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Yunhong Wang

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20 papers

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YNIMG Journal 2026 Journal Article

Creative individuals demonstrate heightened neural synchrony during the viewing of montage film

Sai Yao
Xueyang Wang
Yunhong Wang
Peng Yao
Chengzhen Liu
Jiang Qiu

Creative individuals can generate novel ideas or products, but do they also possess an extraordinary sensitivity to artistic stimuli that are inherently highly creative? To address this question, we employed a naturalistic paradigm in which adolescents viewed montage-style film clips during functional magnetic resonance imaging (fMRI) scanning (n = 88, ages 8-18), while creativity was assessed using the Alternate Uses Task (AUT). Inter-subject correlation (ISC) analysis revealed that individuals with higher creativity demonstrated significantly stronger neural synchrony during montage sequences, particularly in the dorsal attention network (r = 0.31). Furthermore, neural synchrony in the high-creativity group was significantly greater than that in both the low-creativity group and the mixed high-low creativity group (p < 0.001). Dynamic ISC analysis further demonstrated that montage segments elicited significantly stronger neural synchrony than non-montage segments, particularly in the frontal eye fields (t = 4.683, p < 0.001), suggesting enhanced attentional alignment during montage sequences. These findings suggest that creative individuals exhibit stronger neural alignment when processing temporally discontinuous cinematic structures. More broadly, the results highlight the value of temporally complex, ecologically valid stimuli for investigating individual differences in brain function and provide potential neural markers of creativity in dynamic audiovisual environments.

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AAAI Conference 2025 Conference Paper

GeoBEV: Learning Geometric BEV Representation for Multi-view 3D Object Detection

Jinqing Zhang
Yanan Zhang
Yunlong Qi
Zehua Fu
Qingjie Liu
Yunhong Wang

Bird's-Eye-View (BEV) representation has emerged as a mainstream paradigm for multi-view 3D object detection, demonstrating impressive perceptual capabilities. However, existing methods overlook the geometric quality of BEV representation, leaving it in a low-resolution state and failing to restore the authentic geometric information of the scene. In this paper, we identify the drawbacks of previous approaches that limit the geometric quality of BEV representation and propose Radial-Cartesian BEV Sampling (RC-Sampling), which outperforms other feature transformation methods in efficiently generating high-resolution dense BEV representation to restore fine-grained geometric information. Additionally, we design a novel In-Box Label to substitute the traditional depth label generated from the LiDAR points. This label reflects the actual geometric structure of objects rather than just their surfaces, injecting real-world geometric information into the BEV representation. In conjunction with the In-Box Label, Centroid-Aware Inner Loss (CAI Loss) is developed to capture the inner geometric structure of objects. Finally, we integrate the aforementioned modules into a novel multi-view 3D object detector, dubbed GeoBEV, which achieves a state-of-the-art result of 66.2% NDS on the nuScenes test set.

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AAAI Conference 2025 Conference Paper

Multi-modal Deepfake Detection via Multi-task Audio-Visual Prompt Learning

Hui Miao
Yuanfang Guo
Zeming Liu
Yunhong Wang

With the malicious use and dissemination of multi-modal deepfake videos, researchers start to investigate multi-modal deepfake detection. Unfortunately, most of the existing methods tune all the parameters of the deep network with limited speech video datasets and are trained under coarse-grained consistency supervision, which hinders their generalization ability in practical scenarios. To solve these problems, in this paper, we propose the first multi-task audio-visual prompt learning method for multi-modal deepfake video detection, by exploiting multiple foundation models. Specifically, we construct a two-stream multi-task learning architecture and propose sequential visual prompts and short-time audio prompts to extract multi-modal features, which are aligned at the frame level and utilized in subsequent fine-grained feature matching and fusion. Due to the natural alignment of visual content and audio signal in real data, we propose a frame-level cross-modal feature matching loss function to learn the fine-grained audio-visual consistency. Comprehensive experiments demonstrate the effectiveness and superior generalization ability of our method against the state-of-the-art methods.

