224 citations · 591 across the 26 of their papers we have counts for
26 papers
Mage-VL: An Efficient Codec-Native Streaming Multimodal Foundation Model
Senqiao Yang, Kaichen Zhang, Zhaoyang Jia +20
Standard vision-language models (VLMs) suffer from Moravec's paradox: they excel at complex offline visual reasoning but struggle with simple streaming perception tasks and process…
Generative Video Compression with Adaptive Score Distillation
Naifu Xue, Zhaoyang Jia, Haosen Li +7
Diffusion models provide strong generative capabilities for video compression at ultra-low bitrates. Existing diffusion-based video codecs adapt base models originally developed fo…
Mage-Flow: An Efficient Native-Resolution Foundation Model for Image Generation and Editing
Xinjie Zhang, Peng Zhang, Shicheng Zheng +21
Large-scale visual generators are increasingly capable but costly to train, fine-tune, and deploy. We introduce Mage-Flow, a compact 4B-scale generative stack for efficient text-to…
Ultra-Fast Neural Video Compression
Jiahao Li, Wenxuan Xie, Zhaoyang Jia +4
While neural video codecs (NVCs) have demonstrated superior compression ratio, their prohibitive computational complexity remains a critical barrier to real-world deployment. This…
Generative Video Compression with One-Dimensional Latent Representation
Zihan Zheng, Zhaoyang Jia, Naifu Xue +7
Recent advancements in generative video codec (GVC) typically encode video into a 2D latent grid and employ high-capacity generative decoders for reconstruction. However, this para…
Efficient Autoregressive Video Diffusion with Dummy Head
Hang Guo, Zhaoyang Jia, Jiahao Li +5
The autoregressive video diffusion model has recently gained considerable research interest due to its causal modeling and iterative denoising. In this work, we identify that the m…
Generative Latent Coding for Ultra-Low Bitrate Image Compression
Zhaoyang Jia, Jiahao Li, Bin Li +2
Most existing image compression approaches perform transform coding in the pixel space to reduce its spatial redundancy. However, they encounter difficulties in achieving both high…
Single-step Diffusion-based Video Coding with Semantic-Temporal Guidance
Naifu Xue, Zhaoyang Jia, Jiahao Li +4
While traditional and neural video codecs (NVCs) have achieved remarkable rate-distortion performance, improving perceptual quality at low bitrates remains challenging. Some NVCs i…
Divide, then Ground: Adapting Frame Selection to Query Types for Long-Form Video Understanding
Jialuo Li, Bin Li, Jiahao Li +1
The application of Large Multimodal Models (LMMs) to long-form video understanding is constrained by limited context lengths and the computationally prohibitive cost of processing…
CoD: A Diffusion Foundation Model for Image Compression
Zhaoyang Jia, Zihan Zheng, Naifu Xue +6
Existing diffusion codecs typically build on text-to-image diffusion foundation models like Stable Diffusion. However, text conditioning is suboptimal from a compression perspectiv…
Generative Latent Video Compression
Zongyu Guo, Zhaoyang Jia, Jiahao Li +3
Perceptual optimization is widely recognized as essential for neural compression, yet balancing the rate-distortion-perception tradeoff remains challenging. This difficulty is espe…
When MLLMs Meet Compression Distortion: A Coding Paradigm Tailored to MLLMs
Jinming Liu, Zhaoyang Jia, Jiahao Li +4
The increasing deployment of powerful Multimodal Large Language Models (MLLMs), typically hosted on cloud platforms, urgently requires effective compression techniques to efficient…
UniMIC: Token-Based Multimodal Interactive Coding for Human-AI Collaboration
Qi Mao, Tinghan Yang, Jiahao Li +3
The rapid progress of Large Multimodal Models (LMMs) and cloud-based AI agents is transforming human-AI collaboration into bidirectional, multimodal interaction. However, existing…
PICD: Versatile Perceptual Image Compression with Diffusion Rendering
Tongda Xu, Jiahao Li, Bin Li +3
Recently, perceptual image compression has achieved significant advancements, delivering high visual quality at low bitrates for natural images. However, for screen content, existi…
Deep Video Discovery: Agentic Search with Tool Use for Long-form Video Understanding
Xiaoyi Zhang, Zhaoyang Jia, Zongyu Guo +4
Long-form video understanding presents significant challenges due to extensive temporal-spatial complexity and the difficulty of question answering under such extended contexts. Wh…
Towards Practical Real-Time Neural Video Compression
Zhaoyang Jia, Bin Li, Jiahao Li +4
We introduce a practical real-time neural video codec (NVC) designed to deliver high compression ratio, low latency and broad versatility. In practice, the coding speed of NVCs dep…
DLF: Extreme Image Compression with Dual-generative Latent Fusion
Naifu Xue, Zhaoyang Jia, Jiahao Li +3
Recent studies in extreme image compression have achieved remarkable performance by compressing the tokens from generative tokenizers. However, these methods often prioritize clust…
Neural Video Compression with Feature Modulation
Jiahao Li, Bin Li, Yan Lu
The emerging conditional coding-based neural video codec (NVC) shows superiority over commonly-used residual coding-based codec and the latest NVC already claims to outperform the…
Neural Video Compression with Diverse Contexts
Jiahao Li, Bin Li, Yan Lu
For any video codecs, the coding efficiency highly relies on whether the current signal to be encoded can find the relevant contexts from the previous reconstructed signals. Tradit…
EVC: Towards Real-Time Neural Image Compression with Mask Decay
Guo-Hua Wang, Jiahao Li, Bin Li +1
Neural image compression has surpassed state-of-the-art traditional codecs (H.266/VVC) for rate-distortion (RD) performance, but suffers from large complexity and separate models f…
Hybrid Spatial-Temporal Entropy Modelling for Neural Video Compression
Jiahao Li, Bin Li, Yan Lu
For neural video codec, it is critical, yet challenging, to design an efficient entropy model which can accurately predict the probability distribution of the quantized latent repr…
Neural Compression-Based Feature Learning for Video Restoration
Cong Huang, Jiahao Li, Bin Li +2
How to efficiently utilize the temporal features is crucial, yet challenging, for video restoration. The temporal features usually contain various noisy and uncorrelated informatio…
Temporal Context Mining for Learned Video Compression
Xihua Sheng, Jiahao Li, Bin Li +3
We address end-to-end learned video compression with a special focus on better learning and utilizing temporal contexts. For temporal context mining, we propose to store not only t…
Deep Contextual Video Compression
Jiahao Li, Bin Li, Yan Lu
Most of the existing neural video compression methods adopt the predictive coding framework, which first generates the predicted frame and then encodes its residue with the current…