2 citations · 2 across the 5 of their papers we have counts for
12 papers
PhysVista: Benchmarking Physical Intelligence in VLMs via a Perception-Reasoning-Assessment Loop
Xinge Peng, Yiting Lu, Tianwu Zhi +4
Vision-Language Models (VLMs) have shown strong multimodal reasoning capabilities, yet whether they truly capture the physical consistency underlying real-world dynamics remains un…
VLA-JEPA: Enhancing Vision-Language-Action Model with Latent World Model
Jingwen Sun, Wenyao Zhang, Zekun Qi +6
Pretraining Vision-Language-Action (VLA) policies on internet-scale video is appealing, yet current latent-action objectives often learn the wrong thing: they remain anchored to pi…
WorldArena: A Unified Benchmark for Evaluating Perception and Functional Utility of Embodied World Models
Yu Shang, Zhuohang Li, Yiding Ma +18
While world models have emerged as a cornerstone of embodied intelligence by enabling agents to reason about environmental dynamics through action-conditioned prediction, their eva…
Diff-ICMH: Harmonizing Machine and Human Vision in Image Compression with Generative Prior
Ruoyu Feng, Yunpeng Qi, Jinming Liu +4
Image compression methods are usually optimized isolatedly for human perception or machine analysis tasks. We reveal fundamental commonalities between these objectives: preserving…
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…
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…
CCEdit: Creative and Controllable Video Editing via Diffusion Models
Ruoyu Feng, Wenming Weng, Yanhui Wang +5
In this paper, we present CCEdit, a versatile generative video editing framework based on diffusion models. Our approach employs a novel trident network structure that separates st…