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Found 16 papers, 6 papers with code
Modeling Compositionality with Dependency Graph for Dialogue Generation
Because of the compositionality of natural language, syntactic structure which contains the information about the relationship between words is a key factor for semantic understanding.
Context Sensing Attention Network for Video-based Person Re-identification
In this work, we propose a novel Context Sensing Attention Network (CSA-Net), which improves both the frame feature extraction and temporal aggregation steps.
CPED: A Large-Scale Chinese Personalized and Emotional Dialogue Dataset for Conversational AI
Finally, we provide baseline systems for these tasks and consider the function of speakers' personalities and emotions on conversation.
Ranked #1 on
Emotion Recognition in Conversation
on CPED
Compact Model Training by Low-Rank Projection with Energy Transfer
In this paper, we devise a new training method, low-rank projection with energy transfer (LRPET), that trains low-rank compressed networks from scratch and achieves competitive performance.
Weight Evolution: Improving Deep Neural Networks Training through Evolving Inferior Weight Values
Comprehensive experiments show that WE outperforms the other reactivation methods and plug-in training methods with typical convolutional neural networks, especially lightweight networks.
To Deconvolve, or Not to Deconvolve: Inferences of Neuronal Activities using Calcium Imaging Data
With the increasing popularity of calcium imaging data in neuroscience research, methods for analyzing calcium trace data are critical to address various questions.
LSSED: a large-scale dataset and benchmark for speech emotion recognition
Speech emotion recognition is a vital contributor to the next generation of human-computer interaction (HCI).
Listwise View Ranking for Image Cropping
However, the performance of ranking-based methods is often poor and this is mainly due to two reasons: 1) image cropping is a listwise ranking task rather than pairwise comparison; 2) the rescaling caused by pooling layer and the deformation in view generation damage the performance of composition learning.
BIT: Biologically Inspired Tracker
Visual tracking is challenging due to image variations caused by various factors, such as object deformation, scale change, illumination change and occlusion.
Geometry Processing of Conventionally Produced Mouse Brain Slice Images
In this paper we present techniques and algorithms for automatic registration and 3D reconstruction of conventionally produced mouse brain slices in a standardized atlas space.
Deep Sampling Networks
With the powerful down-sampling process, the co-training DSN set a new state-of-the-art performance for image super-resolution.
FReLU: Flexible Rectified Linear Units for Improving Convolutional Neural Networks
Rectified linear unit (ReLU) is a widely used activation function for deep convolutional neural networks.
Manifold Regularized Slow Feature Analysis for Dynamic Texture Recognition
However, complex temporal variations require high-level semantic representations to fully achieve temporal slowness, and thus it is impractical to learn a high-level representation from dynamic textures directly by SFA.
Single Image Super-Resolution Using Multi-Scale Convolutional Neural Network
However, the previous methods mainly restore images from one single area in the low resolution (LR) input, which limits the flexibility of models to infer various scales of details for high resolution (HR) output.
Multi-scale Convolutional Neural Networks for Crowd Counting
Crowd counting on static images is a challenging problem due to scale variations.
DehazeNet: An End-to-End System for Single Image Haze Removal
The key to achieve haze removal is to estimate a medium transmission map for an input hazy image.
Ranked #5 on
Image Dehazing
on KITTI
