An Empirical Study Into What Matters for Calibrating Vision-Language
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An Empirical Study Into What Matters for Calibrating Vision-Language Models

12 February 2024

Tom Gedeon

Papers citing "An Empirical Study Into What Matters for Calibrating Vision-Language Models"

9 / 9 papers shown

Title
ChatGPT-Powered Hierarchical Comparisons for Image Classification Zhiyuan Ren Yiyang Su Xiaoming Liu VLM 40 21 0 01 Nov 2023
BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models Junnan Li Dongxu Li Silvio Savarese Steven C. H. Hoi VLM MLLM 244 4,186 0 30 Jan 2023
What does a platypus look like? Generating customized prompts for zero-shot image classification Sarah M Pratt Ian Covert Rosanne Liu Ali Farhadi VLM 116 211 0 07 Sep 2022
BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation Junnan Li Dongxu Li Caiming Xiong S. Hoi MLLM BDL VLM CLIP 382 4,010 0 28 Jan 2022
Learning to Prompt for Vision-Language Models Kaiyang Zhou Jingkang Yang Chen Change Loy Ziwei Liu VPVLM CLIP VLM 322 2,249 0 02 Sep 2021
Open-vocabulary Object Detection via Vision and Language Knowledge Distillation Xiuye Gu Tsung-Yi Lin Weicheng Kuo Yin Cui VLM ObjD 220 897 0 28 Apr 2021
ImageNet-21K Pretraining for the Masses T. Ridnik Emanuel Ben-Baruch Asaf Noy Lihi Zelnik-Manor SSeg VLM CLIP 154 676 0 22 Apr 2021
Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision Chao Jia Yinfei Yang Ye Xia Yi-Ting Chen Zarana Parekh Hieu H. Pham Quoc V. Le Yun-hsuan Sung Zhen Li Tom Duerig VLM CLIP 293 3,683 0 11 Feb 2021
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 268 5,635 0 05 Dec 2016