Sure! Here’s a forum-style post you can use or adapt for a discussion on a Vision-Language Models (VLM) and Large Language Models (LLM) topic, particularly around the idea of “projectors” and standardized pipelines:
Title: Are VLM “Projectors” the Key to Standardizing GenAI Pipelines – Including Decoders?
Hi all,
I’ve been digging into how Vision-Language Models (VLMs) handle multimodal tasks, and one recurring architectural element that caught my attention is the projector — the module that maps vision embeddings (from CLIP, ViT, etc.) into the language model space. It’s effectively a bridge between modalities, but it also feels like a standard interface — and that got me thinking.
Could these projectors be thought of as the beginnings of a standardized GenAI task pipeline? They seem to abstract modality-specific preprocessing in a similar way to how media pipelines (like GStreamer for video) abstract data input, filtering, and transformation. If so, shouldn’t we be thinking more explicitly about standardizing both the encoder (e.g., projector) and decoder stages?
In fact, this idea could extend beyond vision inputs. You could imagine audio-to-text, tabular-to-text, or even structured data-to-language “projectors” that follow a common spec for embedding transformation — making the whole GenAI stack more composable, modular, and easier to plug into.
Questions for the community:
- Are there any active efforts to standardize these projector components or decoder pipelines in GenAI frameworks?
- Has anyone seen a GStreamer-style pipeline framework (or idea) applied to VLM or LLM decoding workflows?
- Would such a modular pipeline even be feasible given the rapid pace of change in model architectures?
Would love to hear if anyone is working on this, or if there are any open-source efforts or design patterns already leaning in this direction.
Cheers,
victore