Large language models (LLMs) have successfully harnessed the power of subfields of artificial intelligence (ai), including natural language processing (NLP), natural language generation (NLG), and computer vision. With LLMs, it has become possible to create vision and language models that can reason complexly about images, answer image-related queries, and describe images in natural language. However, it is not yet known whether LLMs can perform localization tasks such as word localization or reference localization.
To overcome this challenge, a team of researchers from Google Research and UC San Diego has introduced an intelligent model called PixelLLM that can achieve fine-grained localization and vision-language alignment. This approach has been inspired by the way people naturally behave, especially babies who describe their visual environment with gestures, pointing and naming. The team has shared that the goal is to discover how LLMs can derive spatial understanding and reasoning from visual information.
PixelLLM densely aligns each word output of the language model to a pixel location. To do this, a small multilayer perceptron (MLP) was added on top of the word features, allowing it to return to the pixel location of each word. Low range fine-tuning (LoRA) has been used, which allows language model weights to be updated or frozen. The model can also receive text or location messages, allowing it to provide results tailored to the message.
The model architecture comprises an image encoder, a message encoder, and a message feature extractor. A large language model receives message-conditioned image features and an optional text message with output in the form of word localization and subtitles. With the ability to take various combinations of language or location as input or output, the architecture is versatile and adaptable to a wide range of vision and language activities.
The team evaluated the model using known vision tasks such as dense object captioning, location-conditioned captioning, and landmark localization. With notable performance metrics, including 89.8 [email protected] in RefCOCO reference localization, 19.9 CIDEr in Visual Genome conditional captions, and 17.0 mAP in dense object captions, PixelLLM has demonstrated state-of-the-art results on several challenges. . The pixel-dense localization formulation is important, as demonstrated by ablation studies in RefCOCO, which yield a 3.7 point gain over other localization formulations. Therefore, PixelLLM has proven successful in achieving accurate vision and language alignment and localization.
The team has summarized its main contributions as follows.
- A new vision and language model called PixelLLM has been introduced, which produces word localization and can generate image captions.
- The model supports text or optional location cues in addition to image input.
- The localized narrative dataset has been used for word localization training,
- The model is capable of adapting to a variety of vision and language tasks, including segmentation, location-conditioned captioning, reference localization, and dense captioning.
- The model has shown superior results in location-driven subtitles, dense subtitles, and reference localization and segmentation.
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Tanya Malhotra is a final year student of University of Petroleum and Energy Studies, Dehradun, pursuing BTech in Computer Engineering with specialization in artificial intelligence and Machine Learning.
She is a Data Science enthusiast with good analytical and critical thinking, along with a burning interest in acquiring new skills, leading groups and managing work in an organized manner.
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