Search papers, labs, and topics across Lattice.
Affiliation:
4
0
4
Image editors can now learn to self-correct their edits using only their own outputs, achieving significant gains in edit realization and content preservation without human feedback.
Visual blind spots can be transformed into powerful self-supervision signals, leading to substantial performance gains in multimodal language models.
Visual under-conditioning in LMMs can be overcome by directly regularizing visual attention, leading to remarkable improvements in multimodal understanding tasks.
Unsupervised training can elevate multimodal models, achieving a 3.5% boost in understanding metrics and a notable increase in image generation fidelity without human intervention.
