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.
Proceedings of the National Academy of Sciences , 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.
Compressive Transformer sets new records on WikiText-103 (17.1 ppl) and Enwik8 (0.97 bpc) via memory compression and introduces the PG-19 long-range language benchmark.
StaR-MoE adds sensitivity-aware routing alignment and asymmetric capacity regularization to expandable MoE architectures for class-incremental learning, reducing interference from routing drift and improving average and last-task accuracy on four benchmarks.
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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Compressive Transformers for Long-Range Sequence Modelling
Compressive Transformer sets new records on WikiText-103 (17.1 ppl) and Enwik8 (0.97 bpc) via memory compression and introduces the PG-19 long-range language benchmark.
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Stable Routing for Mixture-of-Experts in Class-Incremental Learning
StaR-MoE adds sensitivity-aware routing alignment and asymmetric capacity regularization to expandable MoE architectures for class-incremental learning, reducing interference from routing drift and improving average and last-task accuracy on four benchmarks.