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AAAI Conference 2025 Conference Paper

TCAQ-DM: Timestep-Channel Adaptive Quantization for Diffusion Models

Haocheng Huang
Jiaxin Chen
Jinyang Guo
Ruiyi Zhan
Yunhong Wang

Diffusion models have achieved remarkable success in the image and video generation tasks. Nevertheless, they often require a large amount of memory and time overhead during inference, due to the complex network architecture and considerable number of timesteps for iterative diffusion. Recently, the post-training quantization (PTQ) technique has proved a promising way to reduce the inference cost by quantizing the float-point operations to low-bit ones. However, most of them fail to tackle with the large variations in the distribution of activations across distinct channels and timesteps, as well as the inconsistent of input between quantization and inference on diffusion models, thus leaving much room for improvement. To address the above issues, we propose a novel method dubbed Timestep-Channel Adaptive Quantization for Diffusion Models (TCAQ-DM). Specifically, we develop a timestep-channel joint reparameterization (TCR) module to balance the activation range along both the timesteps and channels, facilitating the successive reconstruction procedure. Subsequently, we employ a dynamically adaptive quantization (DAQ) module that mitigate the quantization error by selecting an optimal quantizer for each post-Softmax layers according to their specific types of distributions. Moreover, we present a progressively aligned reconstruction (PAR) strategy to mitigate the bias caused by the input mismatch. Extensive experiments on various benchmarks and distinct diffusion models demonstrate that the proposed method substantially outperforms the state-of-the-art approaches in most cases, especially yielding comparable FID metrics to the full precision model on CIFAR-10 in the W6A6 setting, while enabling generating available images in the W4A4 settings.

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AAAI Conference 2025 Conference Paper

Unified Knowledge Maintenance Pruning and Progressive Recovery with Weight Recalling for Large Vision-Language Models

Zimeng Wu
Jiaxin Chen
Yunhong Wang

Large Vision-Language Model (LVLM), leveraging Large Language Model (LLM) as the cognitive core, has recently become one of the most representative multimodal model paradigms. However, with the expansion of unimodal branches, \emph{i.e.} visual encoder and LLM, the storage and computational burdens intensify, posing challenges for deployment. Structured pruning has proved promising in compressing large models by trimming a large portion of insignificant network structures. Nevertheless, most of them are predominantly designed for LLMs, either relying on unitary importance metrics that fail to deal with modality-wise imbalances or adopting generic pruning and recovery paradigms that overlook the unique calibration status and capability requirements of large models, leading to substantial performance degradation. To address these issues, we propose a novel structured pruning approach for LVLMs, dubbed Unified Knowledge Maintenance Pruning and Progressive Recovery with Weight Recalling (UKMP). Specifically, we design a Unified Knowledge Maintenance Importance (UKMI) metric, which simultaneously considers balancing the block-wise and modality-wise importance by adaptive normalization, optimizing the importance estimation by refining gradient-based criteria, and maintaining the knowledge capacity of LVLMs by using the angle distribution information entropy. Moreover, we develop a LoRA-based Progressive Distillation (LPD) method that recalls the pruned weights and performs progressive distillation for comprehensive recovery. Extensive experimental results across various vision-language tasks demonstrate the effectiveness of our approach, comparing to the state-of-the-art structured pruning methods.

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NeurIPS Conference 2024 Conference Paper

4Diffusion: Multi-view Video Diffusion Model for 4D Generation

Haiyu Zhang
Xinyuan Chen
Yaohui Wang
Xihui Liu
Yunhong Wang
Yu Qiao

Current 4D generation methods have achieved noteworthy efficacy with the aid of advanced diffusion generative models. However, these methods lack multi-view spatial-temporal modeling and encounter challenges in integrating diverse prior knowledge from multiple diffusion models, resulting in inconsistent temporal appearance and flickers. In this paper, we propose a novel 4D generation pipeline, namely $\textbf{4Diffusion}$, aimed at generating spatial-temporally consistent 4D content from a monocular video. We first design a unified diffusion model tailored for multi-view video generation by incorporating a learnable motion module into a frozen 3D-aware diffusion model to capture multi-view spatial-temporal correlations. After training on a curated dataset, our diffusion model acquires reasonable temporal consistency and inherently preserves the generalizability and spatial consistency of the 3D-aware diffusion model. Subsequently, we propose 4D-aware Score Distillation Sampling loss, which is based on our multi-view video diffusion model, to optimize 4D representation parameterized by dynamic NeRF. This aims to eliminate discrepancies arising from multiple diffusion models, allowing for generating spatial-temporally consistent 4D content. Moreover, we devise an anchor loss to enhance the appearance details and facilitate the learning of dynamic NeRF. Extensive qualitative and quantitative experiments demonstrate that our method achieves superior performance compared to previous methods.

