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Advanced Pixel Diffusion Model with Guided Sparse Global Refinement
Weiyi You, Jinhua Zhang, Xingyu Zhou +3
Pixel-space diffusion has recently emerged as a promising direction for high-fidelity image generation by modeling images directly in the original pixel domain. However, pixel-spac…
Beyond Token-Level Cross-Entropy: Fréchet Distributional Post-Training for Autoregressive Image Generation
Jinhua Zhang, Yisong Lin, Wei Long +1
Autoregressive image generators are commonly pretrained with token-level cross-entropy under teacher forcing, yet evaluated by the distributional quality of decoded images. This cr…
Differentiable Vector Quantization for Rate-Distortion Optimization of Generative Image Compression
Shiyin Jiang, Wei Long, Minghao Han +3
The rapid growth of visual data under stringent storage and bandwidth constraints makes extremely low-bitrate image compression increasingly important. While Vector Quantization (V…
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…
From Local Windows to Adaptive Candidates via Individualized Exploratory: Rethinking Attention for Image Super-Resolution
Chunyu Meng, Wei Long, Shuhang Gu
Single Image Super-Resolution (SISR) is a fundamental computer vision task that aims to reconstruct a high-resolution (HR) image from a low-resolution (LR) input. Transformer-based…
IDESplat: Iterative Depth Probability Estimation for Generalizable 3D Gaussian Splatting
Wei Long, Haifeng Wu, Shiyin Jiang +3
Generalizable 3D Gaussian Splatting aims to directly predict Gaussian parameters using a feed-forward network for scene reconstruction. Among these parameters, Gaussian means are p…
Texture Vector-Quantization and Reconstruction Aware Prediction for Generative Super-Resolution
Qifan Li, Jiale Zou, Jinhua Zhang +3
Vector-quantized based models have recently demonstrated strong potential for visual prior modeling. However, existing VQ-based methods simply encode visual features with nearest c…
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-…
MVAR: Visual Autoregressive Modeling with Scale and Spatial Markovian Conditioning
Jinhua Zhang, Wei Long, Minghao Han +2
Essential to visual generation is efficient modeling of visual data priors. Conventional next-token prediction methods define the process as learning the conditional probability di…
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…