VSCD presents a query-centric multi-reference model for pixel-wise change detection in unaligned, unsynchronized indoor videos, backed by a 1.1-million-frame benchmark and real-robot validation for surveillance and incremental learning.
Advances in Neural Information Processing Systems , volume=
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Introduces replay-based continual learning with sequential invariance alignment to learn domain-invariant representations, outperforming baselines on generalization to unseen domains across six datasets in vision, medicine, manufacturing, and ecology.
HEDP uses energy regularization inspired by Helmholtz free energy plus hybrid energy-distance weighting in prompts to improve domain selection and achieve a 2.57% accuracy gain on benchmarks like CORe50 while mitigating catastrophic forgetting.
citing papers explorer
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VSCD: Video-based Scene Change Detection in Unaligned Scenes
VSCD presents a query-centric multi-reference model for pixel-wise change detection in unaligned, unsynchronized indoor videos, backed by a 1.1-million-frame benchmark and real-robot validation for surveillance and incremental learning.
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Continual Learning of Domain-Invariant Representations
Introduces replay-based continual learning with sequential invariance alignment to learn domain-invariant representations, outperforming baselines on generalization to unseen domains across six datasets in vision, medicine, manufacturing, and ecology.
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HEDP: A Hybrid Energy-Distance Prompt-based Framework for Domain Incremental Learning
HEDP uses energy regularization inspired by Helmholtz free energy plus hybrid energy-distance weighting in prompts to improve domain selection and achieve a 2.57% accuracy gain on benchmarks like CORe50 while mitigating catastrophic forgetting.