INST-IT: Boosting Instance Understanding via Explicit Visual Prompt Instruction Tuning
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arXiv
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| Autori principali: | , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866912966480232448 |
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| author | Peng, Wujian Meng, Lingchen Chen, Yitong Xie, Yiweng Liu, Yang Gui, Tao Xu, Hang Qiu, Xipeng Wu, Zuxuan Jiang, Yu-Gang |
| author_facet | Peng, Wujian Meng, Lingchen Chen, Yitong Xie, Yiweng Liu, Yang Gui, Tao Xu, Hang Qiu, Xipeng Wu, Zuxuan Jiang, Yu-Gang |
| contents | Large Multimodal Models (LMMs) have made significant breakthroughs with the advancement of instruction tuning. However, while existing models can understand images and videos at a holistic level, they still struggle with instance-level understanding that requires a more fine-grained comprehension and alignment. Instance-level understanding is crucial for LMMs, as it focuses on the specific elements that we are most interested in. Excitingly, existing works find that the SOTA LMMs exhibit strong instance understanding capabilities when provided with explicit visual cues. Motivated by this, we proposed Inst-IT, a solution to enhance LMMs in Instance understanding via explicit visual prompt Instruction Tuning for instance guidance. Inst-IT consists of a benchmark to diagnose multimodal instance-level understanding, a large-scale instruction-tuning dataset, and a continuous instruction-tuning training paradigm to effectively enhance spatial-temporal instance understanding capabilities of existing LMMs. Experimental results show that, enhanced by Inst-IT, our models not only achieve outstanding performance on Inst-IT Bench and other instance understanding benchmarks, but also demonstrate significant improvements across various generic image and video understanding benchmarks. This highlights that our method not only boosts instance-level understanding but also strengthens the overall capabilities of generic image and video comprehension. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_03565 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | INST-IT: Boosting Instance Understanding via Explicit Visual Prompt Instruction Tuning Peng, Wujian Meng, Lingchen Chen, Yitong Xie, Yiweng Liu, Yang Gui, Tao Xu, Hang Qiu, Xipeng Wu, Zuxuan Jiang, Yu-Gang Computer Vision and Pattern Recognition Large Multimodal Models (LMMs) have made significant breakthroughs with the advancement of instruction tuning. However, while existing models can understand images and videos at a holistic level, they still struggle with instance-level understanding that requires a more fine-grained comprehension and alignment. Instance-level understanding is crucial for LMMs, as it focuses on the specific elements that we are most interested in. Excitingly, existing works find that the SOTA LMMs exhibit strong instance understanding capabilities when provided with explicit visual cues. Motivated by this, we proposed Inst-IT, a solution to enhance LMMs in Instance understanding via explicit visual prompt Instruction Tuning for instance guidance. Inst-IT consists of a benchmark to diagnose multimodal instance-level understanding, a large-scale instruction-tuning dataset, and a continuous instruction-tuning training paradigm to effectively enhance spatial-temporal instance understanding capabilities of existing LMMs. Experimental results show that, enhanced by Inst-IT, our models not only achieve outstanding performance on Inst-IT Bench and other instance understanding benchmarks, but also demonstrate significant improvements across various generic image and video understanding benchmarks. This highlights that our method not only boosts instance-level understanding but also strengthens the overall capabilities of generic image and video comprehension. |
| title | INST-IT: Boosting Instance Understanding via Explicit Visual Prompt Instruction Tuning |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2412.03565 |