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Official implementation of the paper "Segment Everything Everywhere All at Once" - GitHub - UX-Decoder/Segment-Everything-Everywhere-All-At-Once: Official implementation of the paper &quo...

Modern approaches typically formulate semantic segmentation as a per-pixelclassification task, while instance-level segmentation is handled with analternative mask classification. Our key insight: mask classification issufficiently general to solve both semantic- and instance-level segmentationt…

Image segmentation is about grouping pixels with different semantics, e.g.,category or instance membership, where each choice of semantics defines a task.While only the semantics of each task differ, current research focuses ondesigning specialized architectures for each task. We present Masked-a…

We present X-Decoder, a generalized decoding model that can predictpixel-level segmentation and language tokens seamlessly. X-Decodert takes asinput two types of queries: (i) generic non-semantic queries and (ii) semanticqueries induced from text inputs, to decode different pixel-level andtoken-…

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Detecting out-of-distribution (OOD) inputs during the inference stage iscrucial for deploying neural networks in the real world. Previous methodscommonly relied on the output of a network derived from the highly activatedfeature map. In this study, we first revealed that a norm of the feature map…

ImageBind is the first AI model capable of binding information from six modalities.