BlackVIP: Black-Box Visual Prompting for Robust Transfer Learning

With the surge of large-scale pre-trained models (PTMs), fine-tuning these models to numerous downstream tasks becomes a crucial problem. Consequently, parameter efficient transfer learning (PETL) of large models has grasped huge attention. While recent PETL methods showcase impressive performance, they rely on optimistic assumptions: 1) the entire parameter set of a PTM is available, and 2) a sufficiently large memory capacity for the fine-tuning is equipped. However, in most real-world applications, PTMs are served as a black-box API or proprietary software without explicit parameter accessibility. Besides, it is hard to meet a large memory requirement for modern PTMs. In this work, we propose black-box visual prompting (BlackVIP), which efficiently adapts the PTMs without knowledge about model architectures and parameters. BlackVIP has two components; 1) Coordinator and 2) simultaneous perturbation stochastic approximation with gradient correction (SPSA-GC). The Coordinator designs input-dependent image-shaped visual prompts, which improves few-shot adaptation and robustness on distribution/location shift. SPSA-GC efficiently estimates the gradient of a target model to update Coordinator. Extensive experiments on 16 datasets demonstrate that BlackVIP enables robust adaptation to diverse domains without accessing PTMs' parameters, with minimal memory requirements. Code: \url{https://github.com/changdaeoh/BlackVIP}Comment: Accepted to CVPR 202

Failure-Atomic Byte-Addressable R-tree for Persistent Memory

In this article, we propose Failure-atomic Byte-addressable R-tree (FBR-tree) that leverages the byte-addressability, persistence, and high performance of persistent memory while guaranteeing the crash consistency. We carefully control the order of store and cacheline flush instructions and prevent any single store instruction from making an FBR-tree inconsistent and unrecoverable. We also develop a non-blocking lock-free range query algorithm for FBR-tree. Since FBR-tree allows read transactions to detect and ignore any transient inconsistent states, multiple read transactions can concurrently access tree nodes without using shared locks while other write transactions are making changes to them. Our performance study shows that FBR-tree successfully reduces the legacy logging overhead and the lock-free range query algorithm shows up to 2.6x higher query processing throughput than the shared lock-based crabbing concurrency protocol