no code implementations • ECCV 2020 • Junsong Fan, Zhao-Xiang Zhang, Tieniu Tan
Instead of struggling to refine a single seed, we propose a novel approach to alleviate the inaccurate seed problem by leveraging the segmentation model's robustness to learn from multiple seeds.
no code implementations • ECCV 2020 • Ke Han, Yan Huang, Zerui Chen, Liang Wang, Tieniu Tan
In this paper, we propose a novel Prediction, Recovery and Identification (PRI) model for LR re-id, which adaptively recovers missing details by predicting a preferable scale factor based on the image content.
1 code implementation • 11 Jun 2025 • Junfei Wu, Jian Guan, Kaituo Feng, Qiang Liu, Shu Wu, Liang Wang, Wei Wu, Tieniu Tan
To address the limitations, we propose drawing to reason in space, a novel paradigm that enables LVLMs to reason through elementary drawing operations in the visual space.
no code implementations • 9 Jun 2025 • Peiyan Li, Yixiang Chen, Hongtao Wu, Xiao Ma, Xiangnan Wu, Yan Huang, Liang Wang, Tao Kong, Tieniu Tan
In GemBench, it surpasses all the comparing baseline methods in terms of average success rate.
no code implementations • 25 May 2025 • Haitian Zhong, Yuhuan Liu, Ziyang Xu, Guofan Liu, Qiang Liu, Shu Wu, Zhe Zhao, Liang Wang, Tieniu Tan
Large language model editing methods frequently suffer from overfitting, wherein factual updates can propagate beyond their intended scope, overemphasizing the edited target even when it's contextually inappropriate.
1 code implementation • 17 May 2025 • Xinlong Chen, Yuanxing Zhang, Qiang Liu, Junfei Wu, Fuzheng Zhang, Tieniu Tan
Large Vision-Language Models (LVLMs) have exhibited impressive capabilities across various visual tasks, yet they remain hindered by the persistent challenge of hallucinations.
1 code implementation • 16 May 2025 • Yexiang Liu, Zekun Li, Zhi Fang, Nan Xu, Ran He, Tieniu Tan
In this paper, we focus on a standard and realistic scaling setting: majority voting.
no code implementations • 21 Apr 2025 • Huanyu Zhang, Chengzu Li, Wenshan Wu, Shaoguang Mao, Yan Xia, Ivan Vulić, Zhang Zhang, Liang Wang, Tieniu Tan, Furu Wei
Multimodal Large Language Models (MLLMs) have demonstrated impressive performance in general vision-language tasks.
no code implementations • 4 Apr 2025 • Wulin Xie, Yi-Fan Zhang, Chaoyou Fu, Yang Shi, Bingyan Nie, Hongkai Chen, Zhang Zhang, Liang Wang, Tieniu Tan
Existing MLLM benchmarks face significant challenges in evaluating Unified MLLMs (U-MLLMs) due to: 1) lack of standardized benchmarks for traditional tasks, leading to inconsistent comparisons; 2) absence of benchmarks for mixed-modality generation, which fails to assess multimodal reasoning capabilities.
1 code implementation • 18 Mar 2025 • Tao Yu, Yi-Fan Zhang, Chaoyou Fu, Junkang Wu, Jinda Lu, Kun Wang, Xingyu Lu, Yunhang Shen, Guibin Zhang, Dingjie Song, Yibo Yan, Tianlong Xu, Qingsong Wen, Zhang Zhang, Yan Huang, Liang Wang, Tieniu Tan
In this paper, we aim to provide a comprehensive and systematic review of alignment algorithms for MLLMs.
1 code implementation • 18 Feb 2025 • Xinlong Chen, Yuanxing Zhang, Chongling Rao, Yushuo Guan, Jiaheng Liu, Fuzheng Zhang, Chengru Song, Qiang Liu, Di Zhang, Tieniu Tan
The training of controllable text-to-video (T2V) models relies heavily on the alignment between videos and captions, yet little existing research connects video caption evaluation with T2V generation assessment.
no code implementations • 30 Dec 2024 • Huanyu Zhang, Chang Xu, Yi-Fan Zhang, Zhang Zhang, Liang Wang, Jiang Bian, Tieniu Tan
In this paper, we introduce TimeRAF, a Retrieval-Augmented Forecasting model that enhance zero-shot time series forecasting through retrieval-augmented techniques.
no code implementations • 30 Dec 2024 • Zhengbo Wang, Jian Liang, Lijun Sheng, Ran He, Zilei Wang, Tieniu Tan
So far, efficient fine-tuning has become a popular strategy for enhancing the capabilities of foundation models on downstream tasks by learning plug-and-play modules.
no code implementations • 23 Aug 2024 • Yi-Fan Zhang, Huanyu Zhang, Haochen Tian, Chaoyou Fu, Shuangqing Zhang, Junfei Wu, Feng Li, Kun Wang, Qingsong Wen, Zhang Zhang, Liang Wang, Rong Jin, Tieniu Tan
The challenges of perceiving high-resolution images and understanding complex real-world scenarios remain urgent issues to be addressed.
