Generalist Vision Models for Any-to-Any Image-to-Video Understanding
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V6I3P117Keywords:
Generalist Vision Models, Any-To-Any Modeling, Multimodal Learning, Image Understanding, Video Understanding, Vision-Language Models, Unified-IO, Pali-3, 4M-21Abstract
Current developments in multimodal foundation models are driving computer vision beyond sets of task-specific systems to generalist vision models that can be used to do a large number of tasks and modalities in the same architecture. Simultaneously, video understanding has transitioned off of specialized backbones and to large models that collectively reason over images, videos and language. Any-to-any vision models strive to bring such a trend together: they take in heterogeneous visual and textual inputs (e.g. image to caption, video to action labels, of image+text to edited image) and deliver heterogeneous outputs via a common interface. The article examines the new space of generalist vision models of any-to-any image-to-video understanding. We introduce the concept of any-to-any modeling first and place it in the context of the rest of the literature on multitask and multimodal learning. We next outline typical models of such as Unified-IO 2, UnIVAL, PaLi-3, and 4M-21 that handle a broad input/output model of images, videos, audio, dense labels and free-form language [1] . We draw attention to general building blocks (unified tokenization, transformer backbones, diffusion or autoregressive heads) and training strategies (large-scale pretraining, instruction tuning, and multi-task curricula). We comment on the performance of these models on image and video benchmark tasks, including question answering, captioning, and spatiotemporal reasoning by using public benchmark results. Lastly, we provide practical and ethical advice in the deployment of generalist vision models in practice and provide open issues, such as unified evaluation in any-to-any context, efficient management of long videos sequences, and safety of open-ended visual interaction. We would like to offer a systematic and human-readable introduction that can assist the researcher and practitioner that is interested in constructing or using generalist image-to-video understanding systems
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References
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