198 citations · 285 across the 151 of their papers we have counts for
8 papers · 1 filter
Joint fMRI Decoding and Encoding with Latent Embedding Alignment
Xuelin Qian, Yikai Wang, Yanwei Fu +3
The connection between brain activity and corresponding visual stimuli is crucial in comprehending the human brain. While deep generative models have exhibited advancement in recov…
Preconditioned Score-based Generative Models
Hengyuan Ma, Xiatian Zhu, Jianfeng Feng +1
Score-based generative models (SGMs) have recently emerged as a promising class of generative models. However, a fundamental limitation is that their sampling process is slow due t…
Vision Transformers: From Semantic Segmentation to Dense Prediction
Li Zhang, Jiachen Lu, Sixiao Zheng +6
The emergence of vision transformers (ViTs) in image classification has shifted the methodologies for visual representation learning. In particular, ViTs learn visual representatio…
Softmax-free Linear Transformers
Jiachen Lu, Junge Zhang, Xiatian Zhu +3
Vision transformers (ViTs) have pushed the state-of-the-art for visual perception tasks. The self-attention mechanism underpinning the strength of ViTs has a quadratic complexity i…
Accelerating Score-based Generative Models with Preconditioned Diffusion Sampling
Hengyuan Ma, Li Zhang, Xiatian Zhu +1
Score-based generative models (SGMs) have recently emerged as a promising class of generative models. However, a fundamental limitation is that their inference is very slow due to…
Accelerating Score-based Generative Models for High-Resolution Image Synthesis
Hengyuan Ma, Li Zhang, Xiatian Zhu +2
Score-based generative models (SGMs) have recently emerged as a promising class of generative models. The key idea is to produce high-quality images by recurrently adding Gaussian…
MIST: Multiple Instance Self-Training Framework for Video Anomaly Detection
Jia-Chang Feng, Fa-Ting Hong, Wei-Shi Zheng
Weakly supervised video anomaly detection (WS-VAD) is to distinguish anomalies from normal events based on discriminative representations. Most existing works are limited in insuff…
Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers
Sixiao Zheng, Jiachen Lu, Hengshuang Zhao +8
Most recent semantic segmentation methods adopt a fully-convolutional network (FCN) with an encoder-decoder architecture. The encoder progressively reduces the spatial resolution a…