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ACT is the first framework to study semi-supervised VIOD under this image-pair-level setting and forms consensus pseudo labels under pair-preserving views to recover branch-wise misses and supervise unlabeled pairs.
MVLGeo, an efficient framework designed to unify multiple viewpoints and reduce model redundancy, achieves state-of-the-art performance, demonstrating robustness to input degradation and generalization across viewpoints.
JFRDet achieves state-of-the-art performance in visible-infrared object detection by explicitly addressing severe geometric misalignment and illumination variability.
Frequency-domain fusion of RGB and event data significantly boosts object tracking performance, achieving a leading 76.6% precision on the challenging COESOT benchmark.
By dynamically adjusting state transitions based on event sparsity, MambaTrack achieves state-of-the-art RGB-Event object tracking, outperforming methods stuck with static transition matrices.
Fine-grained partitioning and targeted safeguards can provide robust NPU reliability with minimal performance overhead, challenging the assumption that redundancy is the only path forward.
