A self-supervised method pretrains an encoder on eight PSP images per view to learn generalizable subsurface scattering representations that transfer to relighting and dense footprint reconstruction on unseen complex objects.
and Lin, Dahua , month = jun, year =
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
EVENT5Ws is a new large-scale, manually verified open-domain event extraction dataset that benchmarks LLMs and demonstrates cross-context generalization.
MODEE is a multimodal system that integrates graphs with LLM embeddings to outperform prior open-domain event extraction methods on large datasets.
citing papers explorer
-
From Phase to Phenomenon: Self-Supervised Learning of Subsurface Scattering with Minimal Phase-shift Inputs
A self-supervised method pretrains an encoder on eight PSP images per view to learn generalizable subsurface scattering representations that transfer to relighting and dense footprint reconstruction on unseen complex objects.
-
EVENT5Ws: A Large Dataset for Open-Domain Event Extraction from Documents
EVENT5Ws is a new large-scale, manually verified open-domain event extraction dataset that benchmarks LLMs and demonstrates cross-context generalization.
-
A Multimodal Text- and Graph-Based Approach for Open-Domain Event Extraction from Documents
MODEE is a multimodal system that integrates graphs with LLM embeddings to outperform prior open-domain event extraction methods on large datasets.