1 code implementation • 3 Feb 2024 • Xinyu Peng, Ziyang Zheng, Wenrui Dai, Nuoqian Xiao, Chenglin Li, Junni Zou, Hongkai Xiong
In this paper, we propose the first unified interpretation for existing zero-shot methods from the perspective of approximating the conditional posterior mean for the reverse diffusion process of conditional sampling.
no code implementations • 12 Jan 2024 • Bowen Shi, Peisen Zhao, Zichen Wang, Yuhang Zhang, Yaoming Wang, Jin Li, Wenrui Dai, Junni Zou, Hongkai Xiong, Qi Tian, Xiaopeng Zhang
Vision-language foundation models, represented by Contrastive language-image pre-training (CLIP), have gained increasing attention for jointly understanding both vision and textual tasks.
no code implementations • 16 Dec 2023 • Rui Yang, Wenrui Dai, Chenglin Li, Junni Zou, Dapeng Wu, Hongkai Xiong
A gene-level GNN is established to adaptively learn gene-gene interactions and cell representations via the self-attention mechanism, and a cell-level GNN builds on the cell-cell graph that is constructed from the cell representations generated by the gene-level GNN.
no code implementations • 16 Dec 2023 • Rui Yang, Wenrui Dai, Huajun She, Yiping P. Du, Dapeng Wu, Hongkai Xiong
To address these issues in an end-to-end manner, we model the brain network as a directed acyclic graph (DAG) to discover direct causal connections between brain regions and propose Spatial-Temporal DAG Convolutional Network (ST-DAGCN) to jointly infer effective connectivity and classify rs-fMRI time series by learning brain representations based on nonlinear structural equation model.
no code implementations • 7 Dec 2023 • Yabo Chen, Jiemin Fang, YuYang Huang, Taoran Yi, Xiaopeng Zhang, Lingxi Xie, Xinggang Wang, Wenrui Dai, Hongkai Xiong, Qi Tian
We propose a cascade generation framework constructed with two Zero-1-to-3 models, named Cascade-Zero123, to tackle this issue, which progressively extracts 3D information from the source image.
1 code implementation • NeurIPS 2023 • Jin Li, Yaoming Wang, Xiaopeng Zhang, Bowen Shi, Dongsheng Jiang, Chenglin Li, Wenrui Dai, Hongkai Xiong, Qi Tian
Specifically, at the intermediate layer of the ViT, we utilize a spatial-aware density-based clustering algorithm to select representative tokens from the token sequence.
1 code implementation • 25 Oct 2023 • Han Li, Shaohui Li, Wenrui Dai, Chenglin Li, Junni Zou, Hongkai Xiong
Learned image compression (LIC) has gained traction as an effective solution for image storage and transmission in recent years.
no code implementations • 18 Jul 2023 • Hongwei Zheng, Han Li, Bowen Shi, Wenrui Dai, Botao Wan, Yu Sun, Min Guo, Hongkai Xiong
Recent 2D-to-3D human pose estimation (HPE) utilizes temporal consistency across sequences to alleviate the depth ambiguity problem but ignore the action related prior knowledge hidden in the pose sequence.
no code implementations • 28 Jun 2023 • Bowen Shi, Xiaopeng Zhang, Yaoming Wang, Jin Li, Wenrui Dai, Junni Zou, Hongkai Xiong, Qi Tian
In order to better obtain both discrimination and diversity, we propose a simple but effective Hybrid Distillation strategy, which utilizes both the supervised/CL teacher and the MIM teacher to jointly guide the student model.
no code implementations • 5 Mar 2023 • Jixiang Luo, Shaohui Li, Wenrui Dai, Chenglin Li, Junni Zou, Hongkai Xiong
In this paper, we propose a novel framework for learned lossless compression of JPEG images that achieves end-to-end optimized prediction of the distribution of decoded DCT coefficients.
no code implementations • 15 Feb 2023 • Han Li, Bowen Shi, Wenrui Dai, Hongwei Zheng, Botao Wang, Yu Sun, Min Guo, Chenlin Li, Junni Zou, Hongkai Xiong
There has been a recent surge of interest in introducing transformers to 3D human pose estimation (HPE) due to their powerful capabilities in modeling long-term dependencies.
no code implementations • ICCV 2023 • Yuchen Liu, Yabo Chen, Mengran Gou, Chun-Ting Huang, Yaoming Wang, Wenrui Dai, Hongkai Xiong
In this paper, we propose the first Unsupervised Domain Generalization framework for Face Anti-Spoofing, namely UDG-FAS, which could exploit large amounts of easily accessible unlabeled data to learn generalizable features for enhancing the low-data regime of FAS.
