Recently, human-computer interaction with various modalities has shown promising applications, like GPT-4o and Gemini. Given the foundational role of multimodal joint representation in understanding and generation pipelines, high-quality omni joint representations would be a step toward co-processing more diverse multimodal information. In this work, we present OmniBind, large-scale multimodal joint representation models ranging in scale from 7 billion to 30 billion parameters, which support 3D, audio, image, and language inputs. Due to the scarcity of data pairs across all modalities, instead of training large models from scratch, we propose remapping and binding the spaces of various pre-trained specialist models together. This approach enables "scaling up" by indirectly increasing the model parameters and the amount of seen data. To effectively integrate various spaces, we dynamically assign weights to different spaces by learning routers with two objectives: cross-modal overall alignment and language representation decoupling. Notably, since binding and routing spaces both only require lightweight networks, OmniBind is extremely training-efficient. Learning the largest 30B model requires merely unpaired unimodal data and approximately 3 days on a single 8-4090 node. Extensive experiments demonstrate the versatility and superiority of OmniBind as an omni representation model, highlighting its great potential for diverse applications, such as any-query and composable multimodal understanding.
A gradient alloy steel prepared by laser melting deposition (LMD) was subjected to two heat treatments (quenching-intercritical quenching denoted as Q-IQ and quenching-intercritical quenching-tempering denoted as Q-IQ-T). Results showed that martensite microstructure was changed to a duplex microstructure of martensite and ferrite after heat treatment, with a small number of Cr 23 C 6 phases appearing. The samples before and after heat treatment exhibited random crystallographic orientation, while the grain was refined and the fraction of Cr 23 C 6 increased after heat treatment. The samples after heat treatment possessed more slip systems and exhibited a decrease in hardness and wear resistance in comparison with LMDed sample. The corrosion resistance decreased in the order of Q-IQ > Q-IQ-T > LMDed samples in 3.5 wt-% NaCl solution.
Summary Myofibrillar proteins (MP) and two forms of nanocellulose (cellulose nanofibers [CNFs] and cellulose nanocrystals [CNCs]) were used to prepare oil‐in‐water emulsion. The effect of CNFs and CNCs on the properties of pork MP‐lard emulsion was studied by analysing the emulsion index, microstructure, oil droplet size, zeta potential and rheological behaviour of emulsion. The results showed that both CNFs and CNCs improved MP‐lard emulsion stability. At the same nanofiber concentration, the creaming index of CNFs stabilised emulsion was lower than that of CNCs stabilised emulsion, especially at the concentration of 0.5%, emulsion prepared with CNFs has no phenomenon of creaming index, while emulsion prepared with CNCs still has creaming index phenomenon. At the same nanofiber concentration, the oil droplet distribution of CNFs stabilised emulsion was more uniform, especially at low concentrations (≤0.5%). At higher cellulose concentration (≥0.75%), the particle size of CNFs stabilised emulsion was larger than that of CNCs stabilised emulsion. CNFs stabilised emulsion had a higher zeta potential and modulus than that of CNCs stabilised emulsion and the emulsion formed by CNFs was more viscoelastic.
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