1 code implementation • 1 Apr 2024 • Ruowen Zhao, Zhengyi Wang, Yikai Wang, Zihan Zhou, Jun Zhu
However, due to the challenge of directly deforming the mesh representation to approach the target topology, most methodologies learn an implicit representation (such as NeRF) during the sparse-view reconstruction and acquire the target mesh by a post-processing extraction.
no code implementations • 1 Apr 2024 • Ling Wang, Runfa Chen, Yikai Wang, Fuchun Sun, Xinzhou Wang, Sun Kai, Guangyuan Fu, Jianwei Zhang, Wenbing Huang
Based on the assumption of local rigidity, one solution for reducing complexity is to decompose the overall shape into independent local regions using Local Reference Frames (LRFs) that are invariant to SE(3) transformations.
no code implementations • 27 Mar 2024 • Jingyang Huo, Yikai Wang, Xuelin Qian, Yun Wang, Chong Li, Jianfeng Feng, Yanwei Fu
Recent fMRI-to-image approaches mainly focused on associating fMRI signals with specific conditions of pre-trained diffusion models.
no code implementations • 21 Mar 2024 • Junliang Ye, Fangfu Liu, Qixiu Li, Zhengyi Wang, Yikai Wang, Xinzhou Wang, Yueqi Duan, Jun Zhu
Building upon the 3D reward model, we finally perform theoretical analysis and present the Reward3D Feedback Learning (DreamFL), a direct tuning algorithm to optimize the multi-view diffusion models with a redefined scorer.
1 code implementation • 15 Mar 2024 • Pengkun Liu, Yikai Wang, Fuchun Sun, Jiafang Li, Hang Xiao, Hongxiang Xue, Xinzhou Wang
As a result, with a single image CLIP embedding, Isotropic3D is capable of generating multi-view mutually consistent images and also a 3D model with more symmetrical and neat content, well-proportioned geometry, rich colored texture, and less distortion compared with existing image-to-3D methods while still preserving the similarity to the reference image to a large extent.
2 code implementations • 11 Mar 2024 • Zilong Chen, Yikai Wang, Feng Wang, Zhengyi Wang, Huaping Liu
To fully unleash the potential of video diffusion to perceive the 3D world, we further introduce geometrical consistency prior and extend the video diffusion model to a multi-view consistent 3D generator.
no code implementations • 8 Mar 2024 • Zhengyi Wang, Yikai Wang, Yifei Chen, Chendong Xiang, Shuo Chen, Dajiang Yu, Chongxuan Li, Hang Su, Jun Zhu
In this work, we present the Convolutional Reconstruction Model (CRM), a high-fidelity feed-forward single image-to-3D generative model.
1 code implementation • 30 Jan 2024 • Yikai Wang, Chenjie Cao, Ke Fan, Qiaole Dong, YiFan Li, xiangyang xue, Yanwei Fu
Our research reveals that the fundamental sub-tasks of subject repositioning, which include filling the void left by the repositioned subject, reconstructing obscured portions of the subject and blending the subject to be consistent with surrounding areas, can be effectively reformulated as a unified, prompt-guided inpainting task.
1 code implementation • 8 Dec 2023 • Yikai Wang, Chenjie Cao, Ke Fan Xiangyang Xue Yanwei Fu
Recent progress in inpainting increasingly relies on generative models, leveraging their strong generation capabilities for addressing large irregular masks.
no code implementations • 6 Dec 2023 • Xinzhou Wang, Yikai Wang, Junliang Ye, Zhengyi Wang, Fuchun Sun, Pengkun Liu, Ling Wang, Kai Sun, Xintong Wang, Bin He
Extensive experiments demonstrate the capability of our method in generating high-flexibility text-guided 3D models from the monocular video, while also showing improved reconstruction performance over existing non-rigid reconstruction methods.
