As the rapid progression of practical applications based on Large Langua...
Root Cause Analysis (RCA) plays an indispensable role in distributed dat...
Predictive autoscaling (autoscaling with workload forecasting) is an
imp...
With the ever-growing communication demands and the unceasing miniaturiz...
Pedestrian detection in a crowd is a challenging task due to a high numb...
Recent evidence shows that convolutional neural networks (CNNs) are bias...
One-stage object detectors are trained by optimizing classification-loss...
Monitoring the population and movements of endangered species is an impo...
One-stage object detectors are trained by optimizing classification-loss...
Current knowledge distillation methods require full training data to dis...
Binary neural networks have great resource and computing efficiency, whi...
We propose Deeply Supervised Object Detectors (DSOD), an object detectio...
Depthwise separable convolution has shown great efficiency in network de...
Object detection has made great progress in the past few years along wit...
Visual question answering (VQA) requires joint comprehension of images a...
Deep neural networks are vulnerable to adversarial examples, which poses...
The deployment of deep convolutional neural networks (CNNs) in many real...
We present Deeply Supervised Object Detector (DSOD), a framework that ca...
Low-bit deep neural networks (DNNs) become critical for embedded applica...
This paper focuses on a novel and challenging vision task, dense video
c...
Recently, many researches employ middle-layer output of convolutional ne...
This paper proposes a novel face recognition algorithm based on large-sc...