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Tree-VQ is presented, a post-hoc conversion of a pretrained flat VQ tokenizer into a fine-grained, arbitrary-prefix progressive representation while preserving its encoder assignments and every learned leaf vector.
A Lightweight Multiscale Contextual Attention Network (LMCAN) is proposed that reconstructs spatial context without altering the pixel-level representation, enabling information at different scales to interact adaptively rather than being modeled in isolation.
A monocular 3D localization framework using a prebuilt, georegistered triangular mesh using a two-dimensional grid indexes the mesh, and a progressive multi-scale strategy loads high-resolution blocks for ray–mesh intersection.
Tree-VQ is proposed, a progressive tree-structured vector quantization framework for learned image compression that codes progressive continuation decisions and routed branch refinements using only causally available decoded contexts and achieves a superior performance--efficiency trade-off.
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