1 code implementation • 24 Nov 2023 • YiWen Chen, Zilong Chen, Chi Zhang, Feng Wang, Xiaofeng Yang, Yikai Wang, Zhongang Cai, Lei Yang, Huaping Liu, Guosheng Lin
3D editing plays a crucial role in many areas such as gaming and virtual reality.
1 code implementation • 6 Nov 2023 • Jianhui Li, Shilong Liu, Zidong Liu, Yikai Wang, Kaiwen Zheng, Jinghui Xu, Jianmin Li, Jun Zhu
With the success of Neural Radiance Field (NeRF) in 3D-aware portrait editing, a variety of works have achieved promising results regarding both quality and 3D consistency.
1 code implementation • 28 Sep 2023 • Zilong Chen, Feng Wang, Yikai Wang, Huaping Liu
Specifically, our method adopts a progressive optimization strategy, which includes a geometry optimization stage and an appearance refinement stage.
1 code implementation • ICCV 2023 • Jianxiong Gao, Xuelin Qian, Yikai Wang, Tianjun Xiao, Tong He, Zheng Zhang, Yanwei Fu
To address this issue, we propose a convolution refine module to inject fine-grained information and provide a more precise amodal object segmentation based on visual features and coarse-predicted segmentation.
no code implementations • ICCV 2023 • Yikai Wang, Yinpeng Dong, Fuchun Sun, Xiao Yang
The key idea of our method, Root Pose Decomposition (RPD), is to maintain a per-frame root pose transformation, meanwhile building a dense field with local transformations to rectify the root pose.
2 code implementations • 9 Aug 2023 • Peike Li, BoYu Chen, Yao Yao, Yikai Wang, Allen Wang, Alex Wang
Despite the task's significance, prevailing generative models exhibit limitations in music quality, computational efficiency, and generalization.
Ranked #1 on Text-to-Music Generation on MusicCaps
1 code implementation • 6 Jun 2023 • Fusheng Hao, Fengxiang He, Yikai Wang, Fuxiang Wu, Jing Zhang, Jun Cheng, DaCheng Tao
Massive human-related data is collected to train neural networks for computer vision tasks.
2 code implementations • NeurIPS 2023 • Zhengyi Wang, Cheng Lu, Yikai Wang, Fan Bao, Chongxuan Li, Hang Su, Jun Zhu
In comparison, VSD works well with various CFG weights as ancestral sampling from diffusion models and simultaneously improves the diversity and sample quality with a common CFG weight (i. e., $7. 5$).
3 code implementations • 19 May 2023 • Chenjie Cao, Yunuo Cai, Qiaole Dong, Yikai Wang, Yanwei Fu
As an exemplar, we leverage LeftRefill to address two different challenges: reference-guided inpainting and novel view synthesis, based on the pre-trained StableDiffusion.
1 code implementation • 14 May 2023 • ZiHao Wang, Le Ma, Chen Zhang, Bo Han, Yunfei Xu, Yikai Wang, Xinyi Chen, HaoRong Hong, Wenbo Liu, Xinda Wu, Kejun Zhang
Music as an emotional intervention medium has important applications in scenarios such as music therapy, games, and movies.
no code implementations • 21 Apr 2023 • Yikai Wang, Zheyuan Jiang, Jianyu Chen
In this paper, we propose a new framework for learning robust, agile and natural legged locomotion skills over challenging terrain.
1 code implementation • CVPR 2023 • Xiao Yang, Chang Liu, Longlong Xu, Yikai Wang, Yinpeng Dong, Ning Chen, Hang Su, Jun Zhu
The goal of this work is to develop a more reliable technique that can carry out an end-to-end evaluation of adversarial robustness for commercial systems.
no code implementations • 26 Mar 2023 • Xuelin Qian, Yikai Wang, Yanwei Fu, Xinwei Sun, xiangyang xue, Jianfeng Feng
Our Latent Embedding Alignment (LEA) model concurrently recovers visual stimuli from fMRI signals and predicts brain activity from images within a unified framework.
