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Found 109 papers, 40 papers with code
Resolve Domain Conflicts for Generalizable Remote Physiological Measurement
Remote photoplethysmography (rPPG) technology has become increasingly popular due to its non-invasive monitoring of various physiological indicators, making it widely applicable in multimedia interaction, healthcare, and emotion analysis.
SurvMamba: State Space Model with Multi-grained Multi-modal Interaction for Survival Prediction
SurvMamba is implemented with a Hierarchical Interaction Mamba (HIM) module that facilitates efficient intra-modal interactions at different granularities, thereby capturing more detailed local features as well as rich global representations.
Self-supervised Monocular Depth Estimation on Water Scenes via Specular Reflection Prior
Monocular depth estimation from a single image is an ill-posed problem for computer vision due to insufficient reliable cues as the prior knowledge.
Toward Tiny and High-quality Facial Makeup with Data Amplify Learning
The core idea of DAL lies in employing a Diffusion-based Data Amplifier (DDA) to "amplify" limited images for the model training, thereby enabling accurate pixel-to-pixel supervision with merely a handful of annotations.
SoftPatch: Unsupervised Anomaly Detection with Noisy Data
Noise discriminators are utilized to generate outlier scores for patch-level noise elimination before coreset construction.
Toward Multi-class Anomaly Detection: Exploring Class-aware Unified Model against Inter-class Interference
To address this issue, we introduce a Multi-class Implicit Neural representation Transformer for unified Anomaly Detection (MINT-AD), which leverages the fine-grained category information in the training stage.
Tuning-Free Image Customization with Image and Text Guidance
To our knowledge, this is the first tuning-free method that concurrently utilizes text and image guidance for image customization in specific regions.
HCPM: Hierarchical Candidates Pruning for Efficient Detector-Free Matching
Deep learning-based image matching methods play a crucial role in computer vision, yet they often suffer from substantial computational demands.
Absence of spurious solutions far from ground truth: A low-rank analysis with high-order losses
Matrix sensing problems exhibit pervasive non-convexity, plaguing optimization with a proliferation of suboptimal spurious solutions.
Generalizable Whole Slide Image Classification with Fine-Grained Visual-Semantic Interaction
It is designed to enhance the model's generalizability by leveraging the interaction between localized visual patterns and fine-grained pathological semantics.
Enhancing Quality of Compressed Images by Mitigating Enhancement Bias Towards Compression Domain
However, these methods exhibit a pervasive enhancement bias towards the compression domain, inadvertently regarding it as more realistic than the raw domain.
Unsupervised Continual Anomaly Detection with Contrastively-learned Prompt
Unsupervised Anomaly Detection (UAD) with incremental training is crucial in industrial manufacturing, as unpredictable defects make obtaining sufficient labeled data infeasible.
MetaSegNet: Metadata-collaborative Vision-Language Representation Learning for Semantic Segmentation of Remote Sensing Images
Semantic segmentation of remote sensing images plays a vital role in a wide range of Earth Observation (EO) applications, such as land use land cover mapping, environment monitoring, and sustainable development.
Decoupled Knowledge with Ensemble Learning for Online Distillation
To obtain early decoupled knowledge, an initialization scheme for the teacher is devised, and a 2D geometry-based analysis experiment is conducted under ideal conditions to showcase the effectiveness of this scheme.
CrackCLF: Automatic Pavement Crack Detection based on Closed-Loop Feedback
Meanwhile, these models can not automatically correct errors in the prediction, nor can it adapt to the changes of the environment to automatically extract and detect thin cracks.
Self-similarity Prior Distillation for Unsupervised Remote Physiological Measurement
In this paper, we propose a Self-Similarity Prior Distillation (SSPD) framework for unsupervised rPPG estimation, which capitalizes on the intrinsic self-similarity of cardiac activities.
Exploring Driving Behavior for Autonomous Vehicles Based on Gramian Angular Field Vision Transformer
Effective classification of autonomous vehicle (AV) driving behavior emerges as a critical area for diagnosing AV operation faults, enhancing autonomous driving algorithms, and reducing accident rates.
Neural Dysfunction Underlying Working Memory Processing at Different Stages of the Illness Course in Schizophrenia:A Comparative Meta-analysis
Schizophrenia (SCZ), as a chronic and persistent disorder, exhibits working memory deficits across various stages of the disorder, yet the neural mechanisms underlying these deficits remain elusive with inconsistent neuroimaging findings.