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AAAI Conference 2024 Conference Paper

AGS: Affordable and Generalizable Substitute Training for Transferable Adversarial Attack

Ruikui Wang
Yuanfang Guo
Yunhong Wang

In practical black-box attack scenarios, most of the existing transfer-based attacks employ pretrained models (e.g. ResNet50) as the substitute models. Unfortunately, these substitute models are not always appropriate for transfer-based attacks. Firstly, these models are usually trained on a largescale annotated dataset, which is extremely expensive and time-consuming to construct. Secondly, the primary goal of these models is to perform a specific task, such as image classification, which is not developed for adversarial attacks. To tackle the above issues, i.e., high cost and over-fitting on taskspecific models, we propose an Affordable and Generalizable Substitute (AGS) training framework tailored for transferbased adversarial attack. Specifically, we train the substitute model from scratch by our proposed adversary-centric constrastive learning. This proposed learning mechanism introduces another sample with slight adversarial perturbations as an additional positive view of the input image, and then encourages the adversarial view and two benign views to interact comprehensively with each other. To further boost the generalizability of the substitute model, we propose adversarial invariant learning to maintain the representations of the adversarial example invariants under augmentations with various strengths. Our AGS model can be trained solely with unlabeled and out-of domain data and avoid overfitting to any task-specific models, because of its inherently self-supervised nature. Extensive experiments demonstrate that our AGS achieves comparable or superior performance compared to substitute models pretrained on the complete ImageNet training set, when executing attacks across a diverse range of target models, including ViTs, robustly trained models, object detection and segmentation models. Our source codes are available at https://github.com/lwmming/AGS.

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NeurIPS Conference 2024 Conference Paper

Transforming Vision Transformer: Towards Efficient Multi-Task Asynchronous Learner

Hanwen Zhong
Jiaxin Chen
Yutong Zhang
Di Huang
Yunhong Wang

Multi-Task Learning (MTL) for Vision Transformer aims at enhancing the model capability by tackling multiple tasks simultaneously. Most recent works have predominantly focused on designing Mixture-of-Experts (MoE) structures and integrating Low-Rank Adaptation (LoRA) to efficiently perform multi-task learning. However, their rigid combination hampers both the optimization of MoE and the effectiveness of reparameterization of LoRA, leading to sub-optimal performance and low inference speed. In this work, we propose a novel approach dubbed Efficient Multi-Task Learning (EMTAL) by transforming a pre-trained Vision Transformer into an efficient multi-task learner during training, and reparameterizing the learned structure for efficient inference. Specifically, we firstly develop the MoEfied LoRA structure, which decomposes the pre-trained Transformer into a low-rank MoE structure and employ LoRA to fine-tune the parameters. Subsequently, we take into account the intrinsic asynchronous nature of multi-task learning and devise a learning Quality Retaining (QR) optimization mechanism, by leveraging the historical high-quality class logits to prevent a well-trained task from performance degradation. Finally, we design a router fading strategy to integrate the learned parameters into the original Transformer, archiving efficient inference. Extensive experiments on public benchmarks demonstrate the superiority of our method, compared to the state-of-the-art multi-task learning approaches.

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AAAI Conference 2023 Conference Paper

Deepfake Video Detection via Facial Action Dependencies Estimation

Lingfeng Tan
Yunhong Wang
Junfu Wang
Liang Yang
Xunxun Chen
Yuanfang Guo

Deepfake video detection has drawn significant attention from researchers due to the security issues induced by deepfake videos. Unfortunately, most of the existing deepfake detection approaches have not competently modeled the natural structures and movements of human faces. In this paper, we formulate the deepfake video detection problem into a graph classification task, and propose a novel paradigm named Facial Action Dependencies Estimation (FADE) for deepfake video detection. We propose a Multi-Dependency Graph Module (MDGM) to capture abundant dependencies among facial action units, and extracts subtle clues in these dependencies. MDGM can be easily integrated into the existing frame-level detection schemes to provide significant performance gains. Extensive experiments demonstrate the superiority of our method against the state-of-the-art methods.