no code implementations • CVPR 2023 • Yikai Wang, Wenbing Huang, Yinpeng Dong, Fuchun Sun, Anbang Yao
Binary Neural Network (BNN) represents convolution weights with 1-bit values, which enhances the efficiency of storage and computation.
no code implementations • 20 Mar 2023 • Yinpeng Dong, Caixin Kang, Jinlai Zhang, Zijian Zhu, Yikai Wang, Xiao Yang, Hang Su, Xingxing Wei, Jun Zhu
3D object detection is an important task in autonomous driving to perceive the surroundings.
1 code implementation • 22 Feb 2023 • Yikai Wang, Jianan Wang, Guansong Lu, Hang Xu, Zhenguo Li, Wei zhang, Yanwei Fu
In the image manipulation phase, SeMani adopts a generative model to synthesize new images conditioned on the entity-irrelevant regions and target text descriptions.
1 code implementation • 2 Jan 2023 • Yikai Wang, Yanwei Fu, Xinwei Sun
While Knockoffs-SPR can be regarded as a sample selection module for a standard supervised training pipeline, we further combine it with a semi-supervised algorithm to exploit the support of noisy data as unlabeled data.
Ranked #1 on Learning with noisy labels on Clothing1M
1 code implementation • CVPR 2023 • Yinpeng Dong, Caixin Kang, Jinlai Zhang, Zijian Zhu, Yikai Wang, Xiao Yang, Hang Su, Xingxing Wei, Jun Zhu
3D object detection is an important task in autonomous driving to perceive the surroundings.
2 code implementations • CVPR 2022 • Yikai Wang, TengQi Ye, Lele Cao, Wenbing Huang, Fuchun Sun, Fengxiang He, DaCheng Tao
Recently, there is a trend of leveraging multiple sources of input data, such as complementing the 3D point cloud with 2D images that often have richer color and fewer noises.
no code implementations • 13 Sep 2022 • ZiHao Wang, Qihao Liang, Kejun Zhang, Yuxing Wang, Chen Zhang, Pengfei Yu, Yongsheng Feng, Wenbo Liu, Yikai Wang, Yuntai Bao, Yiheng Yang
In this paper, we propose SongDriver, a real-time music accompaniment generation system without logical latency nor exposure bias.
no code implementations • 17 Jul 2022 • Ke Fan, Yikai Wang, Qian Yu, Da Li, Yanwei Fu
In contrast, this paper proposes a simple Test-time Linear Training (ETLT) method for OOD detection.
Out-of-Distribution Detection Out of Distribution (OOD) Detection
11 code implementations • journal 2022 • Yikai Wang, Xinghao Chen, Lele Cao, Wenbing Huang, Fuchun Sun, Yunhe Wang
Many adaptations of transformers have emerged to address the single-modal vision tasks, where self-attention modules are stacked to handle input sources like images.
Ranked #1 on Semantic Segmentation on SUN-RGBD (using extra training data)
1 code implementation • CVPR 2022 • Yikai Wang, Xinwei Sun, Yanwei Fu
Noisy training set usually leads to the degradation of generalization and robustness of neural networks.
Ranked #4 on Learning with noisy labels on Clothing1M
1 code implementation • ICLR 2022 • Yinfeng Yu, Wenbing Huang, Fuchun Sun, Changan Chen, Yikai Wang, Xiaohong Liu
In this work, we design an acoustically complex environment in which, besides the target sound, there exists a sound attacker playing a zero-sum game with the agent.
1 code implementation • 4 Dec 2021 • Yikai Wang, Fuchun Sun, Wenbing Huang, Fengxiang He, DaCheng Tao
For the application of dense image prediction, the validity of CEN is tested by four different scenarios: multimodal fusion, cycle multimodal fusion, multitask learning, and multimodal multitask learning.
