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Claim this profileWorks154 from public data
- 2,415
- MemNet: A Persistent Memory Network for Image Restoration1,878
A very deep persistent memory network (MemNet) is proposed that introduces a memory block, consisting of a recursive unit and a gate unit, to explicitly mine persistent memory through an adaptive learning process.
- CurricularFace: Adaptive Curriculum Learning Loss for Deep Face Recognition676
This work proposes a novel Adaptive Curriculum Learning loss (CurricularFace) that embeds the idea of curriculum learning into the loss function to achieve a novel training strategy for deep face recognition, which mainly addresses easy samples in the early training stage and hard ones in the later stage.
- FSRNet: End-to-End Learning Face Super-Resolution with Facial Priors580
A novel deep end-to-end trainable Face Super-Resolution Network (FSRNet), which makes use of the geometry prior, i.e., facial landmark heatmaps and parsing maps, to super-resolve very low-resolution face images without well-aligned requirement, is presented.
- DSFD: Dual Shot Face Detector487
A novel detection network named Dual Shot face Detector (DSFD) is proposed which inherits the architecture of SSD and introduces a Feature Enhance Module (FEM) for transferring the original feature maps to extend the single shot detector to dual shot detector.
- Rethinking Counting and Localization in Crowds: A Purely Point-Based Framework436
This paper proposes a purely point-based framework for joint crowd counting and individual localization, called Point to Point Network (P2PNet), and proposes a new metric, called density Normalized Average Precision (nAP), to provide more comprehensive and more precise performance evaluation.
- Chained-Tracker: Chaining Paired Attentive Regression Results for End-to-End Joint Multiple-Object Detection and Tracking382
The two major novelties: chained structure and paired attentive regression, make CTracker simple, fast and effective, setting new MOTA records on MOT16 and MOT17 challenge datasets (67.6 and 66.6, respectively), without relying on any extra training data.
- 368
- Nuclear Norm Based Matrix Regression with Applications to Face Recognition with Occlusion and Illumination Changes359
This paper presents a two-dimensional image-matrix-based error model, namely, nuclear norm based matrix regression (NMR), for face representation and classification, and develops a fast ADMM algorithm to solve the approximate NMR model.
- Learning Salient Boundary Feature for Anchor-free Temporal Action Localization329
This paper proposes the first purely anchor-free temporal localization method, which is both efficient and effective, and beats all anchor-based and actionness-guided methods with a remarkable margin on THUMOS14, achieving state-of-the-art results, and comparable ones on ActivityNet v1.3.
- TEINet: Towards an Efficient Architecture for Video Recognition275
The proposed TEINet can achieve a good recognition accuracy on these datasets but still preserve a high efficiency, and is able to capture temporal structure flexibly and effectively, but also efficient for model inference.
- IFRNet: Intermediate Feature Refine Network for Efficient Frame Interpolation232
This work devise an efficient encoder-decoder based network, termed IFRNet, for fast in-termediate frame synthesizing, which first extracts pyramid features from given inputs, and then refines the bilateral in-Termediate flow fields together with a powerful intermedi-ate feature until generating the desired output.
- Person Search via a Mask-Guided Two-Stream CNN Model230
A simple yet effective re-ID method is proposed, which models foreground person and original image patches individually, and obtains enriched representations from two separate CNN streams, and achieves mAP of 83.0% on the standard person search benchmark datasets.
- Fast Learning of Temporal Action Proposal via Dense Boundary Generator228
An efficient and unified framework to generate temporal action proposals named Dense Boundary Generator (DBG), which draws inspiration from boundary-sensitive methods and implements boundary classification and action completeness regression for densely distributed proposals.
- Learning by Analogy: Reliable Supervision From Transformations for Unsupervised Optical Flow Estimation218
This work twists the general unsupervised learning pipeline by running another forward pass with transformed data from augmentation, along with using transformed predictions of original data as the self-supervision signal, and introduces a lightweight network with multiple frames by a highly-shared flow decoder.
- 216
- To Choose or to Fuse? Scale Selection for Crowd Counting209
This paper proposes a Scale-Adaptive Selection Network (SASNet), which automatically learns the internal correspondence between the scales and the feature levels, and considers the predictions from other feature levels via weighted average, which helps to mitigate the gap between discrete feature levels and continuous scale variation.
- NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results191
The NTIRE 2020 challenge addresses the real world setting, where paired true high and low-resolution images are unavailable, and the ultimate goal is to achieve the best perceptual quality, evaluated using a human study.
- 177
- HifiFace: 3D Shape and Semantic Prior Guided High Fidelity Face Swapping173
This work proposes 3D shape-aware identity to control the face shape with the geometric supervision from 3DMM and 3D face reconstruction method and introduces the Semantic Facial Fusion module to optimize the combination of encoder and decoder features and make adaptive blending, which makes the results more photo-realistic.
- Learning Multi-Granular Hypergraphs for Video-Based Person Re-Identification162
This work proposes a novel graph-based framework, namely Multi-Granular Hypergraph (MGH), to pursue better representational capabilities by modeling spatiotemporal dependencies in terms of multiple granularities to enhance the overall video representation.
- Face Anti-Spoofing via Disentangled Representation Learning155
A novel perspective of face anti-spoofing is proposed that disentangles the liveness features and content features from images, and theLiveness features is further used for classification, and a Convolutional Neural Network architecture is put forward.
- 130
- Generalizable Representation Learning for Mixture Domain Face Anti-Spoofing127
This work designs a domain feature based on Instance Normalization and proposes a domain representation learning module (DRLM) to extract discriminative domain features for clustering and designs a generalizable face anti-spoofing with meta-learning to enhance the interpretability through visualization.
- 115
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 2026-10-13. 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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