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Found 7 papers, 3 papers with code
Harmonious Feature Learning for Interactive Hand-Object Pose Estimation
Notably, the performance of our model on hand pose estimation even surpasses that of existing works that only perform the single-hand pose estimation task.
Ranked #2 on
hand-object pose
on DexYCB
Learning Granularity-Unified Representations for Text-to-Image Person Re-identification
In PGU, we adopt a set of shared and learnable prototypes as the queries to extract diverse and semantically aligned features for both modalities in the granularity-unified feature space, which further promotes the ReID performance.
Privacy-Preserving Inference in Machine Learning Services Using Trusted Execution Environments
However, the overhead of blinding and unblinding the data is a limiting factor to scalability.
Train Where the Data is: A Case for Bandwidth Efficient Coded Training
Most of the model training is performed on high performance compute nodes and the training data is stored near these nodes for faster training.
Collage Inference: Achieving low tail latency during distributed image classification using coded redundancy models
In this work, we argue that MLaaS platforms also provide unique opportunities to cut the cost of redundancy.
Collage Inference: Using Coded Redundancy for Low Variance Distributed Image Classification
Deploying the collage-cnn models in the cloud, we demonstrate that the 99th percentile tail latency of inference can be reduced by 1. 2x to 2x compared to replication based approaches while providing high accuracy.
GradiVeQ: Vector Quantization for Bandwidth-Efficient Gradient Aggregation in Distributed CNN Training
Data parallelism can boost the training speed of convolutional neural networks (CNN), but could suffer from significant communication costs caused by gradient aggregation.
