most citedChatGPT-Crawler: Find out if ChatGPT really knows what it's talking about

2 citations · 5 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2023

Artificial-Spiking Hierarchical Networks for Vision-Language Representation Learning

Yeming Chen, Siyu Zhang, Yaoru Sun +2

With the success of self-supervised learning, multimodal foundation models have rapidly adapted a wide range of downstream tasks driven by vision and language (VL) pretraining. Sta…

cs.CV20232 cited

S3IM: Stochastic Structural SIMilarity and Its Unreasonable Effectiveness for Neural Fields

Zeke Xie, Xindi Yang, Yujie Yang +5

Recently, Neural Radiance Field (NeRF) has shown great success in rendering novel-view images of a given scene by learning an implicit representation with only posed RGB images. Ne…

cs.CL20232 cited

ChatGPT-Crawler: Find out if ChatGPT really knows what it's talking about

Aman Rangapur, Haoran Wang

Large language models have gained considerable interest for their impressive performance on various tasks. Among these models, ChatGPT developed by OpenAI has become extremely popu…

cs.CV2022

Boosting Video-Text Retrieval with Explicit High-Level Semantics

Haoran Wang, Di Xu, Dongliang He +4

Video-text retrieval (VTR) is an attractive yet challenging task for multi-modal understanding, which aims to search for relevant video (text) given a query (video). Existing metho…

cs.CV20221 cited

NSNet: Non-saliency Suppression Sampler for Efficient Video Recognition

Boyang Xia, Wenhao Wu, Haoran Wang +5

It is challenging for artificial intelligence systems to achieve accurate video recognition under the scenario of low computation costs. Adaptive inference based efficient video re…