Search Results for author:
Found 17 papers, 6 papers with code
Learning Gabor Texture Features for Fine-Grained Recognition
We innovatively utilize Gabor filters as a powerful extractor to exploit texture features, motivated by the capability of Gabor filters in effectively capturing multi-frequency features and detailed local information.
Towards Balanced Active Learning for Multimodal Classification
Our studies demonstrate that the proposed method achieves more balanced multimodal learning by avoiding greedy sample selection from the dominant modality.
Continual Semantic Segmentation with Automatic Memory Sample Selection
Continual Semantic Segmentation (CSS) extends static semantic segmentation by incrementally introducing new classes for training.
MIGPerf: A Comprehensive Benchmark for Deep Learning Training and Inference Workloads on Multi-Instance GPUs
The vision of this paper is to provide a more comprehensive and practical benchmark study for MIG in order to eliminate the need for tedious manual benchmarking and tuning efforts.
Going Deeper into Recognizing Actions in Dark Environments: A Comprehensive Benchmark Study
While action recognition (AR) has gained large improvements with the introduction of large-scale video datasets and the development of deep neural networks, AR models robust to challenging environments in real-world scenarios are still under-explored.
Self-Supervised Video Representation Learning by Video Incoherence Detection
This paper introduces a novel self-supervised method that leverages incoherence detection for video representation learning.
Aligning Correlation Information for Domain Adaptation in Action Recognition
Yet correlation features of the same action would differ across domains due to domain shift.
InferBench: Understanding Deep Learning Inference Serving with an Automatic Benchmarking System
Our system design follows the best practice in DL clusters operations to expedite day-to-day DL service evaluation efforts by the developers.
Effective Action Recognition with Embedded Key Point Shifts
Temporal feature extraction is an essential technique in video-based action recognition.
PNL: Efficient Long-Range Dependencies Extraction with Pyramid Non-Local Module for Action Recognition
Empirical results prove the effectiveness and efficiency of our PNL module, which achieves state-of-the-art performance of 83. 09% on the Mini-Kinetics dataset, with decreased computation cost compared to the non-local block.
MLModelCI: An Automatic Cloud Platform for Efficient MLaaS
MLModelCI provides multimedia researchers and developers with a one-stop platform for efficient machine learning (ML) services.
ARID: A New Dataset for Recognizing Action in the Dark
We bridge the gap of the lack of data for this task by collecting a new dataset: the Action Recognition in the Dark (ARID) dataset.
Exploiting Inter-Frame Regional Correlation for Efficient Action Recognition
Temporal feature extraction is an important issue in video-based action recognition.
Improving Deep Lesion Detection Using 3D Contextual and Spatial Attention
Secondly, in CT scans, the lesions are often indistinguishable from the background since the lesion and non-lesion areas may have very similar appearances.
Secure Deep Learning Engineering: A Software Quality Assurance Perspective
Over the past decades, deep learning (DL) systems have achieved tremendous success and gained great popularity in various applications, such as intelligent machines, image processing, speech processing, and medical diagnostics.
DeepHunter: Hunting Deep Neural Network Defects via Coverage-Guided Fuzzing
In company with the data explosion over the past decade, deep neural network (DNN) based software has experienced unprecedented leap and is becoming the key driving force of many novel industrial applications, including many safety-critical scenarios such as autonomous driving.
Stochastic Downsampling for Cost-Adjustable Inference and Improved Regularization in Convolutional Networks
We propose a novel approach for cost-adjustable inference in CNNs - Stochastic Downsampling Point (SDPoint).
