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Found 10 papers, 2 papers with code
CAP-RAM: A Charge-Domain In-Memory Computing 6T-SRAM for Accurate and Precision-Programmable CNN Inference
A compact, accurate, and bitwidth-programmable in-memory computing (IMC) static random-access memory (SRAM) macro, named CAP-RAM, is presented for energy-efficient convolutional neural network (CNN) inference.
Robust Unsupervised Video Anomaly Detection by Multi-Path Frame Prediction
In this paper, we propose a novel and robust unsupervised video anomaly detection method by frame prediction with proper design which is more in line with the characteristics of surveillance videos.
Towards automatic visual inspection: A weakly supervised learning method for industrial applicable object detection
With the recent progress of deep learning, advanced industrial object detectors are built for smart industrial applications.
Ranked #17 on
Weakly Supervised Object Detection
on PASCAL VOC 2007
AWR: Adaptive Weighting Regression for 3D Hand Pose Estimation
In this paper, we propose an adaptive weighting regression (AWR) method to leverage the advantages of both detection-based and regression-based methods.
Ranked #1 on
Hand Pose Estimation
on MSRA Hands
Adversarial and Domain-Aware BERT for Cross-Domain Sentiment Analysis
To tackle these problems, we design a post-training procedure, which contains the target domain masked language model task and a novel domain-distinguish pre-training task.
Investigating Capsule Network and Semantic Feature on Hyperplanes for Text Classification
It has been demonstrated that multiple senses of a word actually reside in linear superposition within the word embedding so that specific senses can be extracted from the original word embedding.
Capsule Network with Interactive Attention for Aspect-Level Sentiment Classification
Aspect-level sentiment classification is a crucial task for sentiment analysis, which aims to identify the sentiment polarities of specific targets in their context.
OICSR: Out-In-Channel Sparsity Regularization for Compact Deep Neural Networks
Many channel pruning works utilize structured sparsity regularization to zero out all the weights in some channels and automatically obtain structure-sparse network in training stage.
Fewer is More: Image Segmentation Based Weakly Supervised Object Detection with Partial Aggregation
As most weakly supervised object detection methods are based on pre-generated proposals, they often show two false detections: (i) group multiple object instances with one bounding box, and (ii) focus on only parts rather than the whole objects.
Ranked #20 on
Weakly Supervised Object Detection
on PASCAL VOC 2007
Chinese/English mixed Character Segmentation as Semantic Segmentation
We take advantage of the successful architecture called fully convolutional networks (FCN) in the field of semantic segmentation.