1 code implementation • 25 Jul 2024 • Zhengbo Wang, Jian Liang, Ran He, Zilei Wang, Tieniu Tan
And this low-rank gradient can be expressed in terms of the gradients of the two low-rank matrices in LoRA.
2 code implementations • 26 Jun 2024 • Min Ren, Yunlong Wang, Yuhao Zhu, Yongzhen Huang, Zhenan Sun, Qi Li, Tieniu Tan
Insect production for food and feed presents a promising supplement to ensure food safety and address the adverse impacts of agriculture on climate and environment in the future.
1 code implementation • 12 Jun 2024 • Yingyan Li, Lue Fan, JiaWei He, Yuqi Wang, Yuntao Chen, Zhaoxiang Zhang, Tieniu Tan
Specifically, our framework \textbf{LAW} uses a LAtent World model to predict future latent features based on the predicted ego actions and the latent feature of the current frame.
Ranked #17 on
NavSim
on OpenScene
no code implementations • 17 Mar 2024 • Zheling Meng, Bo Peng, Jing Dong, Tieniu Tan
We also find that the artifact features APN focuses on across generators and scenes are global and diverse.
1 code implementation • 12 Mar 2024 • Han Huang, Haitian Zhong, Tao Yu, Qiang Liu, Shu Wu, Liang Wang, Tieniu Tan
Compared to this, editing Large Vision-Language Models (LVLMs) faces extra challenges from diverse data modalities and complicated model components, and data for LVLMs editing are limited.
1 code implementation • 8 Mar 2024 • Yi-Fan Zhang, Weichen Yu, Qingsong Wen, Xue Wang, Zhang Zhang, Liang Wang, Rong Jin, Tieniu Tan
In the realms of computer vision and natural language processing, Large Vision-Language Models (LVLMs) have become indispensable tools, proficient in generating textual descriptions based on visual inputs.
1 code implementation • 18 Feb 2024 • Junfei Wu, Qiang Liu, Ding Wang, Jinghao Zhang, Shu Wu, Liang Wang, Tieniu Tan
In this work, we adopt the intuition that the LVLM tends to respond logically consistently for existent objects but inconsistently for hallucinated objects.
1 code implementation • 6 Feb 2024 • Zhengbo Wang, Jian Liang, Ran He, Zilei Wang, Tieniu Tan
This paper proposes a \textbf{C}ollabo\textbf{ra}tive \textbf{F}ine-\textbf{T}uning (\textbf{CraFT}) approach for fine-tuning black-box VLMs to downstream tasks, where one only has access to the input prompts and the output predictions of the model.
1 code implementation • 6 Feb 2024 • Zhengbo Wang, Jian Liang, Lijun Sheng, Ran He, Zilei Wang, Tieniu Tan
Extensive results on 17 datasets validate that our method surpasses or achieves comparable results with state-of-the-art methods on few-shot classification, imbalanced learning, and out-of-distribution generalization.
1 code implementation • 4 Jan 2024 • Kuangpu Guo, Yuhe Ding, Jian Liang, Ran He, Zilei Wang, Tieniu Tan
As minority classes suffer from worse accuracy due to overfitting on local imbalanced data, prior methods often incorporate class-balanced learning techniques during local training.
1 code implementation • 20 Dec 2023 • Yi-Fan Zhang, Zhang Zhang, Liang Wang, Tieniu Tan, Rong Jin
In an effort to address these issues, we delve into the realm of zero-shot machine-generated text detection.
no code implementations • 18 Dec 2023 • Dongze Li, Kang Zhao, Wei Wang, Bo Peng, Yingya Zhang, Jing Dong, Tieniu Tan
Audio-driven talking head synthesis is a promising topic with wide applications in digital human, film making and virtual reality.
no code implementations • 28 Nov 2023 • Yifan Zhang, Xue Wang, Tian Zhou, Kun Yuan, Zhang Zhang, Liang Wang, Rong Jin, Tieniu Tan
We demonstrate the effectiveness of \abbr through comprehensive experiments on multiple OOD detection benchmarks, extensive empirical studies show that \abbr significantly improves the performance of OOD detection over state-of-the-art methods.
2 code implementations • NeurIPS 2023 • Yi-Fan Zhang, Qingsong Wen, Xue Wang, Weiqi Chen, Liang Sun, Zhang Zhang, Liang Wang, Rong Jin, Tieniu Tan
Online updating of time series forecasting models aims to address the concept drifting problem by efficiently updating forecasting models based on streaming data.
