Panorama-Language Models with a sparse attention module and PanoVQA dataset deliver superior holistic reasoning on 360° adverse omni-scenes compared to stitched pinhole views.
Vizwiz grand challenge: Answering visual questions from blind people
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
A 0.5B student VLM distills from a 3B teacher using visual-switch distillation and DBiLD loss to gain 3.6 points on average across 10 multimodal benchmarks without architecture changes.
citing papers explorer
-
More than the Sum: Panorama-Language Models for Adverse Omni-Scenes
Panorama-Language Models with a sparse attention module and PanoVQA dataset deliver superior holistic reasoning on 360° adverse omni-scenes compared to stitched pinhole views.
-
Switch-KD: Visual-Switch Knowledge Distillation for Vision-Language Models
A 0.5B student VLM distills from a 3B teacher using visual-switch distillation and DBiLD loss to gain 3.6 points on average across 10 multimodal benchmarks without architecture changes.