Authors:
(1) Hanoona Rasheed, Mohamed bin Zayed University of AI and equally contributing first authors;
(2) Muhammad Maaz, Mohamed bin Zayed University of AI and equally contributing first authors;
(3) Sahal Shaji, Mohamed bin Zayed University of AI;
(4) Abdelrahman Shaker, Mohamed bin Zayed University of AI;
(5) Salman Khan, Mohamed bin Zayed University of AI and Australian National University;
(6) Hisham Cholakkal, Mohamed bin Zayed University of AI;
(7) Rao M. Anwer, Mohamed bin Zayed University of AI and Aalto University;
(8) Eric Xing, Mohamed bin Zayed University of AI and Carnegie Mellon University;
(9) Ming-Hsuan Yang, University of California - Merced and Google Research;
(10) Fahad S. Khan, Mohamed bin Zayed University of AI and Linköping University. Editor's Note: This is Part 2 of 10 of a study detailing the development of an AI model that is designed to describe images to users. Read the rest below. Table of Links Abstract and 1 Introduction
2. Related Work
3. Method
4. Data Annotation Pipeline
5. Experiments
6. Conclusion and References Supplementary Material (Part 1) A. Additional Implementation Details
B. Additional Downstream Tasks
C. Additional Qualitative Results Supplementary Material (Part 2) D. Dataset Visualization
E. Limitations and Future Work
F. Ethics and Societal Impact 2. Related Work LMMs provide a versatile interface for a diverse array of tasks, encompassing language and vision. Prominent models such as BLIP-2 [24], LLaVA [29], InstructBLIP [6] and MiniGPT-4 [61] first conduct image-text feature alignment followed by instruction tuning. Other representative works include Otter [22], mPLUG-Owl [52], LLaMaAdapter [56], Video-ChatGPT [32], InternGPT [31]. However, these approaches lack region-specific understanding. Recent works like Kosmos-2 [35], Shikra [5], GPT4RoI [57], VisionLLM [44], Ferret [53] and All-Seeing [45] aim to allow region-specific conversation. Some methods [5, 35, 45, 53] input location bins and bounding boxes with image data for region-level understanding, relying on the LLM exclusively for interpreting these regions. GPT4RoI advances this by using spatial boxes and RoI-aligned features for input and training on region-text pairs. BuboGPT [59] utilizes an off-the-shelf grounding model [30] and matches the groundings with the language response. In contrast, LISA [21] utilizes embeddings from the vision language model and the SAM [18] decoder to generate output segmentation masks. However, LISA cannot comprehend specific image regions or handle multiple instances. To classify the LMM landscape, methods can be partitioned into four distinct categories (see Tab. 1 - separated via dotted lines). The first encompasses models effective in textual responses but lacking in region-specific capabilities [6, 8, 22, 29, 51, 52, 61]. In contrast, among models that handle region inputs or offer visual grounding, three more categories emerge. The first of these incorporates external vision modules [31, 59], and the next relies exclusively on LMMs for region understanding [5, 35, 36, 44]. The last category combines specialized vision modules with LMMs, trained end-to-end for a comprehensive understanding of regions [21, 45, 57]. Our approach belongs to the last category and distinctly offers pixel-level grounding together with multi-turn conversations and the flexibility to operate on both input images and specific regions. Further, we provide large-scale instance-level grounded visual understanding dataset that allows generalizability of GLaMM to multiple vision-language tasks. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Hanoona Rasheed, Mohamed bin Zayed University of AI and equally contributing first authors; (2) Muhammad Maaz, Mohamed bin Zayed University of AI and equally contributing first authors; (3) Sahal Shaji, Mohamed bin Zayed University of AI; (4) Abdelrahman Shaker, Mohamed bin Zayed University of AI; (5) Salman Khan, Mohamed bin Zayed University of AI and Australian National University; (6) Hisham Cholakkal, Mohamed bin Zayed University of AI; (7) Rao M. Anwer, Mohamed bin Zayed University of AI and Aalto University; (8) Eric Xing, Mohamed bin Zayed University of AI and Carnegie Mellon University; (9) Ming-Hsuan Yang, University of California - Merced and Google Research; (10) Fahad S. Khan, Mohamed bin Zayed University of AI and Linköping University. Authors: Authors: (1) Hanoona Rasheed, Mohamed bin Zayed University of AI and equally contributing first authors; (2) Muhammad Maaz, Mohamed bin Zayed University of AI and equally contributing first authors; (3) Sahal Shaji, Mohamed bin Zayed University of AI; (4) Abdelrahman Shaker, Mohamed bin