3 citations · 4 across the 9 of their papers we have counts for
4 papers · 1 filter
ExPO-HM: Learning to Explain-then-Detect for Hateful Meme Detection
Jingbiao Mei, Mingsheng Sun, Jinghong Chen +4
Hateful memes have emerged as a particularly challenging form of online abuse, motivating the development of automated detection systems. Most prior approaches rely on direct detec…
Rethinking Few Shot CLIP Benchmarks: A Critical Analysis in the Inductive Setting
Alexey Kravets, Da Chen, Vinay P. Namboodiri
CLIP is a foundational model with transferable classification performance in the few-shot setting. Several methods have shown improved performance of CLIP using few-shot examples.…
MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention
MiniMax, :, Aili Chen +125
We introduce MiniMax-M1, the world's first open-weight, large-scale hybrid-attention reasoning model. MiniMax-M1 is powered by a hybrid Mixture-of-Experts (MoE) architecture combin…
MiniMax-01: Scaling Foundation Models with Lightning Attention
MiniMax, Aonian Li, Bangwei Gong +87
We introduce MiniMax-01 series, including MiniMax-Text-01 and MiniMax-VL-01, which are comparable to top-tier models while offering superior capabilities in processing longer conte…