Multi-step prediction of chlorophyll concentration based on Adaptive Graph-Temporal Convolutional Network with Series Decomposition
The adaptive graph convolution learns the relationship between different water quality parameters, updates the state information of each parameter, and improves the learning ability of the update relationship between nodes.
Tackling the Non-IID Issue in Heterogeneous Federated Learning by Gradient Harmonization
In this work, we revisit this key challenge through the lens of gradient conflicts on the server side.
Joint Self-supervised Depth and Optical Flow Estimation towards Dynamic Objects
In this work, we construct a joint inter-frame-supervised depth and optical flow estimation framework, which predicts depths in various motions by minimizing pixel wrap errors in bilateral photometric re-projections and optical vectors.
NTIRE 2023 Quality Assessment of Video Enhancement Challenge
61 participating teams submitted their prediction results during the development phase, with a total of 3168 submissions.
Frequency-aware Dimension Selection for Static Word Embedding by Mixed Product Distance
Static word embedding is still useful, particularly for context-unavailable tasks, because in the case of no context available, pre-trained language models often perform worse than static word embeddings.
FusionDepth: Complement Self-Supervised Monocular Depth Estimation with Cost Volume
Multi-view stereo depth estimation based on cost volume usually works better than self-supervised monocular depth estimation except for moving objects and low-textured surfaces.
On Fast-Converged Deep Reinforcement Learning for Optimal Dispatch of Large-Scale Power Systems under Transient Security Constraints
The effectiveness, efficiency, and accuracy of the proposed method are demonstrated through experiments in the IEEE 39-bus system and a practical 710-bus regional power grid.
EWT: Efficient Wavelet-Transformer for Single Image Denoising
Transformer-based image denoising methods have achieved encouraging results in the past year.
MD-VQA: Multi-Dimensional Quality Assessment for UGC Live Videos
User-generated content (UGC) live videos are often bothered by various distortions during capture procedures and thus exhibit diverse visual qualities.
TextShield: Beyond Successfully Detecting Adversarial Sentences in Text Classification
A recent line of work, detection-based defense, aims to distinguish adversarial sentences from benign ones.
AdaptSLAM: Edge-Assisted Adaptive SLAM with Resource Constraints via Uncertainty Minimization
Edge computing is increasingly proposed as a solution for reducing resource consumption of mobile devices running simultaneous localization and mapping (SLAM) algorithms, with most edge-assisted SLAM systems assuming the communication resources between the mobile device and the edge server to be unlimited, or relying on heuristics to choose the information to be transmitted to the edge.
Semi-Supervised Heterogeneous Graph Learning with Multi-level Data Augmentation
Differing from homogeneous graph, DA in heterogeneous graph has greater challenges: heterogeneity of information requires DA strategies to effectively handle heterogeneous relations, which considers the information contribution of different types of neighbors and edges to the target nodes.
A Dynamic Equivalent Method for PMSG-WTG Based Wind Farms Considering wind Speeds and Fault Severities
In order to reduce the complexity of simulation of power systems including large-scale wind farms, it is critical to develop dynamic equivalent methods for wind farms which are applicable to the expected contingency analysis.
Lightweight network towards real-time image denoising on mobile devices
In this paper, we identify the real bottlenecks that affect the CNN-based models' run-time performance on mobile devices: memory access cost and NPU-incompatible operations, and build the model based on these.
Probability-Dependent Gradient Decay in Large Margin Softmax
Based on the analysis results, we can provide evidence that the large margin Softmax will affect the local Lipschitz constraint of the loss function by regulating the probability-dependent gradient decay rate.
Rediscovery of Numerical Lüscher's Formula from the Neural Network
We present that by predicting the spectrum in discrete space from the phase shift in continuous space, the neural network can remarkably reproduce the numerical L\"uscher's formula to a high precision.
Rethinking Dimensionality Reduction in Grid-based 3D Object Detection
In MDRNet, the Spatial-aware Dimensionality Reduction (SDR) is designed to dynamically focus on the valuable parts of the object during voxel-to-BEV feature transformation.
