Entropy-gradient grounding uses model uncertainty to retrieve evidence regions in VLMs, improving performance on detail-critical and compositional tasks across multiple architectures.
Unifying feature and cost aggregation with transformers for semantic and visual correspondence
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.CV 3verdicts
UNVERDICTED 3roles
background 1polarities
background 1representative citing papers
C3G creates compact 3D Gaussian representations with 2K points by guiding placement via learnable tokens that aggregate multi-view features through attention, yielding better efficiency and performance than dense methods.
Aligning flow-matching assembly networks to frozen 3D encoder topology via cosine and CKA losses speeds training up to 6.9× and improves in- and out-of-distribution assembly accuracy with zero inference cost.
citing papers explorer
-
Entropy-Gradient Grounding: Training-Free Evidence Retrieval in Vision-Language Models
Entropy-gradient grounding uses model uncertainty to retrieve evidence regions in VLMs, improving performance on detail-critical and compositional tasks across multiple architectures.
-
C3G: Learning Compact 3D Representations with 2K Gaussians
C3G creates compact 3D Gaussian representations with 2K points by guiding placement via learnable tokens that aggregate multi-view features through attention, yielding better efficiency and performance than dense methods.
-
TORA: Topological Representation Alignment for 3D Shape Assembly
Aligning flow-matching assembly networks to frozen 3D encoder topology via cosine and CKA losses speeds training up to 6.9× and improves in- and out-of-distribution assembly accuracy with zero inference cost.