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- WebFace260M: A Benchmark Unveiling the Power of Million-Scale Deep Face Recognition375
A distributed framework is developed to train face recognition models efficiently without tampering with the performance, and comprehensive baselines are established on a rich-attribute test set under FRUITS-100ms/500ms/1000ms protocol.
- Sub-center ArcFace: Boosting Face Recognition by Large-Scale Noisy Web Faces305
The intra-class constraint of ArcFace is relaxed to improve the robustness to label noise and recapture intra-compactness, decrease the influence from noise, and achieve comparable performance compared to ArcFace trained on the manually cleaned dataset.
- Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning298
This paper introduces an intermediate class to avoid directly estimating the noisy class posterior of the transition matrix, and introduces the dual $T-estimator for estimating transition matrices, leading to better classification performances.
- 224
- UV-GAN: Adversarial Facial UV Map Completion for Pose-Invariant Face Recognition215
A framework for training Deep Convolutional Neural Network (DCNN) to complete the facial UV map extracted from in-the-wild images, and devise a meticulously designed architecture that combines local and global adversarial DCNNs to learn an identity-preserving facial UV completion model.
- WiLoR: End-to-end 3D Hand Localization and Reconstruction in-the-wild204
This work presents a data-driven pipeline for efficient multi-hand reconstruction in the wild, composed of a real-time fully convolutional hand localization and a high-fidelity transformer-based 3D hand reconstruction model.
- 169
- Spatio-temporal convolutional features with nested LSTM for facial expression recognition161
A novel end-to-end architecture termed Spatio-Temporal Convolutional features with Nested LSTM (STC-NLSTM), which learns the muti-level appearance features and temporal dynamics of facial expressions in a joint fashion is proposed.
- 149
- Deep Polynomial Neural Networks142
Three tensor decompositions that significantly reduce the number of parameters and show how they can be efficiently implemented by hierarchical neural networks are introduced.
- 135
- Killing Two Birds with One Stone: Efficient and Robust Training of Face Recognition CNNs by Partial FC128
A sparsely updating variant of the FC layer, named Partial FC (PFC), where all class centers are still maintained throughout the whole training process, but only a subset is selected and updated in each iteration, resulting in a dramatically reduced computing requirement and frequency of passive update on tail class centers.
- Masked Face Recognition Challenge: The InsightFace Track Report120
This workshop organizes Masked Face Recognition (MFR) challenge1 and focuses on bench-marking deep face recognition methods under the existence of facial masks, and builds up an online model testing system, which can give a comprehensive evaluation of face recognition models.
- Joint Multi-View Face Alignment in the Wild103
This paper proposes the first, to the best of the knowledge, joint multi-view convolutional network to handle large pose variations across faces in-the-wild, and elegantly bridge face detection and facial landmark localization tasks.
- The Menpo Benchmark for Multi-pose 2D and 3D Facial Landmark Localisation and Tracking101
An elaborate semi-automatic methodology is introduced for providing high-quality annotations for both the Menpo 2D and Menpo 3D benchmarks, two new datasets for multi-pose 2d and 3D facial landmark localisation and tracking.
- 100
- ALIP: Adaptive Language-Image Pre-training with Synthetic Caption99
An Adaptive Language-Image Pre-training (ALIP), a bi-path model that integrates supervision from both raw text and synthetic caption that achieves state-of-the-art performance on multiple downstream tasks including zero-shot image-text retrieval and linear probe is proposed.
- CFPL-FAS: Class Free Prompt Learning for Generalizable Face Anti-Spoofing93
This work proposes a novel Class Free Prompt Learning (CFPL) paradigm for DG FAS, which utilizes two lightweight transformers, namely Content Q-Former (CQF) and Style Q-Former (SQF), to learn the different semantic prompts conditioned on content and style features by using a set of learnable query vectors.
- Breaking the Modality Barrier: Universal Embedding Learning with Multimodal LLMs88
UniME (Universal Multimodal Embedding), a novel two-stage framework that leverages MLLMs to learn discriminative representations for diverse downstream tasks, and introduces hard negative enhanced instruction tuning to further advance discriminative representation learning.
- Boosting Object Detection with Zero-Shot Day-Night Domain Adaptation85
This work proposes to boost low-light object detection with zero-shot day-night domain adaptation, which aims to generalize a detector from well-lit scenarios to low-light ones without requiring real low-light data.
- Redesigning Multi-Scale Neural Network for Crowd Counting84
A new relative local counting loss based on relative count differences among hard-predicted local regions in an image, which proves to be complementary to the conventional absolute error loss on the density map, is introduced.
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 2026-10-14. 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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