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When evaluated on unseen species, the best adapted VLMs outperformed specialist models trained on the same data in identifying merge errors and LoRA on a few thousand labels brought open models level with specialist models.
It is shown that the minimum aggregation is essential: mean aggregation can be exploited, saturating the proxy reward while degrading accuracy, and the minimum aggregation is essential for PROSE, Process Reward Guided Self-Training.
Results indicate that a direct, multi-attribute 3D consistency objective, when combined with high-quality correspondences, is effective for addressing the ill-posed sparse-view reconstruction problem.
This work proposes the use of ternary spike neurons, which enhance the information-carrying capacity in the spiking neural rendering model, and introduces Spik-NeRF (Spiking Neural Radi-ance Fields with Ternary Spike), which achieves rendering performance comparable to ANN-based NeRF models.
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