LIBERO is a new benchmark for lifelong robot learning that evaluates transfer of declarative, procedural, and mixed knowledge across 130 manipulation tasks with provided demonstration data.
Imagenet: A large- scale hierarchical image database
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GeoFlowVLM learns joint distributions of l2-normalized VLM embeddings on the product hypersphere via Riemannian flow matching to expose both aleatoric and epistemic uncertainty through derived entropy and typicality scores.
CoDiRe blends VLM and target model predictions via MSP-based weighting and Optimal Transport rectification to enable stable continual test-time adaptation, outperforming CoTTA by 10.55% on ImageNet-C at 48% of the compute cost.
Introduces a benchmark suite of over 18 MBRL environments, evaluates multiple algorithms under consistent settings, and identifies three core challenges: dynamics bottleneck, planning horizon dilemma, and early-termination dilemma.
Proposes a patch-based reflection intensity prior learned by RPEN and applied in PRRN transformer U-Net to achieve state-of-the-art single image reflection removal on real-world benchmarks.
Operational U-Nets integrate Self-ONN layers into a compact U-Net to deliver better active fire detection performance with lower computational cost than standard approaches.
EPNAS uses a progressive search policy with REINFORCE performance prediction to search neural architectures in parallel, supporting multiple resource constraints and outperforming ENAS and PNAS on CIFAR-10 and ImageNet in speed and accuracy.
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Test-Time Distillation for Continual Model Adaptation
CoDiRe blends VLM and target model predictions via MSP-based weighting and Optimal Transport rectification to enable stable continual test-time adaptation, outperforming CoTTA by 10.55% on ImageNet-C at 48% of the compute cost.