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Found 12 papers, 6 papers with code
Diffusion Attack: Leveraging Stable Diffusion for Naturalistic Image Attacking
In Virtual Reality (VR), adversarial attack remains a significant security threat.
A User-Friendly Framework for Generating Model-Preferred Prompts in Text-to-Image Synthesis
To bridge the distribution gap between user input behavior and model training datasets, we first construct a novel Coarse-Fine Granularity Prompts dataset (CFP) and propose a novel User-Friendly Fine-Grained Text Generation framework (UF-FGTG) for automated prompt optimization.
DL3DV-10K: A Large-Scale Scene Dataset for Deep Learning-based 3D Vision
We have witnessed significant progress in deep learning-based 3D vision, ranging from neural radiance field (NeRF) based 3D representation learning to applications in novel view synthesis (NVS).
Flames: Benchmarking Value Alignment of LLMs in Chinese
The widespread adoption of large language models (LLMs) across various regions underscores the urgent need to evaluate their alignment with human values.
Plug-and-Play Feature Generation for Few-Shot Medical Image Classification
Finally, the generated features and prototypes are together to train a more generalized classifier.
RankDNN: Learning to Rank for Few-shot Learning
This paper introduces a new few-shot learning pipeline that casts relevance ranking for image retrieval as binary ranking relation classification.
Weakly Supervised Video Salient Object Detection via Point Supervision
Several works attempt to use scribble annotations to mitigate this problem, but point supervision as a more labor-saving annotation method (even the most labor-saving method among manual annotation methods for dense prediction), has not been explored.
Weakly-Supervised Salient Object Detection Using Point Supervision
Then we develop a transformer-based point-supervised saliency detection model to produce the first round of saliency maps.
LungAttn: advanced lung sound classification using attention mechanism with dual TQWT and triple STFT spectrogram
We propose a deep learning architecture, LungAttn, which incorporates augmented attention convolution into ResNet block to improve the classification accuracy of lung sound.
Ranked #17 on
Audio Classification
on ICBHI Respiratory Sound Database
SANST: A Self-Attentive Network for Next Point-of-Interest Recommendation
To address this limitation, in this paper, we propose a model named SANST that incorporates spatio-temporal patterns of user check-ins into self-attentive networks.
An Empirical Study towards Characterizing Deep Learning Development and Deployment across Different Frameworks and Platforms
However, the differences in architecture designs and implementations of existing frameworks and platforms bring new challenges for DL software development and deployment.
An Orchestrated Empirical Study on Deep Learning Frameworks and Platforms
Up to the present, it still lacks a comprehensive study on how current diverse DL frameworks and platforms influence the DL software development process.