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AAAI Conference 2023 Conference Paper

Global-Local Characteristic Excited Cross-Modal Attacks from Images to Videos

Ruikui Wang
Yuanfang Guo
Yunhong Wang

The transferability of adversarial examples is the key property in practical black-box scenarios. Currently, numerous methods improve the transferability across different models trained on the same modality of data. The investigation of generating video adversarial examples with imagebased substitute models to attack the target video models, i.e., cross-modal transferability of adversarial examples, is rarely explored. A few works on cross-modal transferability directly apply image attack methods for each frame and no factors especial for video data are considered, which limits the cross-modal transferability of adversarial examples. In this paper, we propose an effective cross-modal attack method which considers both the global and local characteristics of video data. Firstly, from the global perspective, we introduce inter-frame interaction into attack process to induce more diverse and stronger gradients rather than perturb each frame separately. Secondly, from the local perspective, we disrupt the inherently local correlation of frames within a video, which prevents black-box video model from capturing valuable temporal clues. Extensive experiments on the UCF-101 and Kinetics-400 validate the proposed method significantly improves cross-modal transferability and even surpasses strong baseline using video models as substitute model. Our source codes are available at https://github.com/lwmming/Cross-Modal-Attack.

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IJCAI Conference 2022 Conference Paper

PACE: Predictive and Contrastive Embedding for Unsupervised Action Segmentation

Jiahao Wang
Jie Qin
Yunhong Wang
Annan Li

Action segmentation, inferring temporal positions of human actions in an untrimmed video, is an important prerequisite for various video understanding tasks. Recently, unsupervised action segmentation (UAS) has emerged as a more challenging task due to the unavailability of frame-level annotations. Existing clustering- or prediction-based UAS approaches suffer from either over-segmentation or overfitting, leading to unsatisfactory results. To address those problems, we propose Predictive And Contrastive Embedding (PACE), a unified UAS framework leveraging both predictability and similarity information for more accurate action segmentation. On the basis of an auto-regressive transformer encoder, predictive embeddings are learned by exploiting the predictability of video context, while contrastive embeddings are generated by leveraging the similarity of adjacent short video clips. Extensive experiments on three challenging benchmarks demonstrate the superiority of our method, with up to 26. 9% improvements in F1-score over the state of the art.

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IJCAI Conference 2022 Conference Paper

SparseTT: Visual Tracking with Sparse Transformers

Zhihong Fu
Zehua Fu
Qingjie Liu
Wenrui Cai
Yunhong Wang

Transformers have been successfully applied to the visual tracking task and significantly promote tracking performance. The self-attention mechanism designed to model long-range dependencies is the key to the success of Transformers. However, self-attention lacks focusing on the most relevant information in the search regions, making it easy to be distracted by background. In this paper, we relieve this issue with a sparse attention mechanism by focusing the most relevant information in the search regions, which enables a much accurate tracking. Furthermore, we introduce a double-head predictor to boost the accuracy of foreground-background classification and regression of target bounding boxes, which further improve the tracking performance. Extensive experiments show that, without bells and whistles, our method significantly outperforms the state-of-the-art approaches on LaSOT, GOT-10k, TrackingNet, and UAV123, while running at 40 FPS. Notably, the training time of our method is reduced by 75% compared to that of TransT. The source code and models are available at https: //github. com/fzh0917/SparseTT.

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AAAI Conference 2021 Conference Paper

MIEHDR CNN: Main Image Enhancement based Ghost-Free High Dynamic Range Imaging using Dual-Lens Systems

Xuan Dong
Xiaoyan Hu
Weixin Li
Xiaojie Wang
Yunhong Wang

We study the High Dynamic Range (HDR) imaging problem using two Low Dynamic Range (LDR) images that are shot from dual-lens systems in a single shot time with different exposures. In most of the related HDR imaging methods, the problem is usually solved by Multiple Images Merging, i. e. the final HDR image is fused from pixels of all the input LDR images. However, ghost artifacts can be hardly avoided using this strategy. Instead of directly merging the multiple LDR inputs, we use an indirect way which enhances the main image, i. e. the short exposure image IS, using the long exposure image IL serving as guidance. In detail, we propose a new model, named MIEHDR CNN model, which consists of three subnets, i. e. Soft Warp CNN, 3D Guided Denoising CNN and Fusion CNN. The Soft Warp CNN aligns IL to get the aligned result ILA using the soft exposed result of IS as reference. The 3D Guided Denoising CNN denoises the soft exposed result of IS using ILA as guidance, whose result are fed into the Fusion CNN with IS to get the HDR result. The MIEHDR CNN model is implemented by MindSpore and experimental results show that we can outperform related methods largely and avoid ghost artifacts.