1 code implementation • 24 Aug 2023 • Jian Liang, Lijun Sheng, Zhengbo Wang, Ran He, Tieniu Tan
The emergence of vision-language models, such as CLIP, has spurred a significant research effort towards their application for downstream supervised learning tasks.
2 code implementations • 17 Aug 2023 • Zhengxiong Luo, Yan Huang, Shang Li, Liang Wang, Tieniu Tan
To address this issue, instead of considering these two problems independently, we adopt an alternating optimization algorithm, which can estimate the degradation and restore the SR image in a single model.
no code implementations • ICCV 2023 • Tianxiang Ma, Bingchuan Li, Qian He, Jing Dong, Tieniu Tan
In this paper, we introduce a novel Geometry-aware Facial Expression Translation (GaFET) framework, which is based on parametric 3D facial representations and can stably decoupled expression.
1 code implementation • ICCV 2023 • Zhengbo Wang, Jian Liang, Ran He, Nan Xu, Zilei Wang, Tieniu Tan
Thereafter, we fine-tune CLIP with off-the-shelf methods by combining labeled and synthesized features.
2 code implementations • 25 Apr 2023 • Yi-Fan Zhang, Xue Wang, Kexin Jin, Kun Yuan, Zhang Zhang, Liang Wang, Rong Jin, Tieniu Tan
In particular, when the adaptation target is a series of domains, the adaptation accuracy of AdaNPC is 50% higher than advanced TTA methods.
no code implementations • 17 Apr 2023 • Weinan Guan, Wei Wang, Jing Dong, Bo Peng, Tieniu Tan
An important topic in manipulation detection is the localization of the fake regions.
1 code implementation • 27 Mar 2023 • Jian Liang, Ran He, Tieniu Tan
Test-time adaptation (TTA), an emerging paradigm, has the potential to adapt a pre-trained model to unlabeled data during testing, before making predictions.
1 code implementation • CVPR 2023 • Wentao Chen, Chenyang Si, Zhang Zhang, Liang Wang, Zilei Wang, Tieniu Tan
Instead of the naive exploitation of semantic information for remedying classifiers, we explore leveraging semantic information as prompts to tune the visual feature extraction network adaptively.
1 code implementation • ICCV 2023 • Lijun Sheng, Jian Liang, Ran He, Zilei Wang, Tieniu Tan
To address this issue, we propose a model preprocessing framework, named AdaptGuard, to improve the security of model adaptation algorithms.
1 code implementation • 17 Mar 2023 • Zhengbo Wang, Jian Liang, Zilei Wang, Tieniu Tan
To address this issue, we present a novel transductive ZSL method that produces semantic attributes of the unseen data and imposes them on the generative process.
2 code implementations • CVPR 2023 • Zhengxiong Luo, Dayou Chen, Yingya Zhang, Yan Huang, Liang Wang, Yujun Shen, Deli Zhao, Jingren Zhou, Tieniu Tan
A diffusion probabilistic model (DPM), which constructs a forward diffusion process by gradually adding noise to data points and learns the reverse denoising process to generate new samples, has been shown to handle complex data distribution.
Ranked #14 on
Video Generation
on UCF-101
1 code implementation • CVPR 2023 • Yueming Lyu, Tianwei Lin, Fu Li, Dongliang He, Jing Dong, Tieniu Tan
Our key idea is to investigate and identify a space, namely delta image and text space that has well-aligned distribution between CLIP visual feature differences of two images and CLIP textual embedding differences of source and target texts.
1 code implementation • Asian Conference on Computer Vision 2023 • Ke Han, Shaogang Gong, Yan Huang, Liang Wang, Tieniu Tan
However, existing Re-ID methods usually generate 3D body shapes without considering identity modeling, which severely weakens the discriminability of 3D human shapes.
1 code implementation • CVPR 2023 • Dongze Li, Wei Wang, Kang Zhao, Jing Dong, Tieniu Tan
This work presents RiDDLE, short for Reversible and Diversified De-identification with Latent Encryptor, to protect the identity information of people from being misused.
1 code implementation • 3 Feb 2023 • Tianxiang Ma, Bingchuan Li, Qian He, Jing Dong, Tieniu Tan
CNeRF divides the image by semantic regions and learns an independent neural radiance field for each region, and finally fuses them and renders the complete image.
no code implementations • 3 Feb 2023 • Tianxiang Ma, Bingchuan Li, Wei Liu, Miao Hua, Jing Dong, Tieniu Tan
In this paper, we propose a more general learning approach by considering two domain features as a whole and learning both inter-domain correspondence and intra-domain potential information interactions.
