26 citations · 155 across the 33 of their papers we have counts for
33 papers
DNA Family: Boosting Weight-Sharing NAS with Block-Wise Supervisions
Guangrun Wang, Changlin Li, Liuchun Yuan +5
Neural Architecture Search (NAS), aiming at automatically designing neural architectures by machines, has been considered a key step toward automatic machine learning. One notable…
NeRF-VPT: Learning Novel View Representations with Neural Radiance Fields via View Prompt Tuning
Linsheng Chen, Guangrun Wang, Liuchun Yuan +3
Neural Radiance Fields (NeRF) have garnered remarkable success in novel view synthesis. Nonetheless, the task of generating high-quality images for novel views persists as a critic…
AlignMiF: Geometry-Aligned Multimodal Implicit Field for LiDAR-Camera Joint Synthesis
Tao Tang, Guangrun Wang, Yixing Lao +5
Neural implicit fields have been a de facto standard in novel view synthesis. Recently, there exist some methods exploring fusing multiple modalities within a single field, aiming…
Tem-adapter: Adapting Image-Text Pretraining for Video Question Answer
Guangyi Chen, Xiao Liu, Guangrun Wang +4
Video-language pre-trained models have shown remarkable success in guiding video question-answering (VideoQA) tasks. However, due to the length of video sequences, training large-s…
LAW-Diffusion: Complex Scene Generation by Diffusion with Layouts
Binbin Yang, Yi Luo, Ziliang Chen +3
Thanks to the rapid development of diffusion models, unprecedented progress has been witnessed in image synthesis. Prior works mostly rely on pre-trained linguistic models, but a t…
MixReorg: Cross-Modal Mixed Patch Reorganization is a Good Mask Learner for Open-World Semantic Segmentation
Kaixin Cai, Pengzhen Ren, Yi Zhu +5
Recently, semantic segmentation models trained with image-level text supervision have shown promising results in challenging open-world scenarios. However, these models still face…
Language-free Compositional Action Generation via Decoupling Refinement
Xiao Liu, Guangyi Chen, Yansong Tang +3
Composing simple elements into complex concepts is crucial yet challenging, especially for 3D action generation. Existing methods largely rely on extensive neural language annotati…
LiDAR-NeRF: Novel LiDAR View Synthesis via Neural Radiance Fields
Tang Tao, Longfei Gao, Guangrun Wang +7
We introduce a new task, novel view synthesis for LiDAR sensors. While traditional model-based LiDAR simulators with style-transfer neural networks can be applied to render novel v…
ViewCo: Discovering Text-Supervised Segmentation Masks via Multi-View Semantic Consistency
Pengzhen Ren, Changlin Li, Hang Xu +5
Recently, great success has been made in learning visual representations from text supervision, facilitating the emergence of text-supervised semantic segmentation. However, existi…
Traditional Classification Neural Networks are Good Generators: They are Competitive with DDPMs and GANs
Guangrun Wang, Philip H. S. Torr
Classifiers and generators have long been separated. We break down this separation and showcase that conventional neural network classifiers can generate high-quality images of a l…
Structure-Preserving 3D Garment Modeling with Neural Sewing Machines
Xipeng Chen, Guangrun Wang, Dizhong Zhu +3
3D Garment modeling is a critical and challenging topic in the area of computer vision and graphics, with increasing attention focused on garment representation learning, garment r…
Learning Self-Regularized Adversarial Views for Self-Supervised Vision Transformers
Tao Tang, Changlin Li, Guangrun Wang +3
Automatic data augmentation (AutoAugment) strategies are indispensable in supervised data-efficient training protocols of vision transformers, and have led to state-of-the-art resu…
Beyond Fixation: Dynamic Window Visual Transformer
Pengzhen Ren, Changlin Li, Guangrun Wang +4
Recently, a surge of interest in visual transformers is to reduce the computational cost by limiting the calculation of self-attention to a local window. Most current work uses a f…
Automated Progressive Learning for Efficient Training of Vision Transformers
Changlin Li, Bohan Zhuang, Guangrun Wang +3
Recent advances in vision Transformers (ViTs) have come with a voracious appetite for computing power, high-lighting the urgent need to develop efficient training methods for ViTs.…
DS-Net++: Dynamic Weight Slicing for Efficient Inference in CNNs and Transformers
Changlin Li, Guangrun Wang, Bing Wang +3
Dynamic networks have shown their promising capability in reducing theoretical computation complexity by adapting their architectures to the input during inference. However, their…
EfficientBERT: Progressively Searching Multilayer Perceptron via Warm-up Knowledge Distillation
Chenhe Dong, Guangrun Wang, Hang Xu +3
Pre-trained language models have shown remarkable results on various NLP tasks. Nevertheless, due to their bulky size and slow inference speed, it is hard to deploy them on edge de…
Pi-NAS: Improving Neural Architecture Search by Reducing Supernet Training Consistency Shift
Jiefeng Peng, Jiqi Zhang, Changlin Li +3
Recently proposed neural architecture search (NAS) methods co-train billions of architectures in a supernet and estimate their potential accuracy using the network weights detached…
Reward-Adaptive Reinforcement Learning: Dynamic Policy Gradient Optimization for Bipedal Locomotion
Changxin Huang, Guangrun Wang, Zhibo Zhou +2
Controlling a non-statically bipedal robot is challenging due to the complex dynamics and multi-criterion optimization involved. Recent works have demonstrated the effectiveness of…
Solving Inefficiency of Self-supervised Representation Learning
Guangrun Wang, Keze Wang, Guangcong Wang +2
Self-supervised learning (especially contrastive learning) has attracted great interest due to its huge potential in learning discriminative representations in an unsupervised mann…
Joint Learning of Neural Transfer and Architecture Adaptation for Image Recognition
Guangrun Wang, Liang Lin, Rongcong Chen +2
Current state-of-the-art visual recognition systems usually rely on the following pipeline: (a) pretraining a neural network on a large-scale dataset (e.g., ImageNet) and (b) finet…
Dynamic Slimmable Network
Changlin Li, Guangrun Wang, Bing Wang +3
Current dynamic networks and dynamic pruning methods have shown their promising capability in reducing theoretical computation complexity. However, dynamic sparse patterns on convo…
BossNAS: Exploring Hybrid CNN-transformers with Block-wisely Self-supervised Neural Architecture Search
Changlin Li, Tao Tang, Guangrun Wang +4
A myriad of recent breakthroughs in hand-crafted neural architectures for visual recognition have highlighted the urgent need to explore hybrid architectures consisting of diversif…
EagleEye: Fast Sub-net Evaluation for Efficient Neural Network Pruning
Bailin Li, Bowen Wu, Jiang Su +2
Finding out the computational redundant part of a trained Deep Neural Network (DNN) is the key question that pruning algorithms target on. Many algorithms try to predict model perf…
Transferable, Controllable, and Inconspicuous Adversarial Attacks on Person Re-identification With Deep Mis-Ranking
Hongjun Wang, Guangrun Wang, Ya Li +2
The success of DNNs has driven the extensive applications of person re-identification (ReID) into a new era. However, whether ReID inherits the vulnerability of DNNs remains unexpl…