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AAAI Conference 2021 Conference Paper

PC-RGNN: Point Cloud Completion and Graph Neural Network for 3D Object Detection

Yanan Zhang
Di Huang
Yunhong Wang

LiDAR-based 3D object detection is an important task for autonomous driving and current approaches suffer from sparse and partial point clouds of distant and occluded objects. In this paper, we propose a novel two-stage approach, namely PC-RGNN, dealing with such challenges by two specific solutions. On the one hand, we introduce a point cloud completion module to recover high-quality proposals of dense points and entire views with original structures preserved. On the other hand, a graph neural network module is designed, which comprehensively captures relations among points through a local-global attention mechanism as well as multi-scale graph based context aggregation, substantially strengthening encoded features. Extensive experiments on the KITTI benchmark show that the proposed approach outperforms the previous state-of-the-art baselines by remarkable margins, highlighting its effectiveness.

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AAAI Conference 2020 Conference Paper

Cycle-CNN for Colorization towards Real Monochrome-Color Camera Systems

Xuan Dong
Weixin Li
Xiaojie Wang
Yunhong Wang

Colorization in monochrome-color camera systems aims to colorize the gray image IG from the monochrome camera using the color image RC from the color camera as reference. Since monochrome cameras have better imaging quality than color cameras, the colorization can help obtain higher quality color images. Related learning based methods usually simulate the monochrome-color camera systems to generate the synthesized data for training, due to the lack of ground-truth color information of the gray image in the real data. However, the methods that are trained relying on the synthesized data may get poor results when colorizing real data, because the synthesized data may deviate from the real data. We present a new CNN model, named cycle CNN, which can directly use the real data from monochrome-color camera systems for training. In detail, we use the colorization CNN model to do the colorization twice. First, we colorize IG using RC as reference to obtain the ﬁrst-time colorization result IC. Second, we colorize the de-colored map of RC, i. e. RG, using the ﬁrst-time colorization result IC as reference to obtain the second-time colorization result R C. In this way, for the second-time colorization result R C, we use the original color map RC as ground-truth and introduce the cycle consistency loss to push R C ≈ RC. Also, for the ﬁrst-time colorization result IC, we propose a structure similarity loss to encourage the luminance maps between IG and IC to have similar structures. In addition, we introduce a spatial smoothness loss within the colorization CNN model to encourage spatial smoothness of the colorization result. Combining all these losses, we could train the colorization CNN model using the real data in the absence of the ground-truth color information of IG. Experimental results show that we can outperform related methods largely for colorizing real data.

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AAAI Conference 2020 Conference Paper

Distraction-Aware Feature Learning for Human Attribute Recognition via Coarse-to-Fine Attention Mechanism

Mingda Wu
Di Huang
Yuanfang Guo
Yunhong Wang

Recently, Human Attribute Recognition (HAR) has become a hot topic due to its scientiﬁc challenges and application potentials, where localizing attributes is a crucial stage but not well handled. In this paper, we propose a novel deep learning approach to HAR, namely Distraction-aware HAR (Da- HAR). It enhances deep CNN feature learning by improving attribute localization through a coarse-to-ﬁne attention mechanism. At the coarse step, a self-mask block is built to roughly discriminate and reduce distractions, while at the ﬁne step, a masked attention branch is applied to further eliminate irrelevant regions. Thanks to this mechanism, feature learning is more accurate, especially when heavy occlusions and complex backgrounds exist. Extensive experiments are conducted on the WIDER-Attribute and RAP databases, and state-ofthe-art results are achieved, demonstrating the effectiveness of the proposed approach.

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IJCAI Conference 2020 Conference Paper

I4R: Promoting Deep Reinforcement Learning by the Indicator for Expressive Representations

Xufang Luo
Qi Meng
Di He
Wei Chen
Yunhong Wang

Learning expressive representations is always crucial for well-performed policies in deep reinforcement learning (DRL). Different from supervised learning, in DRL, accurate targets are not always available, and some inputs with different actions only have tiny differences, which stimulates the demand for learning expressive representations. In this paper, firstly, we empirically compare the representations of DRL models with different performances. We observe that the representations of a better state extractor (SE) are more scattered than a worse one when they are visualized. Thus, we investigate the singular values of representation matrix, and find that, better SEs always correspond to smaller differences among these singular values. Next, based on such observations, we define an indicator of the representations for DRL model, which is the Number of Significant Singular Values (NSSV) of a representation matrix. Then, we propose I4R algorithm, to improve DRL algorithms by adding the corresponding regularization term to enhance the NSSV. Finally, we apply I4R to both policy gradient and value based algorithms on Atari games, and the results show the superiority of our proposed method.

