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Found 8 papers, 2 papers with code
STANKER: Stacking Network based on Level-grained Attention-masked BERT for Rumor Detection on Social Media
Rumor detection on social media puts pre-trained language models (LMs), such as BERT, and auxiliary features, such as comments, into use.
Prompting Large Language Models for Counterfactual Generation: An Empirical Study
Based on this framework, we 1) investigate the strengths and weaknesses of LLMs as the counterfactual generator, and 2) disclose the factors that affect LLMs when generating counterfactuals, including both the intrinsic properties of LLMs and prompt designing.
Movie Genre Classification by Language Augmentation and Shot Sampling
We evaluate our method on MovieNet and Condensed Movies datasets, achieving approximate 6-9% improvement in mean Average Precision (mAP) over the baselines.
ImageSubject: A Large-scale Dataset for Subject Detection
We present a new dataset with the goal of training models to understand the layout of the objects and the context of the image then to find the main subjects among them.
Fine-Grained Control of Artistic Styles in Image Generation
The style vectors are fed to the generator and discriminator to achieve fine-grained control.
Infrared target tracking based on proximal robust principal component analysis method
Infrared target tracking plays an important role in both civil and military fields.
l-Net: Reconstruct Hyperspectral Images From a Snapshot Measurement
We propose the l-net, which reconstructs hyperspectral images (e. g., with 24 spectral channels) from a single shot measurement.
Direct Shape Regression Networks for End-to-End Face Alignment
In this paper, we propose the direct shape regression network (DSRN) for end-to-end face alignment by jointly handling the aforementioned challenges in a unified framework.
Ranked #16 on
Face Alignment
on AFLW-19
