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Found 9 papers, 3 papers with code
Active Prompting with Chain-of-Thought for Large Language Models
For this purpose, we propose a solution to the key problem of determining which questions are the most important and helpful ones to annotate from a pool of task-specific queries.
Automatic Facial Paralysis Estimation with Facial Action Units
In this paper, we propose a novel Adaptive Local-Global Relational Network (ALGRNet) for facial AU detection and use it to classify facial paralysis severity.
Virtuoso: Video-based Intelligence for real-time tuning on SOCs
First, the system does not consider energy consumption of the models while making a decision on which model to run.
1st Place Solutions for UG2+ Challenge 2021 -- (Semi-)supervised Face detection in the low light condition
In this technical report, we briefly introduce the solution of our team "TAL-ai" for (Semi-) supervised Face detection in the low light condition in UG2+ Challenge in CVPR 2021.
JANUS: Benchmarking Commercial and Open-Source Cloud and Edge Platforms for Object and Anomaly Detection Workloads
In addition, we also look at the pros and cons of some of the proprietary deep-learning object detection packages, such as Amazon Rekognition, Google Vision, and Azure Cognitive Services, to contrast with open-source and tunable solutions, such as Faster R-CNN (FRCNN).
GLGE: A New General Language Generation Evaluation Benchmark
Multi-task benchmarks such as GLUE and SuperGLUE have driven great progress of pretraining and transfer learning in Natural Language Processing (NLP).
ApproxDet: Content and Contention-Aware Approximate Object Detection for Mobiles
In this paper we introduce ApproxDet, an adaptive video object detection framework for mobile devices to meet accuracy-latency requirements in the face of changing content and resource contention scenarios.
TAL EmotioNet Challenge 2020 Rethinking the Model Chosen Problem in Multi-Task Learning
This paper introduces our approach to the EmotioNet Challenge 2020.
ApproxNet: Content and Contention-Aware Video Analytics System for Embedded Clients
None of the current approximation techniques for object classification DNNs can adapt to changing runtime conditions, e. g., changes in resource availability on the device, the content characteristics, or requirements from the user.
