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- 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.
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- 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.
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- 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.
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- 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.
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- Dual Reweighting Domain Generalization for Face Presentation Attack Detection106
A novel Dual Reweighting Domain Generalization (DRDG) framework which iteratively reweights the relative importance between samples to further improve the generalization.
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- DIRL: Domain-Invariant Representation Learning for Generalizable Semantic Segmentation99
The domain-invariant representation is constructed which suppresses the effect of the domain-specific style on the quality and correlation of the features and can enhance the robustness of various backbone networks with little computational cost.
- PortraitBooth: A Versatile Portrait Model for Fast Identity-Preserved Personalization96
Por-traitBooth leverages subject embeddings from aface recognition model for personalized image generation without fine-tuning and eliminates computational overhead and mitigates identity distortion, and introduces dynamic identity preservation strategy, which ensures close resemblance to the original image identity.
- NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results94
The NTIRE 2022 challenge was to super-resolve an input image with a magnification factor of ×4 based on pairs of low and corresponding high resolution images and the aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics.
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- A Survey on Benchmarks of Multimodal Large Language Models90
A comprehensive review of 200 benchmarks and evaluations for MLLMs, focusing on perception and understanding, recognition and reasoning, specific domains, key capabilities, and other modalities, concludes that evaluation should be regarded as a crucial discipline to support the development of MLLMs better.
- Towards Highly Accurate and Stable Face Alignment for High-Resolution Videos76
A Fractional Heatmap Regression (FHR) for high-resolution video-based face alignment and a novel stabilization loss that contains two terms to address time delay and non-smooth issues, respectively are proposed.
- Designing One Unified Framework for High-Fidelity Face Reenactment and Swapping74
This paper proposes an effective end-to-end unified framework to achieve both face reenactment and swapping, and sufficiently transfers identity and attribute based on learned disentangled representations to generate high-fidelity faces.
- Adversarial Semantic Data Augmentation for Human Pose Estimation69
Semantic Data Augmentation (SDA), a method that augments images by pasting segmented body parts with various semantic granularity, is proposed, which exploits a generative network to dynamiclly predict tailored pasting configuration.
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- LCTR: On Awakening the Local Continuity of Transformer for Weakly Supervised Object Localization65
A novel framework built upon the transformer, termed LCTR (Local Continuity TRansformer), which targets at enhancing the local perception capability of global features among long-range feature dependencies and a relational patch-attention module (RPAM), which considers cross-patch information on a global basis.
- Improving Face Recognition from Hard Samples via Distribution Distillation Loss63
This work proposes a novel Distribution Distillation Loss to narrow the performance gap between easy and hard samples, which is a simple, effective and generic for various types of facial variations.
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- Deep hierarchical guidance and regularization learning for end-to-end depth estimation60
Comprehensive evaluations over three public benchmark datasets well demonstrate the state-of-the-art performance of the proposed HGR framework for end-to-end monocular depth estimation.
- SESR: Single Image Super Resolution with Recursive Squeeze and Excitation Networks60
This work proposed a novel single image super resolution method via recursive squeeze and excitation networks (SESR), which is proved to be above the state-of-the-art methods in terms of speed and accuracy.
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- ColorFormer: Image Colorization via Color Memory Assisted Hybrid-Attention Transformer57
Experimental results show that the proposed automatic image colorization method via color memory assisted hybrid-attention transformer can generate more realistic and semantically matched color images compared with state-of-the-art methods.
- Nuclear-L1 norm joint regression for face reconstruction and recognition with mixed noise54
Two nuclear-L 1 norm joint matrix regression (NL 1 R) models for face recognition with mixed noise, which are derived by using MAP (maximum a posteriori probability estimation), which can be solved by the alternating direction method of multipliers (ADMM).
- High-Fidelity Generalized Emotional Talking Face Generation with Multi-Modal Emotion Space Learning51
This paper proposes a more flexible and generalized framework for emotional talking face generation and uses an Aligned Multi-modal Emotion encoder to embed the text, image, and audio emotion modality into a unified space, which inherits rich semantic prior from CLIP.
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- ASFD: Automatic and Scalable Face Detector49
This work proposes to search an effective FAE architecture, termed AutoFAE by a differentiable architecture search, which outperforms all existing FAE modules in face detection with a considerable margin, and builds a supernet, which automatically obtains a family of detectors under the different complexity constraints.
