MIF integrates appearance, spatial, and geometry fields with discrepancy detection to raise humanoid relocation success from 12% to 94% in dynamic offices while cutting memory use by 91.4%.
Active perception.Proceedings of the IEEE, 76(8):966–1005, 1988
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
VAP is a training-free active-perception method that improves zero-shot long-form video QA performance and frame efficiency up to 5.6x in VLMs by selecting keyframes that differ from priors generated by a text-conditioned video model.
citing papers explorer
-
Learning to Evolve: Multi-modal Interactive Fields for Robust Humanoid Navigation in Dynamic Environments
MIF integrates appearance, spatial, and geometry fields with discrepancy detection to raise humanoid relocation success from 12% to 94% in dynamic offices while cutting memory use by 91.4%.
-
Video Active Perception: Effective Inference-Time Long-Form Video Understanding with Vision-Language Models
VAP is a training-free active-perception method that improves zero-shot long-form video QA performance and frame efficiency up to 5.6x in VLMs by selecting keyframes that differ from priors generated by a text-conditioned video model.