553 citations · 882 across the 39 of their papers we have counts for
20 papers · 1 filter
Technical Report on the CVPR 2026@AdvML Workshop Challenge
Tianyuan Zhang, Zonglei Jing, Jiangfan Liu +47
Vision-language agents (VLAs) are increasingly used to interpret complex driving scenes and support safety-critical reasoning. This report presents the CVPR 2026@AdvML Workshop Cha…
Multi-Resolution Flow Matching: Training-Free Diffusion Acceleration via Staged Sampling
Xingyu Zheng, Xianglong Liu, Yifu Ding +4
Hardware-agnostic strategies for accelerating text-to-image diffusion, such as timestep distillation and feature caching, can reduce inference time without custom kernels or system…
SQ-VDiT: Accurate Quantized Video Diffusion Transformer with Salient Data and Sparse Token Distillation
Weilun Feng, Haotong Qin, Chuanguang Yang +7
Diffusion transformers have emerged as the mainstream paradigm for video generation models. However, the use of up to billions of parameters incurs significant computational costs.…
MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation
Weilun Feng, Chuanguang Yang, Haotong Qin +10
Diffusion models have demonstrated remarkable performance on vision generation tasks. However, the high computational complexity hinders its wide application on edge devices. Quant…
QVGen: Pushing the Limit of Quantized Video Generative Models
Yushi Huang, Ruihao Gong, Jing Liu +4
Video diffusion models (DMs) have enabled high-quality video synthesis. Yet, their substantial computational and memory demands pose serious challenges to real-world deployment, ev…
BiDM: Pushing the Limit of Quantization for Diffusion Models
Xingyu Zheng, Xianglong Liu, Yichen Bian +5
Diffusion models (DMs) have been significantly developed and widely used in various applications due to their excellent generative qualities. However, the expensive computation and…
DynamicPAE: Generating Scene-Aware Physical Adversarial Examples in Real-Time
Jin Hu, Xianglong Liu, Jiakai Wang +5
Physical adversarial examples (PAEs) are regarded as whistle-blowers of real-world risks in deep-learning applications, thus worth further investigation. However, current PAE gener…
BinaryDM: Accurate Weight Binarization for Efficient Diffusion Models
Xingyu Zheng, Xianglong Liu, Haotong Qin +7
With the advancement of diffusion models (DMs) and the substantially increased computational requirements, quantization emerges as a practical solution to obtain compact and effici…
How Good is Google Bard's Visual Understanding? An Empirical Study on Open Challenges
Haotong Qin, Ge-Peng Ji, Salman Khan +3
Google's Bard has emerged as a formidable competitor to OpenAI's ChatGPT in the field of conversational AI. Notably, Bard has recently been updated to handle visual inputs alongsid…
Towards Accurate Post-Training Quantization for Vision Transformer
Yifu Ding, Haotong Qin, Qinghua Yan +4
Vision transformer emerges as a potential architecture for vision tasks. However, the intense computation and non-negligible delay hinder its application in the real world. As a wi…
BiBench: Benchmarking and Analyzing Network Binarization
Haotong Qin, Mingyuan Zhang, Yifu Ding +5
Network binarization emerges as one of the most promising compression approaches offering extraordinary computation and memory savings by minimizing the bit-width. However, recent…
Defensive Patches for Robust Recognition in the Physical World
Jiakai Wang, Zixin Yin, Pengfei Hu +5
To operate in real-world high-stakes environments, deep learning systems have to endure noises that have been continuously thwarting their robustness. Data-end defense, which impro…
Distribution-sensitive Information Retention for Accurate Binary Neural Network
Haotong Qin, Xiangguo Zhang, Ruihao Gong +3
Model binarization is an effective method of compressing neural networks and accelerating their inference process. However, a significant performance gap still exists between the 1…
Diverse Sample Generation: Pushing the Limit of Generative Data-free Quantization
Haotong Qin, Yifu Ding, Xiangguo Zhang +3
Generative data-free quantization emerges as a practical compression approach that quantizes deep neural networks to low bit-width without accessing the real data. This approach ge…
Towards Real-world X-ray Security Inspection: A High-Quality Benchmark and Lateral Inhibition Module for Prohibited Items Detection
Renshuai Tao, Yanlu Wei, Xiangjian Jiang +6
Prohibited items detection in X-ray images often plays an important role in protecting public safety, which often deals with color-monotonous and luster-insufficient objects, resul…
Multi-Pretext Attention Network for Few-shot Learning with Self-supervision
Hainan Li, Renshuai Tao, Jun Li +4
Few-shot learning is an interesting and challenging study, which enables machines to learn from few samples like humans. Existing studies rarely exploit auxiliary information from…
Diversifying Sample Generation for Accurate Data-Free Quantization
Xiangguo Zhang, Haotong Qin, Yifu Ding +6
Quantization has emerged as one of the most prevalent approaches to compress and accelerate neural networks. Recently, data-free quantization has been widely studied as a practical…
Over-sampling De-occlusion Attention Network for Prohibited Items Detection in Noisy X-ray Images
Renshuai Tao, Yanlu Wei, Hainan Li +4
Security inspection is X-ray scanning for personal belongings in suitcases, which is significantly important for the public security but highly time-consuming for human inspectors.…
BiPointNet: Binary Neural Network for Point Clouds
Haotong Qin, Zhongang Cai, Mingyuan Zhang +5
To alleviate the resource constraint for real-time point cloud applications that run on edge devices, in this paper we present BiPointNet, the first model binarization approach for…
Forward and Backward Information Retention for Accurate Binary Neural Networks
Haotong Qin, Ruihao Gong, Xianglong Liu +4
Weight and activation binarization is an effective approach to deep neural network compression and can accelerate the inference by leveraging bitwise operations. Although many bina…