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Found 4 papers, 3 papers with code
DreamLLM: Synergistic Multimodal Comprehension and Creation
This paper presents DreamLLM, a learning framework that first achieves versatile Multimodal Large Language Models (MLLMs) empowered with frequently overlooked synergy between multimodal comprehension and creation.
Ranked #1 on
Visual Question Answering
on MMBench
(GPT-3.5 score metric)
Reversible Column Networks
Such architectural scheme attributes RevCol very different behavior from conventional networks: during forward propagation, features in RevCol are learned to be gradually disentangled when passing through each column, whose total information is maintained rather than compressed or discarded as other network does.
Ranked #8 on
Semantic Segmentation
on ADE20K
(using extra training data)
Understanding Masked Image Modeling via Learning Occlusion Invariant Feature
Recently, Masked Image Modeling (MIM) achieves great success in self-supervised visual recognition.
Revisiting the Critical Factors of Augmentation-Invariant Representation Learning
We focus on better understanding the critical factors of augmentation-invariant representation learning.
