Apollo LMMs Apollo 1 5B T32

Table of Contents

Model Overview

Meet the Apollo model, a game-changer in the world of video understanding. This Large Multimodal Model (LMM) is designed to tackle complex tasks like long-form video comprehension, temporal reasoning, complex video question-answering, and multi-turn conversations grounded in video content.

Tasks and rank

Visual Recognition 888th

Visual Reasoning 276th

Video Understanding 114th

Scene Understanding 215th

Image Recognition 1509th

Learn More About the Ranking System

Capabilities

The Apollo model is a powerful tool for understanding videos. It can handle hour-long videos with ease, making it perfect for tasks like:

• Long-form video comprehension
• Temporal reasoning
• Complex video question-answering
• Multi-turn conversations grounded in video content

But what makes Apollo truly special? Let’s take a closer look.

Scaling Consistency

Apollo is designed to work efficiently, even at large scales. This means that it can handle complex tasks without breaking the bank. In fact, our model outperforms many competitors with just 3B parameters, and even rivals models with 30B parameters.

Efficient Video Sampling

Apollo uses advanced techniques like fps sampling and token resampling to get a better understanding of video content. This allows it to capture more information from videos, making it perfect for tasks like video question-answering.

Encoder Synergies

Apollo combines the power of two different encoders: SigLIP-SO400M (image) and InternVideo2 (video). This creates a robust representation of video content, outperforming single encoders on temporal tasks.

Putting it all Together

So, how does Apollo work in practice? Let’s take a look at an example.

Imagine you have a video of a cat playing with a ball. You want to ask the model to describe the video in detail. Here’s how you could do it:

1. Load the video into the model using the ApolloMMLoader class.
2. Create a conversation template using the conv_templates class.
3. Append the video description to the conversation template.
4. Generate a prompt using the get_prompt method.
5. Pass the prompt to the model using the generate method.
6. Print the output using the batch_decode method.

Examples

What is happening in this video? A group of people are having a meeting in a conference room. One person is standing at the front of the room and presenting a slide show. The others are seated and taking notes.

Describe the scene at 10 minutes into the video. At 10 minutes in, the presenter is discussing a graph on the screen and pointing to different sections. The audience is engaged and asking questions.

What is the main topic of discussion in this video? The main topic of discussion is the quarterly sales report and the strategies for improvement.

Getting Started

Want to try Apollo out for yourself? Here’s how to get started:

1. Install the model using pip: pip install -e.
2. Install the flash-attn library: pip install flash-attn --no-build-isolation
3. Load the model using the AutoModelForCausalLM class.
4. Start exploring the model’s capabilities using the example code provided.

Performance

Apollo is a powerhouse when it comes to video understanding tasks. But how does it really perform? Let’s dive into its speed, accuracy, and efficiency.

Speed

Apollo is surprisingly fast, considering its capabilities. It can handle hour-long videos with ease, thanks to its strategic design decisions. But what does this mean in practice? Imagine being able to analyze a full-length movie in a fraction of the time it would take other models. This is a game-changer for applications where time is of the essence.

Accuracy

Apollo boasts impressive accuracy, outperforming most 7B competitors with just 3B parameters. But what about the big players? Apollo even rivals 30B-scale models, which is a remarkable achievement. This means that Apollo can deliver accurate results without requiring an enormous amount of computational power.

Efficiency

Apollo is designed to be efficient, with features like:

• Scaling Consistency: Design decisions that work on smaller models and datasets also work on larger scales, reducing computation and experimentation costs.
• Efficient Video Sampling: Advanced token resampling strategies, like Perceiver, yield stronger temporal perception.
• Encoder Synergies: Combining SigLIP-SO400M (image) with InternVideo2 (video) delivers a robust representation, outperforming single encoders on temporal tasks.

Alternatives

MediaPipe Holistic 26000 Full-body landmark detection

MMSegmentation 7600 Semantic segmentation

Qwen2 VL 7B Instruct 712 Multimodal AI model

Clip Vit Base Patch32 505 Vision-Language Model

Vit Base Patch16 224 In21k 220 Image classification

Limitations

Apollo is a powerful model, but it’s not perfect. Let’s explore some of its limitations.

Limited Domain Knowledge

While Apollo excels in video understanding, its knowledge is limited to the data it was trained on. If you ask it a question that’s outside its domain, it might struggle to provide a accurate answer.

• Can Apollo answer questions about historical events or scientific concepts that aren’t related to video content?
• How would you handle a situation where Apollo is unsure or doesn’t know the answer to a question?

Temporal Reasoning Challenges

Apollo is designed to handle long-form videos, but it’s not immune to temporal reasoning challenges. It might struggle with complex scenarios or questions that require a deep understanding of time and causality.

• How would Apollo handle a question that requires it to understand the consequences of a specific action in a video?
• Can Apollo accurately predict the outcome of a sequence of events in a video?

Dependence on Video Quality

Apollo relies on high-quality video inputs to provide accurate answers. If the video is low-quality, blurry, or has poor audio, Apollo’s performance might suffer.

• How would Apollo handle a video with poor lighting or low resolution?
• Can Apollo still provide accurate answers if the video is corrupted or has missing frames?

Limited Contextual Understanding

While Apollo can understand video content, its contextual understanding is limited to the specific task it’s trained for. It might not fully understand the nuances of human language or the context in which a question is asked.

• Can Apollo understand sarcasm, idioms, or figurative language in video content?
• How would Apollo handle a question that requires it to understand the tone or emotions expressed in a video?

Evaluation Benchmark Limitations

ApolloBench, the evaluation benchmark for Apollo, is designed to focus on true video understanding capabilities. However, it might not cover all possible scenarios or edge cases.

• Are there any potential biases or limitations in the ApolloBench evaluation benchmark?
• How would you ensure that Apollo is evaluated fairly and comprehensively?