#multimodal

[beyond-mogai-pride-flags]

Sotrigender Pride Flag

Sotrigender or tritrisogender/trisotrigender: trimodal trigender in which someone is iso, trans, and cis; being trisogender as a result of being trigender; or being trigender as a result of being trisomodal.

[govindhtech]

Pegasus 1.2: High-Performance Video Language Model

Pegasus 1.2 revolutionises long-form video AI with high accuracy and low latency. Scalable video querying is supported by this commercial tool.

TwelveLabs and Amazon Web Services (AWS) announced that Amazon Bedrock will soon provide Marengo and Pegasus, TwelveLabs' cutting-edge multimodal foundation models. Amazon Bedrock, a managed service, lets developers access top AI models from leading organisations via a single API. With seamless access to TwelveLabs' comprehensive video comprehension capabilities, developers and companies can revolutionise how they search for, assess, and derive insights from video content using AWS's security, privacy, and performance. TwelveLabs models were initially offered by AWS.

Introducing Pegasus 1.2

Unlike many academic contexts, real-world video applications face two challenges:

Real-world videos might be seconds or hours lengthy.

Proper temporal understanding is needed.

TwelveLabs is announcing Pegasus 1.2, a substantial industry-grade video language model upgrade, to meet commercial demands. Pegasus 1.2 interprets long films at cutting-edge levels. With low latency, low cost, and best-in-class accuracy, model can handle hour-long videos. Their embedded storage ingeniously caches movies, making it faster and cheaper to query the same film repeatedly.

Pegasus 1.2 is a cutting-edge technology that delivers corporate value through its intelligent, focused system architecture and excels in production-grade video processing pipelines.

Superior video language model for extended videos

Business requires handling long films, yet processing time and time-to-value are important concerns. As input films increase longer, a standard video processing/inference system cannot handle orders of magnitude more frames, making it unsuitable for general adoption and commercial use. A commercial system must also answer input prompts and enquiries accurately across larger time periods.

Latency

To evaluate Pegasus 1.2's speed, it compares time-to-first-token (TTFT) for 3–60-minute videos utilising frontier model APIs GPT-4o and Gemini 1.5 Pro. Pegasus 1.2 consistently displays time-to-first-token latency for films up to 15 minutes and responds faster to lengthier material because to its video-focused model design and optimised inference engine.

Performance

Pegasus 1.2 is compared to frontier model APIs using VideoMME-Long, a subset of Video-MME that contains films longer than 30 minutes. Pegasus 1.2 excels above all flagship APIs, displaying cutting-edge performance.

Pricing

Cost Pegasus 1.2 provides best-in-class commercial video processing at low cost. TwelveLabs focusses on long videos and accurate temporal information rather than everything. Its highly optimised system performs well at a competitive price with a focused approach.

Better still, system can generate many video-to-text without costing much. Pegasus 1.2 produces rich video embeddings from indexed movies and saves them in the database for future API queries, allowing clients to build continually at little cost. Google Gemini 1.5 Pro's cache cost is $4.5 per hour of storage, or 1 million tokens, which is around the token count for an hour of video. However, integrated storage costs $0.09 per video hour per month, x36,000 less. Concept benefits customers with large video archives that need to understand everything cheaply.

Model Overview & Limitations

Architecture

Pegasus 1.2's encoder-decoder architecture for video understanding includes a video encoder, tokeniser, and big language model. Though efficient, its design allows for full textual and visual data analysis.

These pieces provide a cohesive system that can understand long-term contextual information and fine-grained specifics. It architecture illustrates that tiny models may interpret video by making careful design decisions and solving fundamental multimodal processing difficulties creatively.

Restrictions

Safety and bias

Pegasus 1.2 contains safety protections, but like any AI model, it might produce objectionable or hazardous material without enough oversight and control. Video foundation model safety and ethics are being studied. It will provide a complete assessment and ethics report after more testing and input.

Hallucinations

Occasionally, Pegasus 1.2 may produce incorrect findings. Despite advances since Pegasus 1.1 to reduce hallucinations, users should be aware of this constraint, especially for precise and factual tasks.

[eclecticsophism]

any experienced multimodal analysts have any Thoughts on ELAN? i'm on a hunt for a mac-compatible software for annotating vids that allows for a customizable coding scheme. lots of the ones i've seen are for conversation analysis -- which is awesome, but not aligned with my needs

[simplylaurent]

Content of Multimodality

The image attached above is the graphic I created as a multimodal resource. The image displays the eight concepts of rhetoric, serving as a guide into the complexities of writing. Specifically, how multiple variables influence the literary technique of the writer and the receptive perception of the viewer. Created in a well orchestrated diagram, the graphic shows the viewer framework of each concept in relation to another– much displaying how rhetoric isn’t effective if one piece is missing from the “symmetric” image. In course of the definitions, they were added as “mini notes” for the individual concepts of rhetoric for people like me who may be unfamiliar with one or two terms. Being a person who had never really knew what discourse community was, I found the graphic to be helpful in remembering the premise of it through a memorable layout.