1 code implementation • CVPR 2023 • Yaoming Wang, Bowen Shi, Xiaopeng Zhang, Jin Li, Yuchen Liu, Wenrui Dai, Chenglin Li, Hongkai Xiong, Qi Tian
To mitigate the computational and storage demands, recent research has explored Parameter-Efficient Fine-Tuning (PEFT), which focuses on tuning a minimal number of parameters for efficient adaptation.
no code implementations • CVPR 2023 • Yuchen Liu, Yaoming Wang, Yabo Chen, Wenrui Dai, Chenglin Li, Junni Zou, Hongkai Xiong
Then, we propose a novel unsupervised domain generalization approach, namely Dual Nearest Neighbors contrastive learning with strong Augmentation (DN^2A).
1 code implementation • 31 Jul 2022 • Yabo Chen, Yuchen Liu, Dongsheng Jiang, Xiaopeng Zhang, Wenrui Dai, Hongkai Xiong, Qi Tian
We also analyze how to build good views for the teacher branch to produce latent representation from the perspective of information bottleneck.
no code implementations • 31 May 2022 • Mingxing Xu, Chenglin Li, Wenrui Dai, Siheng Chen, Junni Zou, Pascal Frossard, Hongkai Xiong
Specifically, adaptive spherical wavelets are learned with a lifting structure that consists of trainable lifting operators (i. e., update and predict operators).
no code implementations • 27 Apr 2022 • Mingxing Xu, Wenrui Dai, Chenglin Li, Junni Zou, Hongkai Xiong
Subsequently, this local information is aligned and propagated to the preserved nodes to alleviate information loss in graph coarsening.
1 code implementation • 25 Apr 2022 • Ziyang Zheng, Wenrui Dai, Duoduo Xue, Chenglin Li, Junni Zou, Hongkai Xiong
This framework is general to endow arbitrary DNNs for solving linear inverse problems with convergence guarantees.
no code implementations • CVPR 2022 • Yaoming Wang, Yangzhou Jiang, Jin Li, Bingbing Ni, Wenrui Dai, Chenglin Li, Hongkai Xiong, Teng Li
Appearance-based Gaze Estimation leverages deep neural networks to regress the gaze direction from monocular images and achieve impressive performance.
1 code implementation • 23 Nov 2021 • Han Li, Bowen Shi, Wenrui Dai, Yabo Chen, Botao Wang, Yu Sun, Min Guo, Chenlin Li, Junni Zou, Hongkai Xiong
Recent 2D-to-3D human pose estimation works tend to utilize the graph structure formed by the topology of the human skeleton.
Ranked #42 on 3D Human Pose Estimation on MPI-INF-3DHP (AUC metric)
no code implementations • 29 Sep 2021 • Xing Gao, Wenrui Dai, Chenglin Li, Junni Zou, Hongkai Xiong, Pascal Frossard
Graph convolutional networks have been a powerful tool in representation learning of networked data.
no code implementations • 29 Sep 2021 • Yuankun Jiang, Chenglin Li, Wenrui Dai, Junni Zou, Hongkai Xiong
In this paper, we theoretically derive a bias-free and state/environment-dependent optimal baseline for DR, and analytically show its ability to achieve further variance reduction over the standard constant and state-dependent baselines for DR. We further propose a variance reduced domain randomization (VRDR) approach for policy gradient methods, to strike a tradeoff between the variance reduction and computational complexity in practice.
no code implementations • 29 Sep 2021 • Jin Li, Yaoming Wang, Dongsheng Jiang, Xiaopeng Zhang, Wenrui Dai, Hongkai Xiong
To address this issue, we introduce the information bottleneck principle and propose the Self-supervised Variational Information Bottleneck (SVIB) learning framework.
no code implementations • 3 Aug 2021 • Mingxing Xu, Wenrui Dai, Chenglin Li, Junni Zou, Hongkai Xiong, Pascal Frossard
To ensure that the learned graph representations are invariant to node permutations, a layer is employed at the input of the networks to reorder the nodes according to their local topology information.
