{"id":802117,"date":"2021-12-06T14:36:56","date_gmt":"2021-12-06T22:36:56","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&#038;p=802117"},"modified":"2022-07-25T14:01:58","modified_gmt":"2022-07-25T21:01:58","slug":"crossing-the-format-boundary-of-text-and-boxes-towards-unified-vision-language-modeling","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/crossing-the-format-boundary-of-text-and-boxes-towards-unified-vision-language-modeling\/","title":{"rendered":"UniTAB: Unifying Text and Box Outputs for Grounded Vision-Language Modeling"},"content":{"rendered":"<p>We propose UniTAB that Unifies Text And Box outputs for grounded vision-language (VL) modeling. Grounded VL tasks such as<br \/>\ngrounded captioning require the model to generate a text description and align predicted words with object regions. To achieve this, models must<br \/>\ngenerate desired text and box outputs together, and meanwhile indicate the alignments between words and boxes. In contrast to existing solutions<br \/>\nthat use multiple separate modules for different outputs, UniTAB represents both text and box outputs with a shared token sequence, and<br \/>\nintroduces a special <obj> token to naturally indicate word-box alignments in the sequence. UniTAB thus could provide a more comprehensive<br \/>\nand interpretable image description, by freely grounding generated words to object regions. On grounded captioning, UniTAB presents a simpler<br \/>\nsolution with a single output head, and significantly outperforms state of the art in both grounding and captioning evaluations. On general VL<br \/>\ntasks that have different desired output formats (i.e., text, box, or their combination), UniTAB with a single network achieves better or comparable<br \/>\nperformance than task-specific state of the art. Experiments cover 7 VL benchmarks, including grounded captioning, visual grounding, image<br \/>\ncaptioning, and visual question answering. Furthermore, UniTAB\u2019s unified multi-task network and the task-agnostic output sequence design make the model parameter efficient and generalizable to new tasks.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>We propose UniTAB that Unifies Text And Box outputs for grounded vision-language (VL) modeling. Grounded VL tasks such as grounded captioning require the model to generate a text description and align predicted words with object regions. To achieve this, models must generate desired text and box outputs together, and meanwhile indicate the alignments between words 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