1 code implementation • The Eleventh International Conference on Learning Representations (ICLR 2023) 2023 • Yifan Zhang, Xue Wang, Jian Liang, Zhang Zhang, Liang Wang, Rong Jin, Tieniu Tan
A fundamental challenge for machine learning models is how to generalize learned models for out-of-distribution (OOD) data.
Ranked #8 on
Domain Adaptation
on Office-Home
no code implementations • CVPR 2023 • Ke Han, Shaogang Gong, Yan Huang, Liang Wang, Tieniu Tan
Specifically, to formulate meaningful clothing variations in the feature space, our method first estimates a clothing-change normal distribution with intra-ID cross-clothing variances.
no code implementations • ICCV 2023 • Xiaoqiang Zhou, Huaibo Huang, Ran He, Zilei Wang, Jie Hu, Tieniu Tan
In particular, self-attention with cross-scale matching and convolution filters with different kernel sizes are designed to exploit the multi-scale features in images.
no code implementations • 17 Dec 2022 • Zhen Jia, Zhang Zhang, Liang Wang, Tieniu Tan
Image and video synthesis has become a blooming topic in computer vision and machine learning communities along with the developments of deep generative models, due to its great academic and application value.
1 code implementation • ICCV 2023 • Dong An, Yuankai Qi, Yangguang Li, Yan Huang, Liang Wang, Tieniu Tan, Jing Shao
Concretely, we build a local metric map to explicitly aggregate incomplete observations and remove duplicates, while modeling navigation dependency in a global topological map.
Ranked #3 on
Visual Navigation
on R2R
no code implementations • 23 Nov 2022 • Yunfan Liu, Qi Li, Zhenan Sun, Tieniu Tan
One-shot face re-enactment is a challenging task due to the identity mismatch between source and driving faces.
1 code implementation • CVPR 2023 • Huaibo Huang, Xiaoqiang Zhou, Jie Cao, Ran He, Tieniu Tan
STA decomposes vanilla global attention into multiplications of a sparse association map and a low-dimensional attention, leading to high efficiency in capturing global dependencies.
no code implementations • 11 Nov 2022 • Kaiduo Zhang, Muyi Sun, Jianxin Sun, Binghao Zhao, Kunbo Zhang, Zhenan Sun, Tieniu Tan
In this paper, we propose HumanDiffusion, a coarse-to-fine alignment diffusion framework, for text-driven person image generation.
1 code implementation • 25 Oct 2022 • Junsong Fan, Zhaoxiang Zhang, Tieniu Tan
In this paper, we propose a new approach to applying point-level annotations for weakly-supervised panoptic segmentation.
1 code implementation • 16 Jul 2022 • Wentao Chen, Zhang Zhang, Wei Wang, Liang Wang, Zilei Wang, Tieniu Tan
Different from previous cross-domain FSL work (CD-FSL) that considers the domain shift between base and novel classes, the new problem, termed cross-domain cross-set FSL (CDSC-FSL), requires few-shot learners not only to adapt to the new domain, but also to be consistent between different domains within each novel class.
no code implementations • 14 Jun 2022 • Zhengquan Luo, Yunlong Wang, Zilei Wang, Zhenan Sun, Tieniu Tan
Attributes skew hinders the current federated learning (FL) frameworks from consistent optimization directions among the clients, which inevitably leads to performance reduction and unstable convergence.
1 code implementation • CVPR 2022 • Zengjie Song, Yuxi Wang, Junsong Fan, Tieniu Tan, Zhaoxiang Zhang
Sound source localization in visual scenes aims to localize objects emitting the sound in a given image.
1 code implementation • CVPR 2022 • Zhengxiong Luo, Yan Huang, Shang Li, Liang Wang, Tieniu Tan
Compared with previous deterministic degradation models, PDM could model more diverse degradations and generate HR-LR pairs that may better cover the various degradations of test images, and thus prevent the SR model from over-fitting to specific ones.
no code implementations • 1 Mar 2022 • Ke Han, Chenyang Si, Yan Huang, Liang Wang, Tieniu Tan
In this paper, we investigate the generalization problem of person re-identification (re-id), whose major challenge is the distribution shift on an unseen domain.
no code implementations • 29 Sep 2021 • Yifan Zhang, Feng Li, Zhang Zhang, Liang Wang, DaCheng Tao, Tieniu Tan
However, the convex condition of KL DRO may not hold for overparameterized neural networks, such that applying KL DRO often fails to generalize under distribution shifts in real scenarios.
1 code implementation • ICCV 2021 • Min Ren, Lingxiao He, Xingyu Liao, Wu Liu, Yunlong Wang, Tieniu Tan
In this paper, we propose a novel Instance-level and Spatial-Temporal Disentangled Re-ID method (InSTD), to improve Re-ID accuracy.