Scatter Points in Space: 3D Detection from Multi-view Monocular Images
3D object detection from monocular image(s) is a challenging and long-standing problem of computer vision.
Evaluating Point Cloud from Moving Camera Videos: A No-Reference Metric
To tackle the challenge of point cloud quality assessment (PCQA), many PCQA methods have been proposed to evaluate the visual quality levels of point clouds by assessing the rendered static 2D projections.
An Interpretability Evaluation Benchmark for Pre-trained Language Models
To fill in the gap, we propose a novel evaluation benchmark providing with both English and Chinese annotated data.
Adaptive Assignment for Geometry Aware Local Feature Matching
The detector-free feature matching approaches are currently attracting great attention thanks to their excellent performance.
SepLUT: Separable Image-adaptive Lookup Tables for Real-time Image Enhancement
Image-adaptive lookup tables (LUTs) have achieved great success in real-time image enhancement tasks due to their high efficiency for modeling color transforms.
Ranked #5 on
Image Enhancement
on MIT-Adobe 5k
(PSNR on proRGB metric)
DavarOCR: A Toolbox for OCR and Multi-Modal Document Understanding
Compared with the previous opensource OCR toolbox, DavarOCR has relatively more complete support for the sub-tasks of the cutting-edge technology of document understanding.
Dynamic Low-Resolution Distillation for Cost-Efficient End-to-End Text Spotting
In this paper, to address this problem, we propose a novel cost-efficient Dynamic Low-resolution Distillation (DLD) text spotting framework, which aims to infer images in different small but recognizable resolutions and achieve a better balance between accuracy and efficiency.
Knowledge Distillation
Optical Character Recognition (OCR)
+1
Snow Mask Guided Adaptive Residual Network for Image Snow Removal
Recently, some methods have been proposed for snow removing, and most methods deal with snow images directly as the optimization object.
BYHE: A Simple Framework for Boosting End-to-end Video-based Heart Rate Measurement Network
Heart rate measuring based on remote photoplethysmography (rPPG) plays an important role in health caring, which estimates heart rate from facial video in a non-contact, less-constrained way.
Perceptual Quality Assessment for Fine-Grained Compressed Images
Nowadays, the mainstream full-reference (FR) metrics are effective to predict the quality of compressed images at coarse-grained levels (the bit rates differences of compressed images are obvious), however, they may perform poorly for fine-grained compressed images whose bit rates differences are quite subtle.
A Fine-grained Interpretability Evaluation Benchmark for Neural NLP
Based on this benchmark, we conduct experiments on three typical models with three saliency methods, and unveil their strengths and weakness in terms of interpretability.
Spot-adaptive Knowledge Distillation
Knowledge distillation (KD) has become a well established paradigm for compressing deep neural networks.
AdaInt: Learning Adaptive Intervals for 3D Lookup Tables on Real-time Image Enhancement
They adopt a sub-optimal uniform sampling point allocation, limiting the expressiveness of the learned LUTs since the (tri-)linear interpolation between uniform sampling points in the LUT transform might fail to model local non-linearities of the color transform.
Ranked #2 on
Photo Retouching
on MIT-Adobe 5k
Progressive Training of A Two-Stage Framework for Video Restoration
As a widely studied task, video restoration aims to enhance the quality of the videos with multiple potential degradations, such as noises, blurs and compression artifacts.
NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video: Dataset, Methods and Results
This challenge includes three tracks.
Pyramid Frequency Network with Spatial Attention Residual Refinement Module for Monocular Depth Estimation
In this work, a Pyramid Frequency Network(PFN) with Spatial Attention Residual Refinement Module(SARRM) is proposed to deal with the weak robustness of existing deep-learning methods.
An Intelligent End-to-End Neural Architecture Search Framework for Electricity Forecasting Model Development
In the first component, we introduce a theoretical function-preserving transformation of recurrent neural networks (RNN) to the literature for capturing the hidden temporal patterns within the time-series data.
DuReader_retrieval: A Large-scale Chinese Benchmark for Passage Retrieval from Web Search Engine
In this paper, we present DuReader_retrieval, a large-scale Chinese dataset for passage retrieval.
Applications of blockchain and artificial intelligence technologies for enabling prosumers in smart grids: A review
The blockchain and artificial intelligence (AI) are innovative technologies to fulfil these two factors, by which the blockchain provides decentralised trading platforms for energy markets and the AI supports the optimal operational control of power systems.
