile-Former Network for Event-based Pattern Recognition
The mainstream human activity recognition (HAR) algorithms are developed based on RGB cameras, which are easily influenced by low-quality images (e. g., low illumination, motion blur).
MAG-Edit: Localized Image Editing in Complex Scenarios via Mask-Based Attention-Adjusted Guidance
Recent diffusion-based image editing approaches have exhibited impressive editing capabilities in images with simple compositions.
Semantic-Aware Frame-Event Fusion based Pattern Recognition via Large Vision-Language Models
Current methods typically employ backbone networks to individually extract the features of RGB frames and event streams, and subsequently fuse these features for pattern recognition.
VcT: Visual change Transformer for Remote Sensing Image Change Detection
Then, each pixel of feature map is regarded as a graph node and the graph neural network is proposed to model the structured information for coarse change map prediction.
Learning Bottleneck Transformer for Event Image-Voxel Feature Fusion based Classification
Additionally, a bottleneck Transformer is introduced to facilitate the fusion of the dual-stream information.
The Future of ChatGPT-enabled Labor Market: A Preliminary Study in China
To this end, in this paper, we introduce a preliminary data-driven study on the future of ChatGPT-enabled labor market from the view of Human-AI Symbiosis instead of Human-AI Confrontation.
IRNet: Iterative Refinement Network for Noisy Partial Label Learning
In this paper, we relax this assumption and focus on a more general problem, noisy PLL, where the ground-truth label may not exist in the candidate set.
Deep Learning-based Massive MIMO CSI Acquisition for 5G Evolution and 6G
Considering its large impact on air-interface design, it will be a candidate technology for 6th generation (6G) networks, in which an air interface designed by artificial intelligence can be used.
GCNet: Graph Completion Network for Incomplete Multimodal Learning in Conversation
To this end, we propose a novel framework for incomplete multimodal learning in conversations, called "Graph Complete Network (GCNet)", filling the gap of existing works.
Deep Deformation Detail Synthesis for Thin Shell Models
Moreover, as such methods only add details, they require coarse meshes to be close to fine meshes, which can be either impossible, or require unrealistic constraints when generating fine meshes.