Ranked #14 on
Person Re-Identification
on DukeMTMC-reID
no code implementations • 22 Jul 2021 • Zhengxiong Luo, Zhicheng Wang, Yan Huang, Liang Wang, Tieniu Tan, Erjin Zhou
It can generate and fuse multi-scale features of the same spatial sizes by setting different dilation rates for different channels.
1 code implementation • 15 Jul 2021 • Dong An, Yuankai Qi, Yan Huang, Qi Wu, Liang Wang, Tieniu Tan
Specifically, our NvEM utilizes a subject module and a reference module to collect contexts from neighbor views.
Ranked #82 on
Vision and Language Navigation
on VLN Challenge
1 code implementation • CVPR 2021 • Xingyuan Bu, Junran Peng, Junjie Yan, Tieniu Tan, Zhaoxiang Zhang
Transfer learning with pre-training on large-scale datasets has played an increasingly significant role in computer vision and natural language processing recently.
2 code implementations • CVPR 2022 • Ziwen He, Wei Wang, Jing Dong, Tieniu Tan
The experiment shows that our method has improved the transferability by a large margin under a similar sparsity setting compared with state-of-the-art methods.
no code implementations • 25 May 2021 • Wentao Chen, Chenyang Si, Wei Wang, Liang Wang, Zilei Wang, Tieniu Tan
Few-shot learning is a challenging task since only few instances are given for recognizing an unseen class.
1 code implementation • 14 May 2021 • Zhengxiong Luo, Yan Huang, Shang Li, Liang Wang, Tieniu Tan
More importantly, \textit{Restorer} is trained with the kernel estimated by \textit{Estimator}, instead of the ground-truth kernel, thus \textit{Restorer} could be more tolerant to the estimation error of \textit{Estimator}.
Ranked #2 on
Blind Super-Resolution
on DIV2KRK - 4x upscaling
no code implementations • 28 Apr 2021 • Weinan Guan, Wei Wang, Jing Dong, Bo Peng, Tieniu Tan
Maliciously-manipulated images or videos - so-called deep fakes - especially face-swap images and videos have attracted more and more malicious attackers to discredit some key figures.
no code implementations • 21 Apr 2021 • Yueming Lyu, Jing Dong, Bo Peng, Wei Wang, Tieniu Tan
Since human faces are symmetrical in the UV space, we can conveniently remove the undesired shadow and occlusion from the reference image by carefully designing a Flip Attention Module (FAM).
no code implementations • CVPR 2021 • Ya Jing, Tao Kong, Wei Wang, Liang Wang, Lei LI, Tieniu Tan
Referring image segmentation aims to segment the objects referred by a natural language expression.
Generalized Referring Expression Segmentation
Image Segmentation
+2
no code implementations • 29 Mar 2021 • Fenyu Hu, Liping Wang, Shu Wu, Liang Wang, Tieniu Tan
Graph classification is a challenging research problem in many applications across a broad range of domains.
no code implementations • 29 Mar 2021 • Yi-Fan Zhang, Zhang Zhang, Da Li, Zhen Jia, Liang Wang, Tieniu Tan
Generalizable person Re-Identification (ReID) has attracted growing attention in recent computer vision community.
1 code implementation • journal 2021 • Fenyu Hu, Liping Wang, Qiang Liu, Shu Wu, Liang Wang, Tieniu Tan
Graph classification is a challenging research problem in many applications across a broad range of domains.
1 code implementation • 20 Jan 2021 • Yi-Fan Zhang, Weiqiang Ren, Zhang Zhang, Zhen Jia, Liang Wang, Tieniu Tan
(ii) Most of the loss functions ignore the imbalance problem in BBR that the large number of anchor boxes which have small overlaps with the target boxes contribute most to the optimization of BBR.
1 code implementation • CVPR 2021 • Zhengxiong Luo, Zhicheng Wang, Yan Huang, Tieniu Tan, Erjin Zhou
However, for bottom-up methods, which need to handle a large variance of human scales and labeling ambiguities, the current practice seems unreasonable.
no code implementations • 13 Dec 2020 • Zhengxiong Luo, Zhicheng Wang, Yuanhao Cai, GuanAn Wang, Yan Huang, Liang Wang, Erjin Zhou, Tieniu Tan, Jian Sun
Instead, we focus on exploiting multi-scale information from layers with different receptive-field sizes and then making full of use this information by improving the fusion method.
no code implementations • 19 Nov 2020 • Yunfan Liu, Qi Li, Zhenan Sun, Tieniu Tan
Generative Adversarial Networks (GANs) with style-based generators (e. g. StyleGAN) successfully enable semantic control over image synthesis, and recent studies have also revealed that interpretable image translations could be obtained by modifying the latent code.
