178 citations · 987 across the 31 of their papers we have counts for
13 papers · 1 filter
Mimic The Raw Domain: Accelerating Action Recognition in the Compressed Domain
Barak Battash, Haim Barad, Hanlin Tang +1
Video understanding usually requires expensive computation that prohibits its deployment, yet videos contain significant spatiotemporal redundancy that can be exploited. In particu…
MLPerf Inference Benchmark
Vijay Janapa Reddi, Christine Cheng, David Kanter +44
Machine-learning (ML) hardware and software system demand is burgeoning. Driven by ML applications, the number of different ML inference systems has exploded. Over 100 organization…
Using Image Priors to Improve Scene Understanding
Brigit Schroeder, Hanlin Tang, Alexandre Alahi
Semantic segmentation algorithms that can robustly segment objects across multiple camera viewpoints are crucial for assuring navigation and safety in emerging applications such as…
Central Server Free Federated Learning over Single-sided Trust Social Networks
Chaoyang He, Conghui Tan, Hanlin Tang +2
Federated learning has become increasingly important for modern machine learning, especially for data privacy-sensitive scenarios. Existing federated learning mostly adopts the cen…
MLPerf Training Benchmark
Peter Mattson, Christine Cheng, Cody Coleman +34
Machine learning (ML) needs industry-standard performance benchmarks to support design and competitive evaluation of the many emerging software and hardware solutions for ML. But M…
Triplet-Aware Scene Graph Embeddings
Brigit Schroeder, Subarna Tripathi, Hanlin Tang
Scene graphs have become an important form of structured knowledge for tasks such as for image generation, visual relation detection, visual question answering, and image retrieval…
: Decentralization Meets Error-Compensated Compression
Hanlin Tang, Xiangru Lian, Shuang Qiu +4
Communication is a key bottleneck in distributed training. Recently, an \emph{error-compensated} compression technology was particularly designed for the \emph{centralized} learnin…
Generalization to Novel Objects using Prior Relational Knowledge
Varun Kumar Vijay, Abhinav Ganesh, Hanlin Tang +1
To solve tasks in new environments involving objects unseen during training, agents must reason over prior information about those objects and their relations. We introduce the Pri…
ATRW: A Benchmark for Amur Tiger Re-identification in the Wild
Shuyuan Li, Jianguo Li, Hanlin Tang +2
Monitoring the population and movements of endangered species is an important task to wildlife conversation. Traditional tagging methods do not scale to large populations, while ap…
DoubleSqueeze: Parallel Stochastic Gradient Descent with Double-Pass Error-Compensated Compression
Hanlin Tang, Xiangru Lian, Chen Yu +2
A standard approach in large scale machine learning is distributed stochastic gradient training, which requires the computation of aggregated stochastic gradients over multiple nod…
Compact Scene Graphs for Layout Composition and Patch Retrieval
Subarna Tripathi, Sharath Nittur Sridhar, Sairam Sundaresan +1
Structured representations such as scene graphs serve as an efficient and compact representation that can be used for downstream rendering or retrieval tasks. However, existing eff…
SpaceNet MVOI: a Multi-View Overhead Imagery Dataset
Nicholas Weir, David Lindenbaum, Alexei Bastidas +5
Detection and segmentation of objects in overheard imagery is a challenging task. The variable density, random orientation, small size, and instance-to-instance heterogeneity of ob…
Using Scene Graph Context to Improve Image Generation
Subarna Tripathi, Anahita Bhiwandiwalla, Alexei Bastidas +1
Generating realistic images from scene graphs asks neural networks to be able to reason about object relationships and compositionality. As a relatively new task, how to properly e…