EGA adapter induces gradient sparsity so that 96.5% of triplets become inactive at convergence, preserving unseen-class geometry while refining seen classes and improving worst-case Label Precision on OOD benchmarks.
Sigmoid loss for language image pre-training
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ST-Prune is a training-free spatio-temporal token pruning framework for VLMs in autonomous driving that achieves near-lossless results at 90% token reduction by exploiting motion volatility, temporal recency, and multi-view geometry.
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EGA: Adapting Frozen Encoders for Vector Search with Bounded Out-of-Distribution Degradation
EGA adapter induces gradient sparsity so that 96.5% of triplets become inactive at convergence, preserving unseen-class geometry while refining seen classes and improving worst-case Label Precision on OOD benchmarks.
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ST-Prune: Training-Free Spatio-Temporal Token Pruning for Vision-Language Models in Autonomous Driving
ST-Prune is a training-free spatio-temporal token pruning framework for VLMs in autonomous driving that achieves near-lossless results at 90% token reduction by exploiting motion volatility, temporal recency, and multi-view geometry.