1 code implementation • NeurIPS 2020 • Zhengxiong Luo, Yan Huang, Shang Li, Liang Wang, Tieniu Tan
More importantly, \textit{Restorer} is trained with the kernel estimated by \textit{Estimator}, instead of ground-truth kernel, thus \textit{Restorer} could be more tolerant to the estimation error of \textit{Estimator}.
Ranked #2 on
Blind Super-Resolution
on Set5 - 2x upscaling
no code implementations • ECCV 2020 • Chenyang Si, Xuecheng Nie, Wei Wang, Liang Wang, Tieniu Tan, Jiashi Feng
Self-supervised learning (SSL) has been proved very effective at learning representations from unlabeled data in the image domain.
no code implementations • 29 Jun 2020 • Shu Wu, Feng Yu, Xueli Yu, Qiang Liu, Liang Wang, Tieniu Tan, Jie Shao, Fan Huang
The CTR (Click-Through Rate) prediction plays a central role in the domain of computational advertising and recommender systems.
Ranked #34 on
Click-Through Rate Prediction
on Criteo
no code implementations • CVPR 2020 • Junran Peng, Xingyuan Bu, Ming Sun, Zhao-Xiang Zhang, Tieniu Tan, Junjie Yan
Training with more data has always been the most stable and effective way of improving performance in deep learning era.
1 code implementation • 6 May 2020 • Feng Yu, Yanqiao Zhu, Qiang Liu, Shu Wu, Liang Wang, Tieniu Tan
However, these methods compress a session into one fixed representation vector without considering the target items to be predicted.
Ranked #3 on
Session-Based Recommendations
on yoochoose1
no code implementations • 20 Apr 2020 • Yi Li, Huaibo Huang, Junchi Yu, Ran He, Tieniu Tan
Face verification aims at determining whether a pair of face images belongs to the same identity.
1 code implementation • 3 Apr 2020 • Bo Peng, Wei Wang, Jing Dong, Tieniu Tan
Learning to reconstruct 3D shapes using 2D images is an active research topic, with benefits of not requiring expensive 3D data.
no code implementations • 22 Feb 2020 • Ziwen He, Wei Wang, Jing Dong, Tieniu Tan
In this paper, we demonstrate that the state-of-the-art gait recognition model is vulnerable to such attacks.
no code implementations • 19 Dec 2019 • Ziwen He, Wei Wang, Xinsheng Xuan, Jing Dong, Tieniu Tan
Thus, in this paper, we propose a new attack mechanism which performs the non-targeted attack when the targeted attack fails.
no code implementations • NeurIPS 2019 • Junran Peng, Ming Sun, Zhao-Xiang Zhang, Tieniu Tan, Junjie Yan
Instead of searching and constructing an entire network, NATS explores the architecture space on the base of existing network and reusing its weights.
3 code implementations • 1 Dec 2019 • Min Ren, Yunlong Wang, Zhenan Sun, Tieniu Tan
During dynamic graph matching, we propose a novel strategy to measure the distances of both nodes and adjacent matrixes.
no code implementations • 1 Dec 2019 • Min Ren, Caiyong Wang, Yunlong Wang, Zhenan Sun, Tieniu Tan
And illumination variations may cause irregular distortion of iris texture.
no code implementations • 15 Nov 2019 • Yunfan Liu, Qi Li, Zhenan Sun, Tieniu Tan
Face aging, which aims at aesthetically rendering a given face to predict its future appearance, has received significant research attention in recent years.
1 code implementation • 5 Nov 2019 • Fenyu Hu, Yanqiao Zhu, Shu Wu, Weiran Huang, Liang Wang, Tieniu Tan
Then, in order to better capture the complicated non-linearity of graph data, we present a novel GraphAIR framework which models the neighborhood interaction in addition to neighborhood aggregation.
no code implementations • 5 Sep 2019 • Junran Peng, Ming Sun, Zhao-Xiang Zhang, Tieniu Tan, Junjie Yan
With the combination of these two designs, an architecture transformation scheme could be discovered to adapt a network designed for image classification to task of object detection.
no code implementations • ICCV 2019 • Junran Peng, Ming Sun, Zhao-Xiang Zhang, Tieniu Tan, Junjie Yan
Scale-sensitive object detection remains a challenging task, where most of the existing methods could not learn it explicitly and are not robust to scale variance.
no code implementations • 10 Jun 2019 • Chenyang Si, Wentao Chen, Wei Wang, Liang Wang, Tieniu Tan
Furthermore, the inter-class classification and the intra-class transduction are extremely flexible to be repeated several times to progressively purify the clusters.
no code implementations • 7 Apr 2019 • Jie Gui, Tongliang Liu, Zhenan Sun, DaCheng Tao, Tieniu Tan
Rather than adopting this method, FSDH uses a very simple yet effective regression of the class labels of training examples to the corresponding hash code to accelerate the algorithm.
no code implementations • 7 Apr 2019 • Jie Gui, Tongliang Liu, Zhenan Sun, DaCheng Tao, Tieniu Tan
In SDHR, the regression target is instead optimized.
