Image Generation Evaluation + Cambrian-1

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DREAMBENCH++: A Human-Aligned Benchmark for Personalized Image Generation

Rich Human Feedback for Text-to-Image Generation

Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs

[BioFocher]

I also listen to your streams as Podcast when Im at the gym 😂

[wolpumba]

*Summary*

*Main Topics:*

* *Shifting Landscape of ML Papers (**3:34**):* Fewer groundbreaking papers from independent researchers as big companies absorb talent.
* *Image Generation Evaluation (**5:24**):* Focusing on two papers:
* *Rich Human Feedback for Text-to-Image Generation (**12:09**):* Collects detailed human feedback (annotated regions, misaligned keywords) to train a reward model for evaluating generated images.
* *DREAMBENCH++: A Human-Aligned Benchmark for Personalized Image Generation (**15:34**):* Proposes using a multimodal large language model (MLLM) like GPT-4 to automatically evaluate generated images based on pre-defined criteria and chain-of-thought reasoning.
* *Cambrian-1 Paper (**16:17**):*
* A survey of MLLMs, advocating for a shift from vision-language models (VLMs) to MLLMs.
* Explores various aspects of MLLMs like pre-training methods, connector designs, instruction tuning data and evaluation protocols.
* Highlights challenges in existing VLM benchmarks (reliance on language models, lack of visual grounding).
* Proposes repurposing existing vision benchmarks for MLLM evaluation.
* *Meta 3D Gen (**1:31:43**):*
* New state-of-the-art text-to-3D asset generation pipeline from Meta.
* Generates multi-view consistent images and uses a signed distance function (SDF) to create high-quality 3D meshes.
* *Comparison of Text-to-3D Generation Tools (**1:38:15**):*
* Compared Meta 3D Gen to commercially available tools like Tripo Sr and Meshy.
* Found that all tools struggle with complex prompts and require significant prompt engineering.
* Tripo Sr performed better than Meshy in the demonstrated examples.

*Key Takeaways:*

* Automated image evaluation using MLLMs is a promising direction for the future of generative AI.
* Instruction tuning data and its mixture ratio heavily influence MLLM performance.
* Data diversity and scale are crucial for training robust MLLMs.
* Current text-to-3D tools are still in early stages and require significant prompt engineering to achieve high-quality results.

*Discussion Points:*

* The role of subconscious human feedback in image evaluation.
* The potential of graph neural networks (GNNs) in object detection.
* The convergence of various AI tasks towards MLLMs.
* The future of prompt engineering and the development of more intuitive generative AI tools.


i used gemini 1.5 pro to summarize the video

[dadsonworldwide]

If all models converge thats gonna be a puritan ai model ultimately lol newton doesn't use plato he sees a 3 body problem rather than 3 lines of measure = eqaulibrium
So do they tune weights and measure in many different nureal node models that are able to accessibly switch back, and for model to model to follow different lines of step by step?
What i see image or llm nueral nodes convergences should occur based on how we prescribe symbols upon objects. How we taught ourselves pre secularized eras.
I hear about allowing the thermostat temperature of the room thermodynamics to tune some nureal nodes sensor to help simulate crystalline of water ice molecular space between axioms particles of inertia.
I ask only because im recognizing how successful social behavior in ancient world esoterically applied symbols to objects .like curses and blessings standardized weights and measure in market place. That addition and subtraction is tuned in pragmatic common sense Christian objectivism proper phylosphy , longitude latitude of English, encoded with not etymology but kings james Biblical coded things like King assure into measure.
Yes its built on other building block of language etymology but much more is encoded in puns .it was an alignment of natures orientation and direction.

For someone like elon who wants self drivers, optimus grok all networking with his gen "x" platform where hes been admit about it being governed by American English law connected to Austin it all has this common sense objectivism proper tuned weights and measure in such a network.
This is tuned with issac newton reverse metamorphosis theological thread like American founders used and so did the king James English authors.

His xai is to be tuned completely differently for understanding the universe he says, which theyve messed all up in astronomy to where equivalence principle opposite to newtons frame of reference..flips the weights and measure.

Obviously most of these American tech names tell how they know textualism methodology objectivism = technological origins in the esoteric roots. All of them are something weights and measure anthropic, meta, Optimus, etc etc etc

But not all lines of thought are eqaul, not all are logical rational and blessed with eqaul measure in other languages. Most are very dualistic and even Americans tuned that way dont know how Their They're There +time to use English. They interchange devided in /individual as if it was a personal response or actor.
They use free will inertia as if its soul agency or individual and not a personal actions in or on a frame of reference

Anthromorphized model would no doubt use dualistic umbrella terms or measurements.

Anyway sorry if i cant articulate the question or point well here.

American English courts or pragmaticism you never fall into whataboutisms of hulucinations or nilhisms of just uh deductiveness.
To many curses and blessings trying to gatekeep or block, censor or just using the wrong line of measure would cause that to happen..you can use physical forms & shape to see this statistical anylitical failures and deformitys excersized.

But it would correlate subjective space between soul agency thats divded in individuals atoms 2 personal response free will inertia lattus structure and body 3 frame of reference critical extreme states or environment.
Xyz manmade time hierarchy knowledge of good evil equations

Its how free speech bottom up rule all falls out of nature mechanics English pragmaticism etc etc
But now other phylosphical world views get it all mixed up

[dadsonworldwide]

Everyones most taught fundamental go to ordering skills from biology is an umbrella hierarchical pov population species that is like defining the city by.
Big Walls or cell walls
Or
Ethnicity personal actors
Looks ok on paper but once put in excersize it hyper split no strong identifiers
You csn legitimately hyper split over the slightest variations its the most taught to ourselves of all.
Its like how all 32 nfl draft team fans look on paper and sees a playoff roster but once you put it in excersize the nilhisms fall out .