Guide Local Feature Matching by Overlap Estimation
Local image feature matching under large appearance, viewpoint, and distance changes is challenging yet important.
Resource allocation algorithm for MEC based on Deep Reinforcement Learning
In recent years, driven by the commercialization of the 6th Generation Communication Technology (6G), an increasing number of 6G devices connected to mobile networks produces computation-intensive tasks such as ultra-high-resolution video streaming, inter-active visual reality (VR) gaming, augmented reality (AR).
VR Viewport Pose Model for Quantifying and Exploiting Frame Correlations
The importance of the dynamics of the viewport pose, i. e., the location and the orientation of users' points of view, for virtual reality (VR) experiences calls for the development of VR viewport pose models.
DuQM: A Chinese Dataset of Linguistically Perturbed Natural Questions for Evaluating the Robustness of Question Matching Models
For this purpose, we create a Chinese dataset namely DuQM which contains natural questions with linguistic perturbations to evaluate the robustness of question matching models.
Feasibility Study of Neural ODE and DAE Modules for Power System Dynamic Component Modeling
In the context of high penetration of renewables, the need to build dynamic models of power system components based on accessible measurement data has become urgent.
Semi-supervised Domain Adaptation for Semantic Segmentation
We demonstrate that the proposed approach outperforms state-of-the-art methods on two common synthetic-to-real semantic segmentation benchmarks.
Multi-institutional Validation of Two-Streamed Deep Learning Method for Automated Delineation of Esophageal Gross Tumor Volume using planning-CT and FDG-PETCT
252 institution-1 patients had a treatment planning-CT (pCT) and a pair of diagnostic FDG-PETCT; 354 patients from other 3 institutions had only pCT.
Exploration of Artificial Intelligence-oriented Power System Dynamic Simulators
With the rapid development of artificial intelligence (AI), it is foreseeable that the accuracy and efficiency of dynamic analysis for future power system will be greatly improved by the integration of dynamic simulators and AI.
Multi-Modal Sarcasm Detection Based on Contrastive Attention Mechanism
In the past decade, sarcasm detection has been intensively conducted in a textual scenario.
DuTrust: A Sentiment Analysis Dataset for Trustworthiness Evaluation
Therefore, in order to systematically evaluate the factors for building trustworthy systems, we propose a novel and well-annotated sentiment analysis dataset to evaluate robustness and interpretability.
Multi-granularity for knowledge distillation
Considering the fact that students have different abilities to understand the knowledge imparted by teachers, a multi-granularity distillation mechanism is proposed for transferring more understandable knowledge for student networks.
Tree-Like Decision Distillation
Knowledge distillation pursues a diminutive yet well-behaved student network by harnessing the knowledge learned by a cumbersome teacher model.
Deep Switching State Space Model (DS$^3$M) for Nonlinear Time Series Forecasting with Regime Switching
Modern time series data often display complex nonlinear dependencies along with irregular regime-switching behaviors.
RtFPS: An Interactive Map that Visualizes and Predicts Wildfires in the US
Climate change has largely impacted our daily lives.
Integrated Communication and Navigation for Ultra-Dense LEO Satellite Networks: Vision, Challenges and Solutions
Next generation beyond 5G networks are expected to provide both Terabits per second data rate communication services and centimeter-level accuracy localization services in an efficient, seamless and cost-effective manner.
KDExplainer: A Task-oriented Attention Model for Explaining Knowledge Distillation
Knowledge distillation (KD) has recently emerged as an efficacious scheme for learning compact deep neural networks (DNNs).
Spectral Machine Learning for Pancreatic Mass Imaging Classification
Factors that influenced high performance of a well-designed integration of spectral learning and machine learning included: 1) use of eigenvectors corresponding to several of the largest eigenvalues of sample covariance matrix (spike eigenvectors) to choose input attributes in classification training, taking into account only the fundamental information of the raw images with less noise; 2) removal of irrelevant pixels based on mean-level spectral test to lower the challenges of memory capacity and enhance computational efficiency while maintaining superior classification accuracy; 3) adoption of state-of-the-art machine learning classification, gradient boosting and random forest.
