As more and more on-premise databases are moving towards the cloud service, it is crucial to have a benchmark to holistically evaluate the performance of their core features including elasticity, multi-tenancy, and cost-efficiency. However, existing benchmarks lack specific workload patterns and metrics for evaluating cloud-native databases, and the real workload is often unavailable due to privacy requirements. In this paper, we propose a new testbed for cloud-native databases, named CloudyBench. Its core contribution is to provide tailored workloads and metrics to evaluate the service quality of cloud-native databases in various dimensions. First, we design cloud-native workload patterns with peaks and valleys for elasticity evaluation. Second, we devise new multi-tenancy patterns by posing varied resource contention to evaluate the resource scheduling among tenants. Third, we propose a unified metric that considers performance, cost, elasticity, multitenancy, replication lag time, and fail-over. Fourth, we provide an evaluation testbed for evaluating cloud-native databases. To verify the effectiveness of CloudyBench, extensive experiments have been conducted over five commercial representatives from multiple cloud providers. We also obtain a number of insights for the performance implications of cloud-native databases from the architectural perspective.

In this paper, we introduce a novel approach to novel object captioning which employs relative contrastive learning to learn visual and semantic alignment. Our approach maximizes compatibility between regions and object tags in a contrastive manner. To set up a proper contrastive learning objective, for each image, we augment tags by leveraging the relative nature of positive and negative pairs obtained from foundation models such as CLIP. We then use the rank of each augmented tag in a list as a relative relevance label to contrast each top-ranked tag with a set of lower-ranked tags. This learning objective encourages the top-ranked tags to be more compatible with their image and text context than lower-ranked tags, thus improving the discriminative ability of the learned multi-modality representation. We evaluate our approach on two datasets and show that our proposed RCA-NOC approach outperforms state-of-the-art methods by a large margin, demonstrating its effectiveness in improving vision-language representation for novel object captioning.

We propose INS-Conv, an INcremental Sparse Convolutional network which enables online accurate 3D semantic and instance segmentation. Benefiting from the incremental nature of RGB-D reconstruction, we only need to update the residuals between the reconstructed scenes of consecutive frames, which are usually sparse. For layer design, we define novel residual propagation rules for sparse convolution operations, achieving close approximation to standard sparse convolution. For network architecture, an uncertainty term is proposed to adaptively select which residual to update, further improving the inference accuracy and efficiency. Based on INS-Conv, an online joint 3D semantic and instance segmentation pipeline is proposed, reaching an inference speed of 15 FPS on GPU and 10 FPS on CPU. Experiments on ScanNetv2 and SceneNN datasets show that the accuracy of our method surpasses previous online methods by a large margin, and is on par with state-of-the-art offline methods. A live demo on portable devices further shows the superior performance of INS-Conv.