2 code implementations • CVPR 2019 • Xuecai Hu, Haoyuan Mu, Xiangyu Zhang, Zilei Wang, Tieniu Tan, Jian Sun
In this work, we propose a novel method called Meta-SR to firstly solve super-resolution of arbitrary scale factor (including non-integer scale factors) with a single model.
no code implementations • CVPR 2019 • Chenyang Si, Wentao Chen, Wei Wang, Liang Wang, Tieniu Tan
Nevertheless, how to effectively extract discriminative spatial and temporal features is still a challenging problem.
Ranked #63 on
Skeleton Based Action Recognition
on NTU RGB+D
1 code implementation • 13 Feb 2019 • Fenyu Hu, Yanqiao Zhu, Shu Wu, Liang Wang, Tieniu Tan
Graph convolutional networks (GCNs) have been successfully applied in node classification tasks of network mining.
no code implementations • 10 Feb 2019 • Ran He, Jie Cao, Lingxiao Song, Zhenan Sun, Tieniu Tan
This paper models high resolution heterogeneous face synthesis as a complementary combination of two components, a texture inpainting component and pose correction component.
1 code implementation • 27 Nov 2018 • Junsong Fan, Zhao-Xiang Zhang, Tieniu Tan, Chunfeng Song, Jun Xiao
Weakly supervised semantic segmentation with only image-level labels saves large human effort to annotate pixel-level labels.
Ranked #6 on
2D Object Detection
on DroneVehicle
(Val/mAP50 metric)
8 code implementations • 1 Nov 2018 • Shu Wu, Yuyuan Tang, Yanqiao Zhu, Liang Wang, Xing Xie, Tieniu Tan
To obtain accurate item embedding and take complex transitions of items into account, we propose a novel method, i. e. Session-based Recommendation with Graph Neural Networks, SR-GNN for brevity.
Ranked #1 on
Session-Based Recommendations
on Gowalla
no code implementations • 22 Sep 2018 • Ya Jing, Chenyang Si, Jun-Bo Wang, Wei Wang, Liang Wang, Tieniu Tan
To exploit the multilevel corresponding visual contents, we propose a pose-guided multi-granularity attention network (PMA).
no code implementations • 7 Sep 2018 • Junran Peng, Lingxi Xie, Zhao-Xiang Zhang, Tieniu Tan, Jingdong Wang
This paper presents an efficient module named spatial bottleneck for accelerating the convolutional layers in deep neural networks.
no code implementations • ECCV 2018 • Yunlong Wang, Fei Liu, Zilei Wang, Guangqi Hou, Zhenan Sun, Tieniu Tan
Limited angular resolution has become the main bottleneck of microlens-based plenoptic cameras towards practical vision applications.
3 code implementations • NeurIPS 2018 • Huaibo Huang, Zhihang Li, Ran He, Zhenan Sun, Tieniu Tan
On the other hand, the inference model is encouraged to classify between the generated and real samples while the generator tries to fool it as GANs.
no code implementations • 11 Jul 2018 • Huaibo Huang, Lingxiao Song, Ran He, Zhenan Sun, Tieniu Tan
Variational capsules model an image as a composition of entities in a probabilistic model.
no code implementations • 27 Jun 2018 • Wei Wang, Jing Dong, Yinlong Qian, Tieniu Tan
Recently, deep learning has shown its power in steganalysis.
1 code implementation • 8 Jun 2018 • Jiedong Hao, Jing Dong, Wei Wang, Tieniu Tan
There are great demands for automatically regulating inappropriate appearance of shocking firearm images in social media or identifying firearm types in forensics.
no code implementations • CVPR 2018 • Junbo Wang, Wei Wang, Yan Huang, Liang Wang, Tieniu Tan
Inspired by the facts that memory modelling poses potential advantages to long-term sequential problems [35] and working memory is the key factor of visual attention [33], we propose a Multimodal Memory Model (M3) to describe videos, which builds a visual and textual shared memory to model the long-term visual-textual dependency and further guide visual attention on described visual targets to solve visual-textual alignments.
no code implementations • CVPR 2018 • Chenyang Si, Wei Wang, Liang Wang, Tieniu Tan
Human image synthesis has extensive practical applications e. g. person re-identification and data augmentation for human pose estimation.
no code implementations • ECCV 2018 • Chenyang Si, Ya Jing, Wei Wang, Liang Wang, Tieniu Tan
Skeleton-based action recognition has made great progress recently, but many problems still remain unsolved.