- FAN: Feature Adaptation Network for Surveillance Face Recognition and Normalization49
This paper proposes a Feature Adaptation Network (FAN) to jointly perform surveillance FR and normalization, and proposes a random scale augmentation scheme to learn resolution robust identity features, with advantages over previous fixed size augmentation.
- Context-Aware Image Inpainting with Learned Semantic Priors48
A context-aware image inpainting model, which adaptively integrates global semantics and local features in a unified image generator that achieves the state of the art on Places2, CelebA, and Paris StreetView datasets is proposed.
- Color-wise Attention Network for Low-light Image Enhancement48
A color-wise attention network (CWAN) is proposed for low-light image enhancement based on convolutional neural networks, motivated by the human visual system when looking at dark images, that learns an end-to-end mapping between low- light and enhanced images.
- Uniformity in Heterogeneity: Diving Deep into Count Interval Partition for Crowd Counting47
This work proposes a novel count interval partition criterion called Uniform Error Partition (UEP), which always keeps the expected counting error contributions equal for all intervals to minimize the prediction risk, and proposes a simple yet effective model termed UEPNet, which achieves state-of-the-art performance on several challenging datasets.
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- SiamRCR: Reciprocal Classification and Regression for Visual Object Tracking42
A novel siamese tracking algorithm called SiamRCR is proposed, addressing this problem with a simple, light and effective solution, and builds reciprocal links between classification and regression branches, which can dynamically re-weight their losses for each positive sample.
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- Region-Aware Text-to-Image Generation via Hard Binding and Soft Refinement39
RAG is a Regional-Aware text-to-image Generation method conditioned on regional descriptions for precise layout composition that achieves superior performance over attribute binding and object relationship than previous tuning-free methods.
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- NTIRE 2021 Challenge on Video Super-Resolution36
This paper presents evaluation results from two competition tracks as well as the proposed solutions to the NTIRE 2021 Challenge on Video Super-Resolution, and develops conventional video SR methods focusing on the restoration quality.
- Learning to Aggregate and Personalize 3D Face from In-the-Wild Photo Collection36
A novel Learning to Aggregate and Personalize (LAP) framework for unsupervised robust 3D face modeling that recovers superior or competitive face shape and texture compared with state-of-the-art (SOTA) methods with or without prior and supervision.
- Temporal Distinct Representation Learning for Action Recognition35
This paper designs a sequential channel filtering mechanism, i.e., Progressive Enhancement Module (PEM), to excite the discriminative channels of features from different frames step by step to avoid repeated information extraction and achieves visible improvements over the best competitor by 2.4% and 1.3%, respectively.
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- StyleFace: Towards Identity-Disentangled Face Generation on Megapixels26
This work proposes StyleFace, a unified framework for 1024 2 resolution high-fidelity identity swapping and de-identification, and proposes an Adaptive Attribute Extractor, which adaptively preserves the identity-irrelevant attributes in a simple yet effective way.
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- FaceChain-ImagineID: Freely Crafting High-Fidelity Diverse Talking Faces from Disentangled Audio25
This paper abstracts the process of people hearing speech, extracting meaningful cues, and creating vari-ous dynamically audio-consistent talking faces, termed Lis-tening and Imagining, into the task of high-fidelity diverse talking faces generation from a single audio, and intro-duce the Controllable Coherent Frame generation.
- SSCGAN: Facial Attribute Editing via Style Skip Connections23
A style skip connection based generative adversarial network, referred to as SSCGAN, is developed which enables accurate facial attribute manipulation and can produce better results in terms of attribute generation accuracy and image quality.
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- STC: Spatio-Temporal Contrastive Learning for Video Instance Segmentation19
This work presents a simple and efficient single-stage VIS framework based on the instance segmentation method CondInst by adding an extra tracking head and proposes a novel bi-directional spatio-temporal contrastive learning strategy for tracking embedding across frames.
- 3QNet18
A novel learning-based point cloud compression framework named 3D Point Cloud Geometry Quantiation Compression Network (3QNet) is proposed, which overcomes the robustness limitation of existing point-based methods and can handle dense points.
