Shravan Venkatraman

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.

Self-supervised learning can achieve state-of-the-art video tracking accuracy without any labeled data or additional inference costs.

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.

Uncertainty in camera poses can be learned and leveraged to dramatically improve neural surface reconstructions, especially in complex scenes.