Ranked #96 on
Skeleton Based Action Recognition
on NTU RGB+D
no code implementations • 10 Dec 2017 • Lingxiao Song, Zhihe Lu, Ran He, Zhenan Sun, Tieniu Tan
An expression invariant face recognition experiment is also performed to further show the advantages of our proposed method.
no code implementations • ICCV 2017 • Huaibo Huang, Ran He, Zhenan Sun, Tieniu Tan
Most modern face super-resolution methods resort to convolutional neural networks (CNN) to infer high-resolution (HR) face images.
Ranked #3 on
Face Hallucination
on FFHQ 512 x 512 - 16x upscaling
no code implementations • 12 Sep 2017 • Yi Li, Lingxiao Song, Xiang Wu, Ran He, Tieniu Tan
This paper proposes a learning from generation approach for makeup-invariant face verification by introducing a bi-level adversarial network (BLAN).
no code implementations • 8 Aug 2017 • Ran He, Xiang Wu, Zhenan Sun, Tieniu Tan
To avoid the over-fitting problem on small-scale heterogeneous face data, a correlation prior is introduced on the fully-connected layers of WCNN network to reduce parameter space.
Ranked #3 on
Face Verification
on BUAA-VisNir
no code implementations • CVPR 2017 • Zhen Zhou, Yan Huang, Wei Wang, Liang Wang, Tieniu Tan
Accordingly, a demanding need is to recognize a person under different cameras, which is called person re-identification.
no code implementations • NeurIPS 2017 • Qi Li, Zhenan Sun, Ran He, Tieniu Tan
Benefit from recent advances in deep learning, deep hashing methods have achieved promising results for image retrieval.
no code implementations • 8 Apr 2017 • Xiang Wu, Lingxiao Song, Ran He, Tieniu Tan
CDL seeks a shared feature space in which the heterogeneous face matching problem can be approximately treated as a homogeneous face matching problem.
no code implementations • 17 Nov 2016 • Junbo Wang, Wei Wang, Yan Huang, Liang Wang, Tieniu Tan
In this paper, we propose a Multimodal Memory Model (M3) to describe videos, which builds a visual and textual shared memory to model the long-term visual-textual dependency and further guide global visual attention on described targets.
no code implementations • 16 Nov 2016 • Shu Zhang, Ran He, Tieniu Tan
The occlusions incurred by random meshes severely degenerate the performance of face verification systems, which raises the MeshFace verification problem between MeshFace and daily photos.
1 code implementation • 5 Nov 2016 • Jiedong Hao, Jing Dong, Wei Wang, Tieniu Tan
Based on the evaluation results, we also identify the best choices for different factors and propose a new multi-scale image feature representation method to encode the image effectively.
no code implementations • 29 Sep 2016 • Qiang Liu, Shu Wu, Feng Yu, Liang Wang, Tieniu Tan
In this paper, we propose a novel representation learning method, Information Credibility Evaluation (ICE), to learn representations of information credibility on social media.
no code implementations • CVPR 2016 • Zhang Zhang, Kaiqi Huang, Tieniu Tan, Peipei Yang, Jun Li
For spectral embedding/clustering, it is still an open problem on how to construct an relation graph to reflect the intrinsic structures in data.
19 code implementations • 9 Nov 2015 • Xiang Wu, Ran He, Zhenan Sun, Tieniu Tan
This paper presents a Light CNN framework to learn a compact embedding on the large-scale face data with massive noisy labels.
Ranked #2 on
Age-Invariant Face Recognition
on CAFR
no code implementations • 9 Jul 2015 • Ran He, Tieniu Tan, Larry Davis, Zhenan Sun
This paper presents a structured ordinal measure method for video-based face recognition that simultaneously learns ordinal filters and structured ordinal features.
no code implementations • CVPR 2015 • Fang Zhao, Yongzhen Huang, Liang Wang, Tieniu Tan
Research efforts have been devoted to learning compact binary codes that preserve semantic similarity based on labels.
no code implementations • CVPR 2014 • Kangwei Liu, Junge Zhang, Kaiqi Huang, Tieniu Tan
The MRF energy function is derived from the deformation decomposition model.
no code implementations • NeurIPS 2013 • Fang Zhao, Yongzhen Huang, Liang Wang, Tieniu Tan
Unstructured social group activity recognition in web videos is a challenging task due to 1) the semantic gap between class labels and low-level visual features and 2) the lack of labeled training data.