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- Multimodal-driven Talking Face Generation via a Unified Diffusion-based Generator17
This work derives a novel paradigm free of unstable seesaw-style optimization, resulting in simple, stable, and effective training and inference schemes, and adopts the Diffusion Models.
- RealTalk: Real-time and Realistic Audio-driven Face Generation with 3D Facial Prior-guided Identity Alignment Network16
A novel generalized audio-driven framework RealTalk, which consists of an audio-to-expression transformer and a high-fidelity expression-to-face renderer, and a lightweight facial identity alignment (FIA) module which includes a lip-shape control structure and a face texture reference structure is proposed.
- Joint Learning Content and Degradation Aware Feature for Blind Super-Resolution16
The proposed Content and Degradation aware SR Network dubbed CDSR outperforms the existing UDP models and achieves competitive performance on PSNR and SSIM even compared with the state-of-the-art SKP methods.
- CtlGAN: Few-shot Artistic Portraits Generation with Contrastive Transfer Learning15
This work proposes CtlGAN, a new few-shot artistic portraits generation model with a novel contrastive transfer learning strategy that significantly outperforms state-of-the-arts under 10-shot and 1-shot settings and generates high quality artistic portraits.
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- A Generalist FaceX via Learning Unified Facial Representation14
This work presents FaceX framework, a novel facial generalist model capable of handling diverse facial tasks simultaneously, and introduces Facial Omni-Representation Decomposing (FORD) for seamless manipulation of various facial components, microscopically decomposing the core aspects of most facial editing tasks.
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- Frequency Consistent Adaptation for Real World Super Resolution13
A novel Frequency Consistent Adaptation (FCA) that ensures the frequency domain consistency when applying existing Super-Resolution methods to the real scene and improves the performance of the SR model under real-world setting achieving state-of-the-art results with high fidelity and plausible perception.
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- Learning Comprehensive Motion Representation for Action Recognition12
A Comprehensive Motion Representation (CMR) learning method for action recognition, which achieves competitive performance on Something-Something V1 & V2 and Kinetics-400, and outperforms the current state-of-the-art on the temporal reasoning datasets Something- something V1 and V2.
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- Pushing Auto-regressive Models for 3D Shape Generation at Capacity and Scalability11
This paper extends auto-regressive models to 3D domains, and introduces discrete representation learning based on a latent vector instead of volumetric grids, which not only reduces computational costs but also preserves essential geometric details by learning the joint distributions in a more tractable order.
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- Dynamic Frame Interpolation in Wavelet Domain9
This work proposes a novel two-stage frame interpolation framework termed WaveletVFI and finds that embedding a classifier in the motion perception network to learn a dynamic threshold for each sample can achieve more computation reduction with almost no loss of accuracy.
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- Scene Prior Filtering for Depth Super-Resolution7
A Scene Prior Filtering network, SPFNet, which utilizes the priors’ surface normal and semantic map from large-scale models and designs a One-to-one Prior Embedding that continuously embeds every single modal prior into depth using Mutual Guided Filtering, further alleviating texture interference while enhancing edge representations.
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- HifiHead: One-Shot High Fidelity Neural Head Synthesis with 3D Control5
HifiHead is proposed, a high fidelity neural talking head synthesis method, which can well preserve the source image's appearance and control the motion flexibly with 3D morphable face models (3DMMs) parameters derived from a driving image or indicated by users.
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- Learning Versatile 3D Shape Generation with Improved AR Models3
The Improved Auto-regressive Model (ImAM) is proposed for 3D shape generation, which applies discrete representation learning based on a latent vector instead of volumetric grids and can synthesize diverse and faithful shapes of multiple categories, achieving state-of-the-art performance.
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- Distribution Distillation Loss: Generic Approach for Improving Face Recognition from Hard Samples2
This work proposes a novel Distribution Distillation Loss to narrow the performance gap between easy and hard samples, which is a simple, effective and generic for various types of facial variations.
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- DiffFAE: Advancing High-fidelity One-shot Facial Appearance Editing with Space-sensitive Customization and Semantic Preservation1
DiffFAE is presented, a one-stage and highly-efficient diffusion-based framework tailored for high-fidelity FAE, and a consistency regularization is introduced for the pipeline to enhance editing controllability by leveraging prior knowledge in the attention matrices of diffusion model.
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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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