1 code implementation • 27 Mar 2024 • Zezhi Wang, Jin Zhu, Peng Chen, Huiyang Peng, Xiaoke Zhang, Anran Wang, Yu Zheng, Junxian Zhu, Xueqin Wang
Applying iterative solvers on sparsity-constrained optimization (SCO) requires tedious mathematical deduction and careful programming/debugging that hinders these solvers' broad impact.
no code implementations • 21 Feb 2024 • Shanchuan Lin, Anran Wang, Xiao Yang
We propose a diffusion distillation method that achieves new state-of-the-art in one-step/few-step 1024px text-to-image generation based on SDXL.
1 code implementation • 2 Oct 2022 • Hao Wang, Guosheng Lin, Ana García del Molino, Anran Wang, Jiashi Feng, Zhiqi Shen
In this paper we present a novel multi-attribute face manipulation method based on textual descriptions.
no code implementations • 3 Sep 2022 • Tianjiao Li, Lin Geng Foo, Qiuhong Ke, Hossein Rahmani, Anran Wang, Jinghua Wang, Jun Liu
We design a novel Dynamic Spatio-Temporal Specialization (DSTS) module, which consists of specialized neurons that are only activated for a subset of samples that are highly similar.
1 code implementation • AAAI 2022 • Anran Wang, Maruchi Kim, Hao Zhang, Shyamnath Gollakota
On-device directional hearing requires audio source separation from a given direction while achieving stringent human-imperceptible latency requirements.
Ranked #1 on Real-time Directional Hearing on VCTK
6 code implementations • NeurIPS 2021 • Zihang Jiang, Qibin Hou, Li Yuan, Daquan Zhou, Yujun Shi, Xiaojie Jin, Anran Wang, Jiashi Feng
In this paper, we present token labeling -- a new training objective for training high-performance vision transformers (ViTs).
Ranked #3 on Efficient ViTs on ImageNet-1K (With LV-ViT-S)
no code implementations • 12 Nov 2018 • Anran Wang, Anh Tuan Luu, Chuan-Sheng Foo, Hongyuan Zhu, Yi Tay, Vijay Chandrasekhar
In this paper, we present the Holistic Multi-modal Memory Network (HMMN) framework which fully considers the interactions between different input sources (multi-modal context, question) in each hop.
no code implementations • CVPR 2016 • Anran Wang, Jianfei Cai, Jiwen Lu, Tat-Jen Cham
While convolutional neural networks (CNN) have been excellent for object recognition, the greater spatial variability in scene images typically meant that the standard full-image CNN features are suboptimal for scene classification.
no code implementations • ICCV 2015 • Anran Wang, Jianfei Cai, Jiwen Lu, Tat-Jen Cham
We first construct deep CNN layers for color and depth separately, and then connect them with our carefully designed multi-modal layers, which fuse color and depth information by enforcing a common part to be shared by features of different modalities.