1 code implementation • ICLR 2022 • Haohang Xu, Jiemin Fang, Xiaopeng Zhang, Lingxi Xie, Xinggang Wang, Wenrui Dai, Hongkai Xiong, Qi Tian
Here bag of instances indicates a set of similar samples constructed by the teacher and are grouped within a bag, and the goal of distillation is to aggregate compact representations over the student with respect to instances in a bag.
no code implementations • 18 Jun 2021 • Xing Gao, Wenrui Dai, Chenglin Li, Junni Zou, Hongkai Xiong, Pascal Frossard
Furthermore, each filter in the spectral domain corresponds to a message passing scheme, and diverse schemes are implemented via the filter bank.
no code implementations • 8 Jun 2021 • Bowen Shi, Xiaopeng Zhang, Haohang Xu, Wenrui Dai, Junni Zou, Hongkai Xiong, Qi Tian
This is achieved by first pretraining the network via the proposed pixel-to-prototype contrastive loss over multiple datasets regardless of their taxonomy labels, and followed by fine-tuning the pretrained model over specific dataset as usual.
no code implementations • 1 Jan 2021 • Yuankun Jiang, Chenglin Li, Junni Zou, Wenrui Dai, Hongkai Xiong
To address this, in this paper, we propose a Bayesian linear regression with informative prior (IP-BLR) operator to leverage the data-dependent prior in the learning process of randomized value function, which can leverage the statistics of training results from previous iterations.
no code implementations • 1 Jan 2021 • Yuankun Jiang, Chenglin Li, Junni Zou, Wenrui Dai, Hongkai Xiong
To mitigate the model discrepancy between training and target (testing) environments, domain randomization (DR) can generate plenty of environments with a sufficient diversity by randomly sampling environment parameters in simulator.
no code implementations • ICCV 2021 • Yaoming Wang, Yuchen Liu, Wenrui Dai, Chenglin Li, Junni Zou, Hongkai Xiong
Existing differentiable neural architecture search approaches simply assume the architectural distribution on each edge is independent of each other, which conflicts with the intrinsic properties of architecture.
no code implementations • 1 Jan 2021 • Rui Yang, Wenrui Dai, Chenglin Li, Junni Zou, Hongkai Xiong
In the variational E-step, graph topology is optimized by approximating the posterior probability distribution of the latent adjacency matrix with a neural network learned from node embeddings.
no code implementations • 7 Dec 2020 • Rui Yang, Wenrui Dai, Chenglin Li, Junni Zou, Hongkai Xiong
Therefore, it can relieve the over-smoothing issue and learn effective node representations over graphs with homophily or heterophily.
1 code implementation • 19 Oct 2020 • Wen Fei, Wenrui Dai, Chenglin Li, Junni Zou, Hongkai Xiong
We leverage the neural tangent kernel (NTK) theory to prove that our weight mean operation whitens activations and transits network into the chaotic regime like BN layer, and consequently, leads to an enhanced convergence.
no code implementations • 19 Jun 2020 • Xing Gao, Wenrui Dai, Chenglin Li, Hongkai Xiong, Pascal Frossard
In this paper, we propose a novel graph pooling strategy that leverages node proximity to improve the hierarchical representation learning of graph data with their multi-hop topology.
1 code implementation • 9 Jan 2020 • Mingxing Xu, Wenrui Dai, Chunmiao Liu, Xing Gao, Weiyao Lin, Guo-Jun Qi, Hongkai Xiong
In this paper, we propose a novel paradigm of Spatial-Temporal Transformer Networks (STTNs) that leverages dynamical directed spatial dependencies and long-range temporal dependencies to improve the accuracy of long-term traffic forecasting.
1 code implementation • 9 Oct 2019 • Yuhui Xu, Yuxi Li, Shuai Zhang, Wei Wen, Botao Wang, Wenrui Dai, Yingyong Qi, Yiran Chen, Weiyao Lin, Hongkai Xiong
To accelerate DNNs inference, low-rank approximation has been widely adopted because of its solid theoretical rationale and efficient implementations.