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Found 13 papers, 5 papers with code
Spiral Scanning and Self-Supervised Image Reconstruction Enable Ultra-Sparse Sampling Multispectral Photoacoustic Tomography
To address this challenge, we propose an ultra-sparse spiral sampling strategy for multispectral PAT, which we named U3S-PAT.
FineFake: A Knowledge-Enriched Dataset for Fine-Grained Multi-Domain Fake News Detecction
Existing benchmarks for fake news detection have significantly contributed to the advancement of models in assessing the authenticity of news content.
LangGPT: Rethinking Structured Reusable Prompt Design Framework for LLMs from the Programming Language
We have built a community on LangGPT to facilitate the tuition and sharing of prompt design.
MixMobileNet: A Mixed Mobile Network for Edge Vision Applications
For global, we propose the global-feature aggregation encoder (GFAE), which employs a pooling strategy and computes the covariance matrix between channels instead of the spatial dimensions, changing the computational complexity from quadratic to linear, and this accelerates the inference of the model.
Enhancing Cognitive Diagnosis using Un-interacted Exercises: A Collaboration-aware Mixed Sampling Approach
Finally, we demonstrate the effectiveness and interpretability of our framework through comprehensive experiments on real-world datasets.
Deep learning acceleration of iterative model-based light fluence correction for photoacoustic tomography
Photoacoustic tomography (PAT) is a promising imaging technique that can visualize the distribution of chromophores within biological tissue.
Hi-ResNet: A High-Resolution Remote Sensing Network for Semantic Segmentation
In this work, we solve the above-mentioned problems by proposing a High-resolution remote sensing network (Hi-ResNet) with efficient network structure designs, which consists of a funnel module, a multi-branch module with stacks of information aggregation (IA) blocks, and a feature refinement module, sequentially, and Class-agnostic Edge Aware (CEA) loss.
Ranked #7 on
Semantic Segmentation
on LoveDA
CISum: Learning Cross-modality Interaction to Enhance Multimodal Semantic Coverage for Multimodal Summarization
Then, the visual description and text content are fused to generate the textual summary to capture the semantics of the multimodal content, and the most relevant image is selected as the visual summary.
Boosting Single Image Super-Resolution via Partial Channel Shifting
Specifically, it is inspired by the temporal shifting in video understanding and displaces part of the channels along the spatial dimensions, thus allowing the effective receptive field to be amplified and the feature diversity to be augmented at almost zero cost.
Hierarchical Cross-Modality Semantic Correlation Learning Model for Multimodal Summarization
In this paper, we propose a hierarchical cross-modality semantic correlation learning model (HCSCL) to learn the intra- and inter-modal correlation existing in the multimodal data.
Group-based Interleaved Pipeline Parallelism for Large-scale DNN Training
The recent trend of using large-scale deep neural networks (DNN) to boost performance has propelled the development of the parallel pipelining technique for efficient DNN training, which has resulted in the development of several prominent pipelines such as GPipe, PipeDream, and PipeDream-2BW.
Matching Distributions between Model and Data: Cross-domain Knowledge Distillation for Unsupervised Domain Adaptation
Unsupervised Domain Adaptation (UDA) aims to transfer the knowledge of source domain to the unlabeled target domain.
K-XLNet: A General Method for Combining Explicit Knowledge with Language Model Pretraining
Though pre-trained language models such as Bert and XLNet, have rapidly advanced the state-of-the-art on many NLP tasks, they implicit semantics only relying on surface information between words in corpus.