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AAAI Conference 2019 Conference Paper

Learning a Deep Convolutional Network for Colorization in Monochrome-Color Dual-Lens System

Xuan Dong
Weixin Li
Xiaojie Wang
Yunhong Wang

In the monochrome-color dual-lens system, the gray image captured by the monochrome camera has better quality than the color image from the color camera, but does not have color information. To get high-quality color images, it is desired to colorize the gray image with the color image as reference. Related works usually use hand-crafted methods to search for the best-matching pixel in the reference image for each pixel in the input gray image, and copy the color of the best-matching pixel as the result. We propose a novel deep convolution network to solve the colorization problem in an end-to-end way. Based on our observation that, for each pixel in the input image, there usually exist multiple pixels in the reference image that have the correct colors, our method performs weighted average of colors of the candidate pixels in the reference image to utilize more candidate pixels with correct colors. The weight values between pixels in the input image and the reference image are obtained by learning a weight volume using deep feature representations, where an attention operation is proposed to focus on more useful candidate pixels and a 3-D regulation is performed to learn with context information. In addition, to correct wrongly colorized pixels in occlusion regions, we propose a color residue joint learning module to correct the colorization result with the input gray image as guidance. We evaluate our method on the Scene Flow, Cityscapes, Middlebury, and Sintel datasets. Experimental results show that our method largely outperforms the state-of-the-art methods.

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AAAI Conference 2018 Conference Paper

CoChat: Enabling Bot and Human Collaboration for Task Completion

Xufang Luo
Zijia Lin
Yunhong Wang
Zaiqing Nie

Chatbots have drawn signiﬁcant attention of late in both industry and academia. For most task completion bots in the industry, human intervention is the only means of avoiding mistakes in complex real-world cases. However, to the best of our knowledge, there is no existing research work modeling the collaboration between task completion bots and human workers. In this paper, we introduce CoChat, a dialog management framework to enable effective collaboration between bots and human workers. In CoChat, human workers can introduce new actions at any time to handle previously unseen cases. We propose a memory-enhanced hierarchical RNN (MemHRNN) to handle the one-shot learning challenges caused by instantly introducing new actions in CoChat. Extensive experiments on real-world datasets well demonstrate that CoChat can relieve most of the human workers’ workload, and get better user satisfaction rates comparing to other state-of-the-art frameworks.

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AAAI Conference 2017 Conference Paper

Learning Heterogeneous Dictionary Pair with Feature Projection Matrix for Pedestrian Video Retrieval via Single Query Image

Xiaoke Zhu
Xiao-Yuan Jing
Fei Wu
Yunhong Wang
Wangmeng Zuo
Wei-Shi Zheng

Person re-identiﬁcation (re-id) plays an important role in video surveillance and forensics applications. In many cases, person re-id needs to be conducted between image and video clip, e. g. , re-identifying a suspect from large quantities of pedestrian videos given a single image of him. We call reid in this scenario as image to video person re-id (IVPR). In practice, image and video are usually represented with different features, and there usually exist large variations between frames within each video. These factors make matching between image and video become a very challenging task. In this paper, we propose a joint feature projection matrix and heterogeneous dictionary pair learning (PHDL) approach for IVPR. Speciﬁcally, PHDL jointly learns an intra-video projection matrix and a pair of heterogeneous image and video dictionaries. With the learned projection matrix, the inﬂuence of variations within each video to the matching can be reduced. With the learned dictionary pair, the heterogeneous image and video features can be transformed into coding coefﬁcients with the same dimension, such that the matching can be conducted using coding coefﬁcients. Furthermore, to ensure that the obtained coding coefﬁcients have favorable discriminability, PHDL designs a point-to-set coefﬁcient discriminant term. Experiments on the public iLIDS-VID and PRID 2011 datasets demonstrate the effectiveness of the proposed approach.

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