1 citations · 2 across the 10 of their papers we have counts for
11 papers
VLN-Cache: Enabling Token Caching for VLN Models with Visual/Semantic Dynamics Awareness
Zihao Zheng, Zhihao Mao, Xingyue Zhou +9
Vision-and-Language Navigation (VLN) increasingly relies on large vision-language models, but their inference cost conflicts with real-time deployment. Token caching is a promising…
FreqCache: Accelerating Embodied VLN Models with Adaptive Frequency-Guided Token Caching
Zihao Zheng, Xingyue Zhou, Zhihao Mao +7
Vision-Language-Navigation (VLN) models exhibit excellent navigation accuracy but incur high computational overhead. Token caching has emerged as a promising training-free strategy…
ATD: Improved Transformer with Adaptive Token Dictionary for Image Restoration
Leheng Zhang, Wei Long, Yawei Li +3
Recently, Transformers have gained significant popularity in image restoration tasks such as image super-resolution and denoising, owing to their superior performance. However, bal…
Learned Image Compression with Dictionary-based Entropy Model
Jingbo Lu, Leheng Zhang, Xingyu Zhou +3
Learned image compression methods have attracted great research interest and exhibited superior rate-distortion performance to the best classical image compression standards of the…
Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution
Xingyu Zhou, Wei Long, Jingbo Lu +4
Video super-resolution (VSR) can achieve better performance compared to single image super-resolution by additionally leveraging temporal information. In particular, the recurrent-…
Neural Stereo Video Compression with Hybrid Disparity Compensation
Shiyin Jiang, Zhenghao Chen, Minghao Han +1
Disparity compensation represents the primary strategy in stereo video compression (SVC) for exploiting cross-view redundancy. These mechanisms can be broadly categorized into two…
Progressive Focused Transformer for Single Image Super-Resolution
Wei Long, Xingyu Zhou, Leheng Zhang +1
Transformer-based methods have achieved remarkable results in image super-resolution tasks because they can capture non-local dependencies in low-quality input images. However, thi…
Consistency Trajectory Matching for One-Step Generative Super-Resolution
Weiyi You, Mingyang Zhang, Leheng Zhang +3
Current diffusion-based super-resolution (SR) approaches achieve commendable performance at the cost of high inference overhead. Therefore, distillation techniques are utilized to…
Transcending the Limit of Local Window: Advanced Super-Resolution Transformer with Adaptive Token Dictionary
Leheng Zhang, Yawei Li, Xingyu Zhou +2
Single Image Super-Resolution is a classic computer vision problem that involves estimating high-resolution (HR) images from low-resolution (LR) ones. Although deep neural networks…
Improved Implicit Neural Representation with Fourier Reparameterized Training
Kexuan Shi, Xingyu Zhou, Shuhang Gu
Implicit Neural Representation (INR) as a mighty representation paradigm has achieved success in various computer vision tasks recently. Due to the low-frequency bias issue of vani…
Video Super-Resolution Transformer with Masked Inter&Intra-Frame Attention
Xingyu Zhou, Leheng Zhang, Xiaorui Zhao +3
Recently, Vision Transformer has achieved great success in recovering missing details in low-resolution sequences, i.e., the video super-resolution (VSR) task. Despite its superior…