BSTC: A Large-Scale Chinese-English Speech Translation Dataset
This corpus is expected to promote the research of automatic simultaneous translation as well as the development of practical systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Refining Language Models with Compositional Explanations
However, such a regularization technique lacks flexibility and coverage, since only importance scores towards a pre-defined list of features are adjusted, while more complex human knowledge such as feature interaction and pattern generalization can hardly be incorporated.
Deep Stochastic Volatility Model
We propose a deep stochastic volatility model (DSVM) based on the framework of deep latent variable models.
The Variational Bayesian Inference for Network Autoregression Models
We develop a variational Bayesian (VB) approach for estimating large-scale dynamic network models in the network autoregression framework.
Exclusive Topic Modeling
We propose an Exclusive Topic Modeling (ETM) for unsupervised text classification, which is able to 1) identify the field-specific keywords though less frequently appeared and 2) deliver well-structured topics with exclusive words.
Concentrated Document Topic Model
We propose a Concentrated Document Topic Model(CDTM) for unsupervised text classification, which is able to produce a concentrated and sparse document topic distribution.
Mask-based Data Augmentation for Semi-supervised Semantic Segmentation
Training a CNN to perform semantic segmentation requires a large amount of labeled data, where the production of such labeled data is both costly and labor intensive.
Self-Born Wiring for Neural Trees
Neural trees aim at integrating deep neural networks and decision trees so as to bring the best of the two worlds, including representation learning from the former and faster inference from the latter.
MANGO: A Mask Attention Guided One-Stage Scene Text Spotter
Recently end-to-end scene text spotting has become a popular research topic due to its advantages of global optimization and high maintainability in real applications.
Ranked #6 on
Text Spotting
on SCUT-CTW1500
Domain Adaptation on Semantic Segmentation for Aerial Images
In this paper, we propose a novel unsupervised domain adaptation framework to address domain shift in the context of aerial semantic image segmentation.
End-to-End Emotion-Cause Pair Extraction with Graph Convolutional Network
Emotion-cause pair extraction (ECPE), which aims at simultaneously extracting emotion-cause pairs that express emotions and their corresponding causes in a document, plays a vital role in understanding natural languages.
Ranked #6 on
Emotion-Cause Pair Extraction
on ECPE
Cascade Attentive Dropout for Weakly Supervised Object Detection
Weakly supervised object detection (WSOD) aims to classify and locate objects with only image-level supervision.
Dense U-net for super-resolution with shuffle pooling layer
By doing so, we effectively replace the handcrafted filter in the SISR pipeline with more lossy down-sampling filters specifically trained for each feature map, whilst also reducing the information loss of the overall SISR operation.
DEAL: Difficulty-aware Active Learning for Semantic Segmentation
To deal with this problem, we propose a semantic Difficulty-awarE Active Learning (DEAL) network composed of two branches: the common segmentation branch and the semantic difficulty branch.
Deep Reinforcement Learning-Based Dynamic Resource Management for Mobile Edge Computing in Industrial Internet of Things
In order to minimize the long-term average delay of the tasks, theoriginal problem is transformed into a Markov decision process (MDP).
Regularised Text Logistic Regression: Key Word Detection and Sentiment Classification for Online Reviews
Moreover, RTL identifies a small set of word features, corresponding to 3% for Restaurant and 20% for Hotel, which boosts working efficiency by allowing managers to drill down into a much smaller set of important customer reviews.
Probabilistic Forecasting for Daily Electricity Loads and Quantiles for Curve-to-Curve Regression
When applying to one day ahead forecasting for the French daily electricity load curves, PPC outperform several state-of-the-art predictive methods in terms of forecasting accuracy, coverage rate and average length of the predictive bands.
Methodology Applications
Automatic Crack Detection on Road Pavements Using Encoder Decoder Architecture
Finally, the hierarchical feature learning module is designed to obtain a multi-scale features from the high to low-level convolutional layers, which are integrated to predict pixel-wise crack detection.
DeepQTMT: A Deep Learning Approach for Fast QTMT-based CU Partition of Intra-mode VVC
In VVC, the quad-tree plus multi-type tree (QTMT) structure of coding unit (CU) partition accounts for over 97% of the encoding time, due to the brute-force search for recursive rate-distortion (RD) optimization.