Zayed University of AI; (5) Salman Khan, Mohamed bin Zayed University of AI and Australian National University; (6) Hisham Cholakkal, Mohamed bin Zayed University of AI; (7) Rao M. Anwer, Mohamed bin Zayed University of AI and Aalto University; (8) Eric Xing, Mohamed bin Zayed University of AI and Carnegie Mellon University; (9) Ming-Hsuan Yang, University of California - Merced and Google Research; (10) Fahad S. Khan, Mohamed bin Zayed University of AI and Linköping University. Editor's Note: This is Part 2 of 10 of a study detailing the development of an AI model that is designed to describe images to users. Read the rest below. Editor's Note: This is Part 2 of 10 of a study detailing the development of an AI model that is designed to describe images to users. Read the rest below. Editor's Note: This is Part 2 of 10 of a study detailing the development of an AI model that is designed to describe images to users. Read the rest below. Editor's Note: This is Part 2 of 10 of a study detailing the development of an AI model that is designed to describe images to users. Read the rest below. Table of Links Abstract and 1 Introduction 2. Related Work 3. Method 4. Data Annotation Pipeline 5. Experiments 6. Conclusion and References Abstract and 1 Introduction Abstract and 1 Introduction 2. Related Work 2. Related Work 3. Method 3. Method 4. Data Annotation Pipeline 4. Data Annotation Pipeline 5. Experiments 5. Experiments 6. Conclusion and References 6. Conclusion and References Supplementary Material (Part 1) Supplementary Material (Part 1) A. Additional Implementation Details B. Additional Downstream Tasks C. Additional Qualitative Results A. Additional Implementation Details A. Additional Implementation Details B. Additional Downstream Tasks B. Additional Downstream Tasks C. Additional Qualitative Results C. Additional Qualitative Results Supplementary Material (Part 2) Supplementary Material (Part 2) D. Dataset Visualization E. Limitations and Future Work F. Ethics and Societal Impact D. Dataset Visualization D. Dataset Visualization E. Limitations and Future Work E. Limitations and Future Work F. Ethics and Societal Impact F. Ethics and Societal Impact 2. Related Work LMMs provide a versatile interface for a diverse array of tasks, encompassing language and vision. Prominent models such as BLIP-2 [24], LLaVA [29], InstructBLIP [6] and MiniGPT-4 [61] first conduct image-text feature alignment followed by instruction tuning. Other representative works include Otter [22], mPLUG-Owl [52], LLaMaAdapter [56], Video-ChatGPT [32], InternGPT [31]. However, these approaches lack region-specific understanding. Recent works like Kosmos-2 [35], Shikra [5], GPT4RoI [57], VisionLLM [44], Ferret [53] and All-Seeing [45] aim to allow region-specific conversation. Some methods [5, 35, 45, 53] input location bins and bounding boxes with image data for region-level understanding, relying on the LLM exclusively for interpreting these regions. GPT4RoI advances this by using spatial boxes and RoI-aligned features for input and training on region-text pairs. BuboGPT [59] utilizes an off-the-shelf grounding model [30] and matches the groundings with the language response. In contrast, LISA [21] utilizes embeddings from the vision language model and the SAM [18] decoder to generate output segmentation masks. However, LISA cannot comprehend specific image regions or handle multiple instances. To classify the LMM landscape, methods can be partitioned into four distinct categories (see Tab. 1 - separated via dotted lines). The first encompasses models effective in textual responses but lacking in region-specific capabilities [6, 8, 22, 29, 51, 52, 61]. In contrast, among models that handle region inputs or offer visual grounding, three more categories emerge. The first of these incorporates external vision modules [31, 59], and the next relies exclusively on LMMs for region understanding [5, 35, 36, 44]. The last category combines specialized vision modules with LMMs, trained end-to-end for a comprehensive understanding of regions [21, 45, 57]. Our approach belongs to the last category and distinctly offers pixel-level grounding together with multi-turn conversations and the flexibility to operate on both input images and specific regions. Further, we provide large-scale instance-level grounded visual understanding dataset that allows generalizability of GLaMM to multiple vision-language tasks. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

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This New AI Model Is Excelling in Understanding and Interacting with Images

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