Contextual Object Detection with Multimodal Large Language Models

Article: https://arxiv.org/pdf/2305.18273.pdf

 

Introduction

Object detection, a crucial aspect of computer vision, involves understanding the objects present in a scene, enabling various applications like robotics, autonomous driving, and AR/VR systems. Recently, Multi-modal Language Models (MLLMs) such as Flamingo, PaLM-E, and OpenAI's GPT-4 have demonstrated remarkable abilities in vision-language tasks like image captioning and question answering. These models enable interactive human-AI interactions, necessitating the modeling of contextual information and relationships among visual objects, human words, phrases, and dialogues. Therefore, there is a need to enhance MLLMs by enabling them to locate, identify, and associate visual objects with language inputs for effective human-AI interaction.

 

Top of Form

Concepts

Multimodal Large Language Models (MLLMs) combine language comprehension with visual inputs, expanding the capabilities of Large Language Models (LLMs). Notable examples include GPT series, T5, PaLM, OPT, and LLaMA. MLLMs have excelled in vision-language tasks like image captioning and visual question answering. However, they are limited to generating text outputs. In contrast, ContextDET, built upon MLLMs, enables contextual object detection with bounding box outputs.

Prompting LLMs with Vision Experts has been explored, leveraging textual outputs from LLMs as prompts for vision expert models like DETR and SAM. In contrast, ContextDET employs an end-to-end training pipeline, utilizing latent features from MLLMs as conditional inputs for a visual decoder, enabling bounding box prediction.

Contextual understanding in object detection involves leveraging multimodal patterns and relationships between visual images and textual words. ContextDET leverages the contextual understanding capability of MLLMs for object detection and proposes new evaluation tasks like the cloze test to assess contextual understanding.

Zero-shot object detection remains challenging, especially in real-world scenarios. Open-Vocabulary Object Detection allows the utilization of additional image-text pairs. While CLIP has been widely used, ContextDET demonstrates the effectiveness of MLLMs in the open-vocabulary setting. It is not constrained by predefined base or novel classes, and the predicted object names align with the most contextually valid English words generated by the MLLMs.

Experiments

Reporting the results of ContextDET on various tasks, including contextual object detection, open-vocabulary object detection, and referring image segmentation. In the context of contextual object detection, we focus on presenting both quantitative and qualitative results for the cloze test setting, which poses a significant challenge due to inferring object words from a vast human vocabulary. Additionally, we provide qualitative results for contextual captioning and contextual question-answering.

Regarding implementation details, the method is implemented in PyTorch, and all models are trained on a single machine equipped with 4 NVIDIA A100 GPUs. During training, we apply data augmentation techniques such as random horizontal flipping and large-scale jittering. The batch size is set to 8, and the model is trained for 6 epochs. We utilize the AdamW optimizer with a learning rate of 1e-4 and a weight decay of 0.05.

 

Conclusion

ContextDET, highlights the untapped potential of Multimodal Large Language Models (MLLMs) in various perception tasks beyond vision-language tasks. Specifically, we focus on the contextual object detection task, which involves predicting precise object names and their locations in images for human-AI interaction. However, due to the high annotation cost of associating object words with bounding boxes, we had to use less training data compared to previous MLLM papers, which may have impacted our final performance. To address this, future research could explore the use of semi-supervised or weakly-supervised learning techniques to reduce annotation costs.

Furthermore, while MLLMs demonstrate contextual understanding abilities, there are other unexplored capabilities that can be leveraged for downstream tasks. For example, we propose investigating their interactive ability for instruction tuning. Can MLLMs be utilized to refine detection outputs based on human language instructions? By providing specific instructions such as adjusting box positions, removing redundant boxes, or correcting predicted classes, can MLLMs adapt their predictions to meet desired expectations? Exploring these possibilities could revolutionize computer vision tasks.

 

 

 

 

 

 

Enhancing Visual Text Generation with GlyphControl

Article: https://arxiv.org/pdf/2305.18259.pdf

 

Introduction

GlyphControl is an innovative approach that improves text-to-image generation by incorporating glyph conditional information. It allows users to customize the content, location, and size of the generated text. In this blog post, we explore the advantages of GlyphControl and its superior performance compared to existing methods.

Advantages of GlyphControl

GlyphControl enhances the Stable-Diffusion model without requiring retraining. Users can customize the generated text according to their needs, resulting in visually appealing and accurate results.

The LAION-Glyph Benchmark Dataset

GlyphControl includes the LAION-Glyph training benchmark dataset, which helps researchers evaluate visual text generation approaches effectively.

 

Superior Performance

GlyphControl outperforms the DeepFloyd IF approach in terms of OCR accuracy and CLIP scores, demonstrating its effectiveness in generating high-quality visual text.

Future Implications

GlyphControl opens up new possibilities in content creation, design, and advertising. Further advancements are expected as researchers build upon GlyphControl's foundation.

Conclusion

GlyphControl is a powerful approach that improves text-to-image generation by leveraging glyph conditional information. It offers customization options, performs well compared to existing methods, and has promising implications for various applications.

 

 

 

 

Pix2Repair: Automated Shape Repair from Images

Article: https://arxiv.org/pdf/2305.18273.pdf

 

Introduction:

 Pix2Repair is an innovative approach that automates shape repair by generating restoration shapes from images. This eliminates the need for expensive 3D scanners and manual cleanup, making the process more accessible and scalable.

Problem:

Traditional shape repair methods rely on high-resolution 3D meshes obtained through costly 3D scanning. This approach is time-consuming and limits accessibility.

Solution:

Pix2Repair takes an image of a fractured object as input and generates a 3D printable restoration shape. It utilizes a novel shape function that deconstructs a latent code representing the object into a complete shape and a break surface.

 

 

 

Summary:

Pix2Repair revolutionizes shape repair by leveraging image-based restoration techniques. It eliminates the need for expensive 3D scanners and manual cleanup, offering a more accessible and scalable solution. Key Contributions: Image-Based Restoration: Pix2Repair generates restoration shapes directly from images, eliminating the need for 3D scanning. Novel Shape Function: The proposed shape function deconstructs a latent code into a complete shape and a break surface, enabling accurate restoration. Dataset Applications: Successful restorations were demonstrated for synthetic fractures and cultural heritage objects from various datasets. Overcoming Challenges: Pix2Repair handles axially symmetric objects by predicting view-centered restorations. Superior Performance: Pix2Repair outperforms shape completion approaches in terms of various metrics, including chamfer distance and normal consistency.

 

 

Conclusion:

Pix2Repair offers an automated shape repair solution by leveraging images, removing the need for expensive 3D scanning equipment. Its novel approach shows promising results in restoring fractured objects, making shape repair more accessible and efficient. This innovation has the potential to transform the field of object restoration and benefit researchers, conservators, and restoration professionals.