DO-Conv: Depthwise Over-parameterized Convolutional Layer
Moreover, in the inference phase, the depthwise convolution is folded into the conventional convolution, reducing the computation to be exactly equivalent to that of a convolutional layer without over-parameterization.
Two-stage short-term wind power forecasting algorithm using different feature-learning models
Considering the overfitting issue, we propose a new moving window-based algorithm using a validation set in the first stage to update the training data in both stages with two different moving window processes. Experiments were conducted at three wind farms, and the results demonstrate that the model with single input multiple output structure obtains better forecasting accuracy compared to existing models.
GSTO: Gated Scale-Transfer Operation for Multi-Scale Feature Learning in Pixel Labeling
Existing CNN-based methods for pixel labeling heavily depend on multi-scale features to meet the requirements of both semantic comprehension and detail preservation.
Stochastic Sparse Subspace Clustering
State-of-the-art subspace clustering methods are based on self-expressive model, which represents each data point as a linear combination of other data points.
Ensemble of Deep Convolutional Neural Networks for Automatic Pavement Crack Detection and Measurement
In this paper, we propose an ensemble of convolutional neural networks (without a pooling layer) based on probability fusion for automated pavement crack detection and measurement.
MixTConv: Mixed Temporal Convolutional Kernels for Efficient Action Recogntion
To efficiently extract spatiotemporal features of video for action recognition, most state-of-the-art methods integrate 1D temporal convolution into a conventional 2D CNN backbone.
MeliusNet: Can Binary Neural Networks Achieve MobileNet-level Accuracy?
However, the binarization of weights and activations leads to feature maps of lower quality and lower capacity and thus a drop in accuracy compared to traditional networks.
Deep SCNN-based Real-time Object Detection for Self-driving Vehicles Using LiDAR Temporal Data
In this paper, we integrate spiking convolutional neural network (SCNN) with temporal coding into the YOLOv2 architecture for real-time object detection.
Portfolio liquidation under transient price impact -- theoretical solution and implementation with 100 NASDAQ stocks
We derive an explicit solution for deterministic market impact parameters in the Graewe and Horst (2017) portfolio liquidation model.
Single Image Super Resolution based on a Modified U-net with Mixed Gradient Loss
To solve this problem, the mixed gradient error, which is composed by MSE and a weighted mean gradient error, is proposed in this work and applied to a modified U-net network as the loss function.
CAUnLP at NLP4IF 2019 Shared Task: Context-Dependent BERT for Sentence-Level Propaganda Detection
This paper presents the sys- tem of our participation in the sentence-level subtask of the propaganda detection shared task.
Temporal-Coded Deep Spiking Neural Network with Easy Training and Robust Performance
Spiking neural network (SNN) is interesting both theoretically and practically because of its strong bio-inspiration nature and potentially outstanding energy efficiency.
Where does active travel fit within local community narratives of mobility space and place?
The analysis uncovers the local mobility culture, embedded norms and values associated with acceptance of active travel modes in different communities.
Cultural Vocal Bursts Intensity Prediction
Sentiment Analysis
Segmentation of Levator Hiatus Using Multi-Scale Local Region Active contours and Boundary Shape Similarity Constraint
In this paper, a multi-scale framework with local region based active contour and boundary shape similarity constraint is proposed for the segmentation of levator hiatus in ultrasound images.
M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network
Finally, we gather up the decoder layers with equivalent scales (sizes) to develop a feature pyramid for object detection, in which every feature map consists of the layers (features) from multiple levels.
Ranked #145 on
Object Detection
on COCO test-dev
Joint Learning for Emotion Classification and Emotion Cause Detection
We present a neural network-based joint approach for emotion classification and emotion cause detection, which attempts to capture mutual benefits across the two sub-tasks of emotion analysis.
Multi-Turn Response Selection for Chatbots with Deep Attention Matching Network
Human generates responses relying on semantic and functional dependencies, including coreference relation, among dialogue elements and their context.
Ranked #6 on
Conversational Response Selection
on RRS
C-arm Tomographic Imaging Technique for Nephrolithiasis and Detection of Kidney Stones
Our C-arm tomographic technique provides a series of two dimensional (2D) images with a single scan over 40o view angle.
Corpus Fusion for Emotion Classification
Machine learning-based methods have obtained great progress on emotion classification.