[statistical-distr-of-polls]

Shape: E (Multimodal, Roughly Symmetrical)

[go-21newstv]

The Time-Dependent Multimodal Effects of Stress Hormones on Memory and Learning

According to the American Institute of Stress, 55% of people in the United States experience daily stress. Stress is, technically defined as the body’s nonspecific response to any demand – pleasant or unpleasant – but more commonly perceived as a state of physical, mental, or emotional strain or tension. Chronic stress is especially prevalent in the workplace, with 83% of employees reporting…

[damilola-doodles]

Project Title: ai-ml-ds-QtY3nCzKbfR – Multimodal Contrastive Learning with Keras - Keras-Exercise-081

Below is a highly advanced Keras project that is distinct from typical classification/regression tasks. It focuses on multimodal contrastive learning, combining image and tabular data in a self‑supervised framework—adapted from cutting‑edge research (e.g., CVPR 2023’s “Best of Both Worlds”) (arxiv.org). The code is the core of the response, with minimal explanation outside it. Project…

[statistical-distr-of-polls]

oops I missed this the first time 😭

Shape: Multimodal (?), Skewed Left

and did you have to look it up

[dammyanimation]

Project Title: ai-ml-ds-QtY3nCzKbfR – Multimodal Contrastive Learning with Keras - Keras-Exercise-081

Below is a highly advanced Keras project that is distinct from typical classification/regression tasks. It focuses on multimodal contrastive learning, combining image and tabular data in a self‑supervised framework—adapted from cutting‑edge research (e.g., CVPR 2023’s “Best of Both Worlds”) (arxiv.org). The code is the core of the response, with minimal explanation outside it. Project…

[damilola-ai-automation]

Project Title: ai-ml-ds-QtY3nCzKbfR – Multimodal Contrastive Learning with Keras - Keras-Exercise-081

Below is a highly advanced Keras project that is distinct from typical classification/regression tasks. It focuses on multimodal contrastive learning, combining image and tabular data in a self‑supervised framework—adapted from cutting‑edge research (e.g., CVPR 2023’s “Best of Both Worlds”) (arxiv.org). The code is the core of the response, with minimal explanation outside it. Project…

[damilola-warrior-mindset]

Project Title: ai-ml-ds-QtY3nCzKbfR – Multimodal Contrastive Learning with Keras - Keras-Exercise-081

Below is a highly advanced Keras project that is distinct from typical classification/regression tasks. It focuses on multimodal contrastive learning, combining image and tabular data in a self‑supervised framework—adapted from cutting‑edge research (e.g., CVPR 2023’s “Best of Both Worlds”) (arxiv.org). The code is the core of the response, with minimal explanation outside it. Project…

[damilola-moyo]

Project Title: ai-ml-ds-QtY3nCzKbfR – Multimodal Contrastive Learning with Keras - Keras-Exercise-081

Below is a highly advanced Keras project that is distinct from typical classification/regression tasks. It focuses on multimodal contrastive learning, combining image and tabular data in a self‑supervised framework—adapted from cutting‑edge research (e.g., CVPR 2023’s “Best of Both Worlds”) (arxiv.org). The code is the core of the response, with minimal explanation outside it. Project…

[daviddavi09]

Meta's Llama 4: The Most Powerful Al Yet!

In this episode of TechTalk, we dive deep into Meta's latest release LLaMA 4. What's new with LLaMA 4, and how does it stand apart from other leading models like ChatGPT-4, Claude, and Gemini?

[statistical-distr-of-polls]

Shape: Multimodal, Roughly Symmetrical (?)

Target audience

[johniac]

SciTech Chronicles. . . . . . . . .Mar 24th, 2025

[damilola-doodles]

Project Title:ai-ml-ds-XyzABC123 — Multimodal Transformer Fusion for Classification - Keras-Exercise-079

Here’s a highly advanced Keras project that’s quite different from typical CNN/RNN tasks—featuring a multimodal transformer that processes text + image + tabular inputs in a unified architecture. The code is the focus, with briefly summarized context. Let me know your thoughts or any tweaks you’d like! Project Title: ai-ml-ds-XyzABC123 — Multimodal Transformer Fusion for ClassificationFile:…