Ranked #7 on Semantic Segmentation on LLRGBD-synthetic
1 code implementation • ICCV 2021 • Yikai Wang, Yi Yang, Fuchun Sun, Anbang Yao
In the low-bit quantization field, training Binary Neural Networks (BNNs) is the extreme solution to ease the deployment of deep models on resource-constrained devices, having the lowest storage cost and significantly cheaper bit-wise operations compared to 32-bit floating-point counterparts.
no code implementations • 29 Sep 2021 • Yikai Wang, Xinwei Sun, Yanwei Fu
Specifically, we re-purpose a sparse linear model with incidental parameters as a unified Relative Instance Credibility Inference (RICI) framework, which will detect and remove outliers in the forward pass of each mini-batch and use the remaining instances to train the network.
2 code implementations • 11 Aug 2021 • Yikai Wang, Wenbing Huang, Bin Fang, Fuchun Sun, Chang Li
By contrast, EIP models the tactile sensor as a group of coordinated particles, and the elastic property is applied to regulate the deformation of particles during contact.
1 code implementation • 11 Aug 2021 • Yikai Wang, Fuchun Sun, Ming Lu, Anbang Yao
We propose a compact and effective framework to fuse multimodal features at multiple layers in a single network.
Ranked #42 on Semantic Segmentation on NYU Depth v2
no code implementations • 1 Jan 2021 • Lujun Li, Yikai Wang, Anbang Yao, Yi Qian, Xiao Zhou, Ke He
In this paper, we present Explicit Connection Distillation (ECD), a new KD framework, which addresses the knowledge distillation problem in a novel perspective of bridging dense intermediate feature connections between a student network and its corresponding teacher generated automatically in the training, achieving knowledge transfer goal via direct cross-network layer-to-layer gradients propagation, without need to define complex distillation losses and assume a pre-trained teacher model to be available.
no code implementations • 30 Nov 2020 • Yikai Wang, Weijian Li
We found that by mapping different word embeddings into the joint component, sentiment performance can be greatly improved for the original word embeddings with lower performance.
no code implementations • 23 Nov 2020 • Yikai Wang, Wenbing Huang, Bin Fang, Fuchun Sun
At its core, EIP models the tactile sensor as a group of coordinated particles, and the elastic theory is applied to regulate the deformation of particles during the contact process.
1 code implementation • NeurIPS 2020 • Yikai Wang, Wenbing Huang, Fuchun Sun, Tingyang Xu, Yu Rong, Junzhou Huang
Deep multimodal fusion by using multiple sources of data for classification or regression has exhibited a clear advantage over the unimodal counterpart on various applications.
no code implementations • 19 Aug 2020 • Yikai Wang, Ying Guo
In this paper, we propose a novel blind source separation method with low-rank structure and uniform sparsity (LOCUS) as a fully data-driven decomposition method for network measures.
1 code implementation • ECCV 2020 • Yikai Wang, Fuchun Sun, Duo Li, Anbang Yao
We propose a general method to train a single convolutional neural network which is capable of switching image resolutions at inference.
2 code implementations • 15 Jul 2020 • Yikai Wang, Li Zhang, Yuan YAO, Yanwei Fu
We rank the credibility of pseudo-labeled instances along the regularization path of their corresponding incidental parameters, and the most trustworthy pseudo-labeled examples are preserved as the augmented labeled instances.
1 code implementation • CVPR 2020 • Yikai Wang, Chengming Xu, Chen Liu, Li Zhang, Yanwei Fu
To measure the credibility of each pseudo-labeled instance, we then propose to solve another linear regression hypothesis by increasing the sparsity of the incidental parameters and rank the pseudo-labeled instances with their sparsity degree.
no code implementations • 3 Nov 2019 • Yikai Wang, Liang Zhang, Quanyu Dai, Fuchun Sun, Bo Zhang, Yang He, Weipeng Yan, Yongjun Bao
In deep CTR models, exploiting users' historical data is essential for learning users' behaviors and interests.