6 papers
EVOKE: Eliciting World Knowledge in Agents for Transferable Decision-Making
Yuhan Guo, Jinming Liu, Liang Xu +8
Large language models (LLMs) are increasingly deployed as agents for multi-step decision-making, yet transfer poorly to unseen environments. World-model methods address this by tra…
Compression Tells Intelligence: Visual Coding, Visual Token Technology, and the Unification
Xin Jin, Jinming Liu, Yuntao Wei +6
"Compression Tells Intelligence", is supported by research in artificial intelligence, particularly concerning (multimodal) large language models (LLMs/MLLMs), where compression ef…
Revisiting MLLM Token Technology through the Lens of Classical Visual Coding
Jinming Liu, Junyan Lin, Yuntao Wei +7
Classical visual coding and Multimodal Large Language Model (MLLM) token technology share the core objective - maximizing information fidelity while minimizing computational cost.…
The 1st International Workshop on Disentangled Representation Learning for Controllable Generation (DRL4Real): Methods and Results
Qiuyu Chen, Xin Jin, Yue Song +45
This paper reviews the 1st International Workshop on Disentangled Representation Learning for Controllable Generation (DRL4Real), held in conjunction with ICCV 2025. The workshop a…
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…