no code implementations • 31 Jan 2024 • Qirui Jiao, Daoyuan Chen, Yilun Huang, Yaliang Li, Ying Shen
Despite the impressive capabilities of Multimodal Large Language Models (MLLMs) in integrating text and image modalities, challenges remain in accurately interpreting detailed visual elements.
Ranked #41 on Visual Question Answering on MM-Vet
1 code implementation • 5 Sep 2023 • Daoyuan Chen, Yilun Huang, Zhijian Ma, Hesen Chen, Xuchen Pan, Ce Ge, Dawei Gao, Yuexiang Xie, Zhaoyang Liu, Jinyang Gao, Yaliang Li, Bolin Ding, Jingren Zhou
A data recipe is a mixture of data from different sources for training LLMs, which plays a vital role in LLMs' performance.
1 code implementation • CVPR 2023 • Xuan Shen, Yaohua Wang, Ming Lin, Yilun Huang, Hao Tang, Xiuyu Sun, Yanzhi Wang
To this end, a novel framework termed Mathematical Architecture Design for Deep CNN (DeepMAD) is proposed to design high-performance CNN models in a principled way.
Ranked #1 on Neural Architecture Search on ImageNet
no code implementations • 14 Feb 2023 • Qi Zhang, Zijian Yang, Yilun Huang, Ze Chen, Zijian Cai, Kangxu Wang, Jiewen Zheng, Jiarong He, Jin Gao
In this paper, we present our solution to the Multilingual Information Retrieval Across a Continuum of Languages (MIRACL) challenge of WSDM CUP 2023\footnote{https://project-miracl. github. io/}.
3 code implementations • 23 Nov 2022 • Xianzhe Xu, Yiqi Jiang, Weihua Chen, Yilun Huang, Yuan Zhang, Xiuyu Sun
Based on these new techs, we build a suite of models at various scales to meet the needs of different scenarios.
Ranked #39 on Real-Time Object Detection on MS COCO
no code implementations • 5 Aug 2022 • Qi Zhang, Zijian Yang, Yilun Huang, Ze Chen, Zijian Cai, Kangxu Wang, Jiewen Zheng, Jiarong He, Jin Gao
Our models are all trained with cross-entropy loss to classify the query-product pairs into ESCI 4 categories at first, and then we use weighted sum with the 4-class probabilities to get the score for ranking.
1 code implementation • 2 Mar 2022 • Qi Zhang, Zijian Yang, Yilun Huang, Jiarong He, Lixiang Wang
The Cross-Market Recommendation task of WSDM CUP 2022 is about finding solutions to improve individual recommendation systems in resource-scarce target markets by leveraging data from similar high-resource source markets.