#1 · Paper · Jul 17, 2024
E5-V: Universal Embeddings with Multimodal Large Language Models
Ting Jiang, Minghui Song, Zihan Zhang, Haizhen Huang, Weiwei Deng, Feng Sun, Qi Zhang, Deqing Wang, Fuzhen Zhuang · arxiv.org
Best Content for Machine Learning in July 2024
Track this topic after sign-in.
Query conditions: topic=machine-learning, and publish_at in 202407
Top Picks
#2 · Paper · Jul 27, 2024
LLMGA: Multimodal Large Language Model based Generation Assistant
Bin Xia, Shiyin Wang, Yingfan Tao, Yitong Wang, Jiaya Jia · arxiv.org
#3 · Paper · Jul 20, 2024
LongVLM: Efficient Long Video Understanding via Large Language Models
Yuetian Weng, Mingfei Han, Haoyu He, Xiaojun Chang, Bohan Zhuang · arxiv.org
#4 · Paper · Jul 3, 2024
InternLM-XComposer-2.5: A Versatile Large Vision Language Model Supporting Long-Contextual Input and Output
Pan Zhang, Xiaoyi Dong, Yuhang Zang, Yuhang Cao, Rui Qian, Lin Chen, Qipeng Guo, Haodong Duan, Bin Wang, Linke Ouyang, Songyang Zhang, Wenwei Zhang, Yining Li, Yang Gao, Peng Sun, Xinyue Zhang, Wei Li, Jingwen Li, Wenhai Wang, Hang Yan, Conghui He, Xingcheng Zhang, Kai Chen, Jifeng Dai, Yu Qiao, Dahua Lin, Jiaqi Wang · arxiv.org
#5 · Paper · Jul 1, 2024
Long Context Transfer from Language to Vision
Peiyuan Zhang, Kaichen Zhang, Bo Li, Guangtao Zeng, Jingkang Yang, Yuanhan Zhang, Ziyue Wang, Haoran Tan, Chunyuan Li, Ziwei Liu · arxiv.org
#6 · Paper · Jul 24, 2024
AesExpert: Towards Multi-modality Foundation Model for Image Aesthetics Perception
Yipo Huang, Xiangfei Sheng, Zhichao Yang, Quan Yuan, Zhichao Duan, Pengfei Chen, Leida Li, Weisi Lin, Guangming Shi · arxiv.org
#7 · Paper · Jul 16, 2024
OmniBind: Large-scale Omni Multimodal Representation via Binding Spaces
Zehan Wang, Ziang Zhang, Hang Zhang, Luping Liu, Rongjie Huang, Xize Cheng, Hengshuang Zhao, Zhou Zhao · arxiv.org
#8 · Paper · Jul 18, 2024
PixelLM: Pixel Reasoning with Large Multimodal Model
Zhongwei Ren, Zhicheng Huang, Yunchao Wei, Yao Zhao, Dongmei Fu, Jiashi Feng, Xiaojie Jin · arxiv.org
FAQ
How is this best Machine Learning content page ranked?
This page ranks Machine Learning content by topic match, content-type filter, checkout momentum, and freshness. The ranking is recalculated as new items and engagement signals arrive, so the top results stay practical for current workflows instead of remaining static or purely chronological.
What does the time scope in this URL mean?
The time suffix in this URL defines the publish-date window used for ranking. Year paths include items in that year, and YYYYMM paths include one calendar month. This makes comparisons cleaner when you want a focused snapshot rather than an all-time aggregate.
Is this list paid placement?
No. This ranking is editorial and signal-driven, not sponsored placement. Attendemia evaluates public metadata, source context, and usage momentum to rank candidates. Payment does not buy position, so readers can interpret the list as a curation surface rather than advertising inventory.