27 citations · 106 across the 16 of their papers we have counts for
16 papers
Hierarchical Visual Categories Modeling: A Joint Representation Learning and Density Estimation Framework for Out-of-Distribution Detection
Jinglun Li, Xinyu Zhou, Pinxue Guo +4
Detecting out-of-distribution inputs for visual recognition models has become critical in safe deep learning. This paper proposes a novel hierarchical visual category modeling sche…
A language-inspired machine learning approach for solving strongly correlated problems with dynamical mean-field theory
Hovan Lee, Zelong Zhao, George Booth +2
We present SCALINN -- Strongly Correlated Approach with Language Inspired Neural Network -- as a method for solving the Anderson impurity model and reducing the computational cost…
Improving Empathetic Dialogue Generation by Dynamically Infusing Commonsense Knowledge
Hua Cai, Xuli Shen, Qing Xu +5
In empathetic conversations, individuals express their empathy towards others. Previous work has mainly focused on generating empathetic responses by utilizing the speaker's emotio…
ColoristaNet for Photorealistic Video Style Transfer
Xiaowen Qiu, Ruize Xu, Boan He +3
Photorealistic style transfer aims to transfer the artistic style of an image onto an input image or video while keeping photorealism. In this paper, we think it's the summary stat…
RankDNN: Learning to Rank for Few-shot Learning
Qianyu Guo, Hongtong Gong, Xujun Wei +4
This paper introduces a new few-shot learning pipeline that casts relevance ranking for image retrieval as binary ranking relation classification. In comparison to image classifica…
FERV39k: A Large-Scale Multi-Scene Dataset for Facial Expression Recognition in Videos
Yan Wang, Yixuan Sun, Yiwen Huang +5
Current benchmarks for facial expression recognition (FER) mainly focus on static images, while there are limited datasets for FER in videos. It is still ambiguous to evaluate whet…
A Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advances
Yan Wang, Wei Song, Wei Tao +8
Affective computing plays a key role in human-computer interactions, entertainment, teaching, safe driving, and multimedia integration. Major breakthroughs have been made recently…
Attribute Surrogates Learning and Spectral Tokens Pooling in Transformers for Few-shot Learning
Yangji He, Weihan Liang, Dongyang Zhao +4
This paper presents new hierarchically cascaded transformers that can improve data efficiency through attribute surrogates learning and spectral tokens pooling. Vision transformers…
Multi-scale Matching Networks for Semantic Correspondence
Dongyang Zhao, Ziyang Song, Zhenghao Ji +3
Deep features have been proven powerful in building accurate dense semantic correspondences in various previous works. However, the multi-scale and pyramidal hierarchy of convoluti…
GraphFPN: Graph Feature Pyramid Network for Object Detection
Gangming Zhao, Weifeng Ge, Yizhou Yu
Feature pyramids have been proven powerful in image understanding tasks that require multi-scale features. State-of-the-art methods for multi-scale feature learning focus on perfor…
Label-PEnet: Sequential Label Propagation and Enhancement Networks for Weakly Supervised Instance Segmentation
Weifeng Ge, Sheng Guo, Weilin Huang +1
Weakly-supervised instance segmentation aims to detect and segment object instances precisely, given imagelevel labels only. Unlike previous methods which are composed of multiple…
Weakly Supervised Complementary Parts Models for Fine-Grained Image Classification from the Bottom Up
Weifeng Ge, Xiangru Lin, Yizhou Yu
Given a training dataset composed of images and corresponding category labels, deep convolutional neural networks show a strong ability in mining discriminative parts for image cla…
Deep Metric Learning with Hierarchical Triplet Loss
Weifeng Ge, Weilin Huang, Dengke Dong +1
We present a novel hierarchical triplet loss (HTL) capable of automatically collecting informative training samples (triplets) via a defined hierarchical tree that encodes global c…
Image Super-Resolution via Deterministic-Stochastic Synthesis and Local Statistical Rectification
Weifeng Ge, Bingchen Gong, Yizhou Yu
Single image superresolution has been a popular research topic in the last two decades and has recently received a new wave of interest due to deep neural networks. In this paper,…
Multi-Evidence Filtering and Fusion for Multi-Label Classification, Object Detection and Semantic Segmentation Based on Weakly Supervised Learning
Weifeng Ge, Sibei Yang, Yizhou Yu
Supervised object detection and semantic segmentation require object or even pixel level annotations. When there exist image level labels only, it is challenging for weakly supervi…
Borrowing Treasures from the Wealthy: Deep Transfer Learning through Selective Joint Fine-tuning
Weifeng Ge, Yizhou Yu
Deep neural networks require a large amount of labeled training data during supervised learning. However, collecting and labeling so much data might be infeasible in many cases. In…