Composed image retrieval is reframed as calibrated intent resolution under uncertainty via conformal prediction sets and expected-information-gain clarification, with new AmbiCIR benchmark showing matched single-turn SOTA and faster multi-turn resolution with valid coverage.
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6 Pith papers cite this work. Polarity classification is still indexing.
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2026 6roles
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RelWitness introduces relation witnesses as observable visual-geometric cues to classify unannotated relations and enable positive-unlabeled learning for open-vocabulary 3D scene graph generation.
C²R framework for robust dataset distillation prioritizes small-margin adversaries via a derived perturbation score and widens class boundaries with contrastive loss, yielding 2.8% average robust accuracy gains on CIFAR and ImageNet benchmarks.
ScriptHOI decomposes HOI phrases into state slots, uses slot-wise script coverage and conflict matching, and applies interval partial-label learning to improve rare and unseen interaction detection.
ReLIC-SGG treats unannotated scene-graph relations as latent variables and uses a semantic relation lattice with positive-unlabeled learning to recover missing relations, improving rare and unseen predicate prediction.
A counterfactual verification framework for open-vocabulary scene graph generation that decomposes predicates into evidence types and tests whether relation predictions are sensitive to removal of necessary visual evidence.
citing papers explorer
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Resolving Ambiguity in Composed Image Retrieval via Calibrated Interaction
Composed image retrieval is reframed as calibrated intent resolution under uncertainty via conformal prediction sets and expected-information-gain clarification, with new AmbiCIR benchmark showing matched single-turn SOTA and faster multi-turn resolution with valid coverage.
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RelWitness: Open-Vocabulary 3D Scene Graph Generation with Visual-Geometric Relation Witnesses
RelWitness introduces relation witnesses as observable visual-geometric cues to classify unannotated relations and enable positive-unlabeled learning for open-vocabulary 3D scene graph generation.
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Mind Your Margin and Boundary: Are Your Distilled Datasets Truly Robust?
C²R framework for robust dataset distillation prioritizes small-margin adversaries via a derived perturbation score and widens class boundaries with contrastive loss, yielding 2.8% average robust accuracy gains on CIFAR and ImageNet benchmarks.
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ScriptHOI: Learning Scripted State Transitions for Open-Vocabulary Human-Object Interaction Detection
ScriptHOI decomposes HOI phrases into state slots, uses slot-wise script coverage and conflict matching, and applies interval partial-label learning to improve rare and unseen interaction detection.
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ReLIC-SGG: Relation Lattice Completion for Open-Vocabulary Scene Graph Generation
ReLIC-SGG treats unannotated scene-graph relations as latent variables and uses a semantic relation lattice with positive-unlabeled learning to recover missing relations, improving rare and unseen predicate prediction.
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CAGE-SGG: Counterfactual Active Graph Evidence for Open-Vocabulary Scene Graph Generation
A counterfactual verification framework for open-vocabulary scene graph generation that decomposes predicates into evidence types and tests whether relation predictions are sensitive to removal of necessary visual evidence.