2 citations · 2 across the 5 of their papers we have counts for
5 papers · 1 filter
Semantics Disentanglement and Composition for Universal Image Coding with Efficiently LLM Reasoning and Generative Diffusion
Jinming Liu, Yuntao Wei, Junyan Lin +5
Learned image compression methods have shown impressive performance but are often highly specialized for either human perception or specific machine vision tasks. This specializati…
Tell Codec What Worth Compressing: Semantically Disentangled Image Coding for Machine with LMMs
Jinming Liu, Yuntao Wei, Junyan Lin +5
We present a new image compression paradigm to achieve ``intelligently coding for machine'' by cleverly leveraging the common sense of Large Multimodal Models (LMMs). We are motiva…
Rethinking Domain Adaptation and Generalization in the Era of CLIP
Ruoyu Feng, Tao Yu, Xin Jin +3
In recent studies on domain adaptation, significant emphasis has been placed on the advancement of learning shared knowledge from a source domain to a target domain. Recently, the…
Rate-Distortion-Cognition Controllable Versatile Neural Image Compression
Jinming Liu, Ruoyu Feng, Yunpeng Qi +4
Recently, the field of Image Coding for Machines (ICM) has garnered heightened interest and significant advances thanks to the rapid progress of learning-based techniques for image…
SeD: Semantic-Aware Discriminator for Image Super-Resolution
Bingchen Li, Xin Li, Hanxin Zhu +4
Generative Adversarial Networks (GANs) have been widely used to recover vivid textures in image super-resolution (SR) tasks. In particular, one discriminator is utilized to enable…