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- Yu QiaoSuggested from co-authorship
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Works136 from public data
- Region Attention Networks for Pose and Occlusion Robust Facial Expression Recognition933
A novel Region Attention Network (RAN), to adaptively capture the importance of facial regions for occlusion and pose variant FER, and a region biased loss to encourage high attention weights for the most important regions.
- Suppressing Uncertainties for Large-Scale Facial Expression Recognition724
Self-Cure Network suppresses the uncertainty from two different aspects: 1) a self-attention mechanism over FER dataset to weight each sample in training with a ranking regularization, and 2) a careful relabeling mechanism to modify the labels of these samples in the lowest-ranked group.
- Bag of visual words and fusion methods for action recognition: Comprehensive study and good practice653
A comprehensive study of all steps in BoVW and different fusion methods is provided, and a simple yet effective representation is proposed, called hybrid supervector, by exploring the complementarity of different BoVW frameworks with improved dense trajectories.
- Multi-region Two-Stream R-CNN for Action Detection395
A multi-region two-stream R-CNN model for action detection in realistic videos that links frame-level detections with the Viterbi algorithm, and temporally localize an action with the maximum subarray method is proposed.
- Action Recognition with Stacked Fisher Vectors388
Experimental results demonstrate the effectiveness of SFV, and the combination of the traditional FV and SFV outperforms state-of-the-art methods on these datasets with a large margin.
- Emotion-LLaMA: Multimodal Emotion Recognition and Reasoning with Instruction Tuning298
The MERR dataset is introduced, containing 28,618 coarse-grained and 4,487 fine-grained annotated samples across diverse emotional categories and Emotion-LLaMA, a model that seamlessly integrates audio, visual, and textual inputs through emotion-specific encoders is proposed, significantly enhances both emotional recognition and reasoning capabilities.
- Attention-Driven Dynamic Graph Convolutional Network for Multi-label Image Recognition265
An Attention-Driven Dynamic Graph Convolutional Network (ADD-GCN) to dynamically generate a specific graph for each image to model the relation of content-aware category representations that are generated by a Semantic Attention Module (SAM).
- Visual Compositional Learning for Human-Object Interaction Detection239
A deep Visual Compositional Learning (VCL) framework is devised, which is a simple yet efficient framework to effectively address the problem of human-Object interaction detection and largely alleviates the long-tail distribution problem and benefits low-shot or zero-shot HOI detection.
- AnoPCN227
AnoPCN unifies reconstruction and prediction methods in an end-to-end framework, and it achieves state-of-the-art performance with better prediction results and larger regularity score gaps on three benchmark datasets including ShanghaiTech Campus, CUHK Avenue, and UCSD Ped2.
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- Frame Attention Networks for Facial Expression Recognition in Videos212
This paper proposes the Frame Attention Networks (FAN)1, to automatically highlight some discriminative frames in an end-to-end framework and shows superior performance compared to other CNN based methods and achieves state-of-the-art performance on CK+.
- Affordance Transfer Learning for Human-Object Interaction Detection144
An affordance transfer learning approach is introduced to jointly detect HOIs with novel object and recognize affordances, and is capable of inferring the affordances of novel objects from known affordance representations.
- Detecting Human-Object Interaction via Fabricated Compositional Learning139
A novel HOI compositional learning framework is devised, termed as Fabricated Compositional Learning (FCL), to address the problem of open long-tailed HOI detection, which introduces an object fabricator to generate effective object representations, and then combines verbs and fabricated objects to compose new HOI samples.
- Frankenstein: Learning Deep Face Representations Using Small Data136
This paper proposes a method to generate very large training data sets of synthetic images by compositing real face images in a given data set and shows that this method enables to learn models from as few as 10 000 training images, which perform on par with models trained from 500 000 images.
- From Large Language Models to Large Multimodal Models: A Literature Review114
This paper provides a summary of both LLMs and LMMs from a unified perspective, makes an analysis of the development status of large-scale models in the view of globalization, and offers potential research directions for large-scale models.
- AffectGPT: A New Dataset, Model, and Benchmark for Emotion Understanding with Multimodal Large Language Models91
This work establishes a new benchmark for MLLM-based emotion understanding with a novel dataset (MER-Caption) and a new model (AffectGPT), and introduces the AffectGPT model, designed with pre-fusion operations to enhance multimodal integration.
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- Deep Recurrent Multi-instance Learning with Spatio-temporal Features for Engagement Intensity Prediction78
This paper elaborates the winner approach for engagement intensity prediction in the EmotiW Challenge 2018, and builds a multi-modal regression model based on multi-instance mechanism as well as LSTM.
- Exploring Emotion Features and Fusion Strategies for Audio-Video Emotion Recognition74
The approaches in EmotiW 2019, which mainly explores emotion features and feature fusion strategies for audio and visual modality, are described and several facial features with different CNN models and different emotion pretrained strategies are evaluated.
- Group emotion recognition with individual facial emotion CNNs and global image based CNNs74
This paper presents the approach for group-level emotion recognition in the Emotion Recognition in the Wild Challenge 2017, based on two types of Convolutional Neural Networks, namely individual facial emotion CNNs and global image based CNNs.
- Boosting VLAD with Supervised Dictionary Learning and High-Order Statistics69
A high-order VLAD (H-VLAD) for visual recognition is proposed, which leverages two kinds of high- order statistics in the VL AD-like framework, namely diagonal covariance and skewness and allows for efficient computation.
- Cascade Attention Networks For Group Emotion Recognition with Face, Body and Image Cues46
This paper presents the approach for group-level emotion recognition sub-challenge in the EmotiW 2018 and proposes a cascade attention network for the face cue in images to generate a global representation based on all faces.
- Action and Gesture Temporal Spotting with Super Vector Representation46
This paper proposes an action and gesture spotting system, which is mainly composed of three steps: temporal segmentation, clip classification, and post processing, and uses some post processing techniques to void the detection of false positives.
- Exploring Motion Boundary based Sampling and Spatial-Temporal Context Descriptors for Action Recognition45
A motion boundary based dense sampling strategy is introduced, which greatly reduces the number of valid trajectories while preserves the discriminative power, and a set of new descriptors which describe the spatial-temporal context of motion trajectories are developed.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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