Images capture the world at one moment, whereas video reveals how it changes. Image self-supervision learns spatial structure from a single moment, while video methods commonly learn temporal relationships inside a representation computed jointly from several frames. We ask whether watching a scene change can instead i...
Wen Huang, Hang Guo, Jia-Rui Yang et al.· 0 citations
This work designs Progressive State Sequence Generation (PSSG) to construct complementary spatial-frequency target states (defining what to recover), and State Transition Evolution (STE) to adapt lightweight transition operators via a restoration-oriented TTT inner loop (determining how the features should evolve).
Kai-Han Zheng, Jun Li, Hang Guo et al.· 0 citations
Low-light image enhancement (LLIE) seeks to restore structural fidelity, natural color rendition, and proper exposure from images captured under inadequate lighting conditions. Recent state-of-the-art approaches, such as CIDNet, adopt a dual-branch architecture comprising a chrominance (HV) branch and an intensity (I)...
Yanxin Guan, Shaonan Zhang, Tao Dai et al.· 0 citations
LAWM-3D is proposed, which introduces three tightly coupled key designs: a multi-view invariant unified action tokenization scheme for learning 3D-aware latent actions, a geometric alignment constraint that anchors intermediate encoder features to a pretrained 3D foundation model, and a non-injective RGB-D joint recons...
Jia-Rui Yang, Jiale Zhange, Jiawei Li et al.· 1 citation
This paper argues that what a VLA needs is not the ability to generate language, but the ability to consume grounded language, and introduces a framework that endows a VLA with language competence through in-context post-training and an agentic tool-use interface.
Jia-Rui Yang, Wen Huang, Jia-Le Zhang et al.· 1 citation
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