Is this you? Claim this profile to correct it, add a bio and choose the work people see first.
Claim this profileAcademic lineage
Possible advisorsa guess from early papers, not confirmed
- Rong ChenSuggested from co-authorship
Is this you? Claim this profile to confirm or dismiss it.
Works189 from public data
- Bayesian Loss for Crowd Count Estimation With Point Supervision671
This work proposes Bayesian loss, a novel loss function which constructs a density contribution probability model from the point annotations, and outperforms previous best approaches by a large margin on the latest and largest UCF-QNRF dataset.
- Few-Shot Class-Incremental Learning587
This paper proposes the TOpology-Preserving knowledge InCrementer (TOPIC) framework, which mitigates the forgetting of the old classes by stabilizing NG's topology and improves the representation learning for few-shot new classes by growing and adapting NG to new training samples.
- Infrared-Visible Cross-Modal Person Re-Identification with an X Modality448
An X-Infrared-Visible (XIV) ReID cross-modal learning framework is proposed, which achieves an absolute gain of over 7% in terms of rank 1 and mAP even compared with the latest state-of-the-art methods.
- S-Prompts Learning with Pre-trained Transformers: An Occam's Razor for Domain Incremental Learning401
This paper proposes one simple paradigm (named as S-Prompting) and two concrete approaches to highly reduce the forgetting degree in one of the most typical continual learning scenarios, i.e., domain increment learning (DIL).
- Learning Graph Convolutional Network for Skeleton-Based Human Action Recognition by Neural Searching371
The first automatically designed GCN for skeleton-based action recognition is proposed and enriched by providing multiple dynamic graph modules after fully exploring the spatial-temporal correlations between nodes, and a sampling- and memory-efficient evolution strategy is proposed to search an optimal architecture for this task.
- Remote Heart Rate Measurement From Highly Compressed Facial Videos: An End-to-End Deep Learning Solution With Video Enhancement341
Comprehensive experiments are performed to show that the proposed method not only achieves superior performance on compressed videos with high-quality videos pair, but also generalizes well on novel data with only compressed videos available, which implies the promising potential for real-world applications.
- Spontaneous facial micro-expression analysis using Spatiotemporal Completed Local Quantized Patterns303
Experimental results demonstrate that STCLQP achieves a substantial improvement for analyzing facial micro-expressions, and an efficient vector quantization and discriminative codebook selection is proposed to make LBP-TOP more discrim inative and compact.
- Spatiotemporal Recurrent Convolutional Networks for Recognizing Spontaneous Micro-Expressions290
A novel deep recurrent convolutional networks based micro-expression recognition approach, capturing the spatiotemporal deformations of micro- expression sequence and optimized by an end-to-end manner and obviates manual feature design is proposed.
- Boosting Crowd Counting via Multifaceted Attention257
A Multifaceted Attention Network (MAN) is proposed to improve transformer models in local spatial relation encoding and an Instance Attention mechanism is provided to focus on the most important instances dynamically during training.
- Few-Shot Class-Incremental Learning via Relation Knowledge Distillation230
This paper proposes the exemplar relation distillation incremental learning framework to balance the tasks of old-knowledge preserving and new-knowledge adaptation, and outperforms other state-of-the-art class-incremental learning methods on the CIFAR100, miniImageNet, and CUB200 datasets.
- 213
- Topology-Preserving Class-Incremental Learning212
A novel topology-preserving class-incremental learning (TPCIL) framework that penalizes the changes of EHG’s neighboring relationships during incremental learning phases and demonstrates the power of the TPCIL for continuously learning new classes with less forgetting.
- A review of recent advances in visual speech decoding187
A detailed review of recent advances in visual speech decoding, focusing on the important questions asked by researchers and summarize the recent studies that attempt to answer them, and providing details of audio-visual speech databases.
- Image-to-image Translation via Hierarchical Style Disentanglement165
Hierarchical Style Disentanglement is proposed, which organizes the labels into a hierarchical tree structure, in which independent tags, exclusive attributes, and disentangled styles are allocated from top to bottom.
- 159
- Short and Long Range Relation Based Spatio-Temporal Transformer for Micro-Expression Recognition158
A comprehensive evaluation of the proposed spatio-temporal transformer architecture shows that the proposed approach consistently outperforms the state of the art, and is the first framework in the published literature on micro-expression recognition to achieve the unweighted F1-score greater than 0.9.
- 135
- Recurrent Convolutional Neural Network Regression for Continuous Pain Intensity Estimation in Video127
A real-time regression framework based on the recurrent convolutional neural network for automatic frame-level pain intensity estimation that can predict the pain intensity of each frame by considering a sufficiently large historical frames while limiting the scale of the parameters within the model.
- Universal Perturbation Attack Against Image Retrieval122
This paper proposes a novel method to generate retrieval-against UAP to break the neighbourhood relationships of image features via degrading the corresponding ranking metric, and proposes a multi-scale random resizing scheme and a ranking distillation strategy.
- 120
- Learning to Count via Unbalanced Optimal Transport113
This paper forms crowd counting as a measure regression problem to minimize the distance between two measures with different supports and unequal total mass, and adopts the unbalanced optimal transport distance to quantify the discrepancy between predicted density maps and point annotations.
- Dynamic texture and scene classification by transferring deep image features101
Inspired by the success of deep structures in image classification, a well-trained Convolutional Neural Network is applied as a feature extractor to extract mid-level features from each frame, and then form the video-level representation by concatenating the first and the second order statistics over the mid- level features.
- Isolated Sign Language Recognition with Grassmann Covariance Matrices94
This article proposes a covariance matrix--based representation to naturally fuse information from multimodal sources to utilize long-term dynamics over an isolated sign sequence, and demonstrates that the proposed method outperforms the state-of-the-art methods both in accuracy and computational cost.
- A Continual Deepfake Detection Benchmark: Dataset, Methods, and Essentials91
A continual deepfake detection benchmark (CDDB) over a new collection of deepfakes from both known and unknown generative models is suggested, which is clearly more challenging than the existing benchmarks, and offers a suitable evaluation avenue to the future research.
- 81
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 2026-10-15. 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.
Report an error
Wrong papers, two people merged into one, or a profile that should not be here? Tell us and we will fix or hide it.