2 citations · 6 across the 13 of their papers we have counts for
16 papers
ScaleLUT: A Fully-Parallel Configurable LUT-Based Accelerator for Real-Time Multi-Scale Super-Resolution
Boyu Li, Chenchen Ding, Zhilin Ai +7
Real-time super-resolution (SR) remains challenging for edge devices because deep-learning-based methods require substantial multiply-accumulate (MAC) operations, resources, and po…
Hybrid-LUT: Channel-Aware Hybrid Lookup Table and Filtering for Efficient Image Denoising
Zhilin Ai, Boyu Li, Sidi Yang +5
Lookup table (LUT)-based image denoising methods have attracted increasing attention due to their high efficiency and hardware-friendly properties. However, existing RGB-LUT approa…
Boogu-Image-0.1: Boosting Open Agentic Multimodal Generation via Understanding under a Minimal Budget
Guoxuan Chen, Chufeng Xiao, Haoran Yang +30
We introduce Boogu-Image-0.1, an open-source unified multimodal understanding and generation model family, comprising Base, Turbo, Edit, and Edit-Turbo variants. It delivers compet…
Re-Activating Frozen Primitives for 3D Gaussian Splatting
Yuxin Cheng, Binxiao Huang, Wenyong Zhou +4
3D Gaussian Splatting (3D-GS) achieves real-time photorealistic novel view synthesis, yet struggles with complex scenes due to over-reconstruction artifacts, manifesting as local b…
Perspective-aware 3D Gaussian Inpainting with Multi-view Consistency
Yuxin Cheng, Binxiao Huang, Taiqiang Wu +5
3D Gaussian inpainting, a critical technique for numerous applications in virtual reality and multimedia, has made significant progress with pretrained diffusion models. However, e…
Decomposing Densification in Gaussian Splatting for Faster 3D Scene Reconstruction
Binxiao Huang, Zhengwu Liu, Ngai Wong
3D Gaussian Splatting (GS) has emerged as a powerful representation for high-quality scene reconstruction, offering compelling rendering quality. However, the training process of G…
DnLUT: Ultra-Efficient Color Image Denoising via Channel-Aware Lookup Tables
Sidi Yang, Binxiao Huang, Yulun Zhang +3
While deep neural networks have revolutionized image denoising capabilities, their deployment on edge devices remains challenging due to substantial computational and memory requir…
Poisoning-based Backdoor Attacks for Arbitrary Target Label with Positive Triggers
Binxiao Huang, Jason Chun Lok, Chang Liu +1
Poisoning-based backdoor attacks expose vulnerabilities in the data preparation stage of deep neural network (DNN) training. The DNNs trained on the poisoned dataset will be embedd…
Taming Lookup Tables for Efficient Image Retouching
Sidi Yang, Binxiao Huang, Mingdeng Cao +4
The widespread use of high-definition screens in edge devices, such as end-user cameras, smartphones, and televisions, is spurring a significant demand for image enhancement. Exist…
Learning Spatially Collaged Fourier Bases for Implicit Neural Representation
Jason Chun Lok Li, Chang Liu, Binxiao Huang +1
Existing approaches to Implicit Neural Representation (INR) can be interpreted as a global scene representation via a linear combination of Fourier bases of different frequencies.…
Hundred-Kilobyte Lookup Tables for Efficient Single-Image Super-Resolution
Binxiao Huang, Jason Chun Lok Li, Jie Ran +4
Conventional super-resolution (SR) schemes make heavy use of convolutional neural networks (CNNs), which involve intensive multiply-accumulate (MAC) operations, and require special…
Lite it fly: An All-Deformable-Butterfly Network
Rui Lin, Jason Chun Lok Li, Jiajun Zhou +3
Most deep neural networks (DNNs) consist fundamentally of convolutional and/or fully connected layers, wherein the linear transform can be cast as the product between a filter matr…
A Spectral Perspective towards Understanding and Improving Adversarial Robustness
Binxiao Huang, Rui Lin, Chaofan Tao +1
Deep neural networks (DNNs) are incredibly vulnerable to crafted, imperceptible adversarial perturbations. While adversarial training (AT) has proven to be an effective defense app…
Frequency Regularization for Improving Adversarial Robustness
Binxiao Huang, Chaofan Tao, Rui Lin +1
Deep neural networks are incredibly vulnerable to crafted, human-imperceptible adversarial perturbations. Although adversarial training (AT) has proven to be an effective defense a…
Multimodal Transformer for Automatic 3D Annotation and Object Detection
Chang Liu, Xiaoyan Qian, Binxiao Huang +4
Despite a growing number of datasets being collected for training 3D object detection models, significant human effort is still required to annotate 3D boxes on LiDAR scans. To aut…
What Do Adversarially trained Neural Networks Focus: A Fourier Domain-based Study
Binxiao Huang, Chaofan Tao, Rui Lin +1
Although many fields have witnessed the superior performance brought about by deep learning, the robustness of neural networks remains an open issue. Specifically, a small adversar…