Summary of Lost in Unet: Improving Infrared Small Target Detection by Underappreciated Local Features, By Wuzhou Quan et al.
Lost in UNet: Improving Infrared Small Target Detection by Underappreciated Local Featuresby Wuzhou Quan, Wei…
Summary of Federating to Grow Transformers with Constrained Resources Without Model Sharing, by Shikun Shen et al.
Federating to Grow Transformers with Constrained Resources without Model Sharingby Shikun Shen, Yifei Zou, Yuan…
Summary of Evtexture: Event-driven Texture Enhancement For Video Super-resolution, by Dachun Kai et al.
EvTexture: Event-driven Texture Enhancement for Video Super-Resolutionby Dachun Kai, Jiayao Lu, Yueyi Zhang, Xiaoyan SunFirst…
Summary of Latent Intuitive Physics: Learning to Transfer Hidden Physics From a 3d Video, by Xiangming Zhu et al.
Latent Intuitive Physics: Learning to Transfer Hidden Physics from A 3D Videoby Xiangming Zhu, Huayu…
Summary of Is It Good Data For Multilingual Instruction Tuning or Just Bad Multilingual Evaluation For Large Language Models?, by Pinzhen Chen et al.
Is It Good Data for Multilingual Instruction Tuning or Just Bad Multilingual Evaluation for Large…
Summary of From Rags to Rich Parameters: Probing How Language Models Utilize External Knowledge Over Parametric Information For Factual Queries, by Hitesh Wadhwa et al.
From RAGs to rich parameters: Probing how language models utilize external knowledge over parametric information…
Summary of Shield: Evaluation and Defense Strategies For Copyright Compliance in Llm Text Generation, by Xiaoze Liu et al.
SHIELD: Evaluation and Defense Strategies for Copyright Compliance in LLM Text Generationby Xiaoze Liu, Ting…
Summary of Via: Unified Spatiotemporal Video Adaptation Framework For Global and Local Video Editing, by Jing Gu et al.
VIA: Unified Spatiotemporal Video Adaptation Framework for Global and Local Video Editingby Jing Gu, Yuwei…
Summary of Detecting Errors Through Ensembling Prompts (deep): An End-to-end Llm Framework For Detecting Factual Errors, by Alex Chandler et al.
Detecting Errors through Ensembling Prompts (DEEP): An End-to-End LLM Framework for Detecting Factual Errorsby Alex…
Summary of Claudeslens: Uncertainty Quantification in Computer Vision Models, by Mohamad Al Shaar et al.
ClaudesLens: Uncertainty Quantification in Computer Vision Modelsby Mohamad Al Shaar, Nils Ekström, Gustav Gille, Reza…