speech-emotion-recognition

Vocal Bursts Intensity Prediction

3260 papers • 126 benchmarks • 313 datasets

predict the intensity of 10 categorical emotions

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in speech-emotion-recognition

Trend

Dataset

Best Model

Actions

HUME-VB

Libraries

i

Use these libraries to find speech-emotion-recognition models and implementations

open-mmlab/mmdetection

6 papers 27,065

Datasets

HUME-VB

Subtasks

No subtasks available.

Most implemented papers

High Quality Monocular Depth Estimation via Transfer Learning

Peter Wonka, Ibraheem Alhashim•Sun Dec 30 2018

A convolutional neural network for computing a high-resolution depth map given a single RGB image with the help of transfer learning, which outperforms state-of-the-art on two datasets and also produces qualitatively better results that capture object boundaries more faithfully.

495

Content

PaddlePaddle/PaddleDetection

5 papers 11,809

PaddlePaddle/PaddleSeg

4 papers 8,044

open-mmlab/mmpose

3 papers 4,726

labmlai/annotated_deep_learning_pap…

2 papers 43,537

rwightman/pytorch-image-models

2 papers 28,773

CSAILVision/semantic-segmentation-p…

2 papers 4,784

osmr/imgclsmob

2 papers 2,894

huawei-noah/hebo

2 papers 2,484

benedekrozemberczki/karateclub

2 papers 2,049

0

Paper Graph

Deep High-Resolution Representation Learning for Visual Recognition

Ke Sun, Bin Xiao, Jingdong Wang, Tianheng Cheng, Borui Jiang, Chaorui Deng, Yang Zhao, Dong Liu, Yadong Mu, Mingkui Tan, Xinggang Wang, Wenyu Liu•Mon Aug 19 2019

The superiority of the proposed HRNet in a wide range of applications, including human pose estimation, semantic segmentation, and object detection, is shown, suggesting that the HRNet is a stronger backbone for computer vision problems.

4216 0

Paper Graph

Deep High-Resolution Representation Learning for Human Pose Estimation

Ke Sun, Bin Xiao, Dong Liu, Jingdong Wang•Sun Feb 24 2019

This paper proposes a network that maintains high-resolution representations through the whole process of human pose estimation and empirically demonstrates the effectiveness of the network through the superior pose estimation results over two benchmark datasets: the COCO keypoint detection dataset and the MPII Human Pose dataset.

4672 0

Paper Graph

High-Resolution Representations for Labeling Pixels and Regions

Ke Sun, Bin Xiao, Dong Liu, Jingdong Wang, Tianheng Cheng, Borui Jiang, Yadong Mu, Xinggang Wang, Wenyu Liu, Yang Zhao•Mon Apr 08 2019

A simple modification is introduced to augment the high-resolution representation by aggregating the (upsampled) representations from all the parallel convolutions rather than only the representation from thehigh-resolution convolution, which leads to stronger representations, evidenced by superior results.

863 0

Paper Graph

Large Scale GAN Training for High Fidelity Natural Image Synthesis

Jeff Donahue, K. Simonyan, Andrew Brock•Wed Sep 26 2018

It is found that applying orthogonal regularization to the generator renders it amenable to a simple "truncation trick," allowing fine control over the trade-off between sample fidelity and variety by reducing the variance of the Generator's input.

5949 0

Paper Graph

High-Resolution Image Synthesis with Latent Diffusion Models

A. Blattmann, Robin Rombach, Dominik Lorenz, B. Ommer, Patrick Esser•Sun Dec 19 2021

These latent diffusion models achieve new state of the art scores for image inpainting and class-conditional image synthesis and highly competitive performance on various tasks, including unconditional image generation, text-to-image synthesis, and super-resolution, while significantly reducing computational requirements compared to pixel-based DMs.

21712 0

Paper Graph

High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs

Bryan Catanzaro, Ming-Yu Liu, Jan Kautz, Jun-Yan Zhu, Ting-Chun Wang, Andrew Tao•Wed Nov 29 2017

A new method for synthesizing high-resolution photo-realistic images from semantic label maps using conditional generative adversarial networks (conditional GANs) is presented, which significantly outperforms existing methods, advancing both the quality and the resolution of deep image synthesis and editing.

4278 0

Paper Graph

High-Dimensional Continuous Control Using Generalized Advantage Estimation

P. Abbeel, S. Levine, Michael I. Jordan, John Schulman, Philipp Moritz•Sun Jun 07 2015

This work addresses the large number of samples typically required and the difficulty of obtaining stable and steady improvement despite the nonstationarity of the incoming data by using value functions to substantially reduce the variance of policy gradient estimates at the cost of some bias.

4112 0

Paper Graph

ICNet for Real-Time Semantic Segmentation on High-Resolution Images

Xiaoyong Shen, Jiaya Jia, Jianping Shi, Xiaojuan Qi, Hengshuang Zhao•Wed Apr 26 2017

An image cascade network (ICNet) that incorporates multi-resolution branches under proper label guidance to address the challenging task of real-time semantic segmentation is proposed and in-depth analysis of the framework is provided.

1558 0

Paper Graph

High-Performance Large-Scale Image Recognition Without Normalization

K. Simonyan, Andrew Brock, Samuel L. Smith, Soham De•Wed Feb 10 2021

An adaptive gradient clipping technique is developed which overcomes instabilities in batch normalization, and a significantly improved class of Normalizer-Free ResNets is designed, achieving significantly better performance when finetuning on ImageNet.

590 0

Paper Graph

Adding a benchmark result helps the community track progress.