https://github.com/shreyanc/model_debugging
공식 코드Tracing Back Music Emotion Predictions to Sound Sources and Intuitive Perceptual Qualities
Papers with Code 아카이브
Emotion Recognition
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 12
- 평가표
- 10
- 보존된 평가 기록
- 18
Emotion Recognition is an important area of research to enable effective human-computer interaction. Human emotions can be detected using speech signal, facial expressions, body language, and electroencephalography (EEG). Source: Using Deep Autoencoders for Facial Expression Recognition
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/shreyashm16/memotion-3.0
공식 코드Memotion 3: Dataset on Sentiment and Emotion Analysis of Codemixed Hindi-English Memes
https://github.com/shu-hai/CDPA
공식 코드CDPA: Common and Distinctive Pattern Analysis between High-dimensional Datasets
https://github.com/shuzihuaiyu/tacfn
공식 코드TACFN: Transformer-based Adaptive Cross-modal Fusion Network for Multimodal Emotion Recognition
https://github.com/siddiquelatif/URDU-Dataset
공식 코드Cross Lingual Speech Emotion Recognition: Urdu vs. Western Languages
https://github.com/siku-bert/sikugpt
공식 코드https://github.com/simpleshinobu/visdial-principles
공식 코드https://github.com/siyuhuang/quantart
공식 코드QuantArt: Quantizing Image Style Transfer Towards High Visual Fidelity
https://github.com/sjmielke/spell-once
공식 코드Spell Once, Summon Anywhere: A Two-Level Open-Vocabulary Language Model
https://github.com/skchao74/Distributed-bootstrap
공식 코드Distributed Bootstrap for Simultaneous Inference Under High Dimensionality
https://github.com/skeletonnn/cfn-sr
공식 코드https://github.com/skywalker023/focused-empathy
공식 코드Perspective-taking and Pragmatics for Generating Empathetic Responses Focused on Emotion Causes
https://github.com/smaityumich/mrlasso
공식 코드Meta-analysis of heterogeneous data: integrative sparse regression in high-dimensions
https://github.com/smart-task/smart-dataset
공식 코드SeMantic AnsweR Type prediction task (SMART) at ISWC 2020 Semantic Web Challenge
https://github.com/smartcameras/privatenet
공식 코드https://github.com/smartcameras/selfcrossattn
공식 코드Is Cross-Attention Preferable to Self-Attention for Multi-Modal Emotion Recognition?
https://github.com/smt-HS/I-SEE
공식 코드High-Quality Diversification for Task-Oriented Dialogue Systems
https://github.com/smwanghhh/co-sa
공식 코드Cooperative Sentiment Agents for Multimodal Sentiment Analysis
https://github.com/snu-arc/l3
공식 코드L3: Accelerator-Friendly Lossless Image Format for High-Resolution, High-Throughput DNN Training
https://github.com/snu-mllab/efficient-cnn-depth-compression
공식 코드Efficient Latency-Aware CNN Depth Compression via Two-Stage Dynamic Programming
https://github.com/soerenkoh/circularfusionoperator
공식 코드https://github.com/sokbae/replication-junlee-2023-aistats
공식 코드Average Adjusted Association: Efficient Estimation with High Dimensional Confounders
https://github.com/sp-uhh/label-uncertainty-ser
공식 코드Label Uncertainty Modeling and Prediction for Speech Emotion Recognition using t-Distributions
https://github.com/spaghettiSystems/serval
공식 코드SER Evals: In-domain and Out-of-domain Benchmarking for Speech Emotion Recognition
https://github.com/speechbrain/speechbrain
공식 코드https://github.com/sphinxteam/GeneralizedLinearModel2017
공식 코드Optimal Errors and Phase Transitions in High-Dimensional Generalized Linear Models
https://github.com/sphinxteam/PhaseRetrieval_demo
공식 코드Phase retrieval in high dimensions: Statistical and computational phase transitions
https://github.com/sreyan88/disclstm
공식 코드M-MELD: A Multilingual Multi-Party Dataset for Emotion Recognition in Conversations
https://github.com/sreyan88/mmer
공식 코드MMER: Multimodal Multi-task Learning for Speech Emotion Recognition
https://github.com/srijankr/multimodality-language-disparity
공식 코드Overcoming Language Disparity in Online Content Classification with Multimodal Learning
https://github.com/srishtigupta253/tone
공식 코드https://github.com/srmatth/mshap
공식 코드https://github.com/stability-ai/generative-models
공식 코드Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models
https://github.com/stan-dev/projpred
공식 코드Projective Inference in High-dimensional Problems: Prediction and Feature Selection
https://github.com/statusrank/XCurve
공식 코드https://github.com/steachhr/pointcloud_scattering
공식 코드The Manifold Scattering Transform for High-Dimensional Point Cloud Data
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.