search
HomeTechnology peripheralsAIAdd fast and slow eyes to the video model, Apple's new training-free method surpasses everything SOTA in seconds

Since the release of Sora, the field of AI video generation has become more "busy". In the past few months, we have witnessed Jimeng, Runway Gen-3, Luma AI, and Kuaishou Keling taking turns to explode.

Unlike the past models that can be identified as AI-generated at a glance, this batch of large video models may be the "best" we have ever seen.

However, behind the amazing performance of the video large language model (LLM) is a huge and finely annotated video data set, which requires a very high cost. Recently, a number of innovative methods have emerged in the research field that do not require additional training: using trained image large language models to directly process video tasks, thus bypassing the "expensive" training process.

In addition, most existing video LLMs suffer from two major disadvantages: (1) they can only handle video input with a limited number of frames, which makes it difficult for the model to capture the subtle spatial and temporal content in the video; (2) they It lacks temporal modeling design, but simply inputs video features into LLM, completely relying on LLM's ability to model motion.

In response to the above problems, Apple researchers proposed SlowFast-LLaVA (SF-LLaVA for short). This model is based on the LLaVA-NeXT architecture developed by the Byte team. It requires no additional fine-tuning and can be used out of the box. Inspired by the successful two-stream network in the field of action recognition, the research team designed a novel SlowFast input mechanism for video LLM.

Simply put, SF-LLaVA will understand the details and motion in the video through two different observation speeds (Slow and Fast).

  • Slow path: extract features at low frame rates while retaining as much spatial detail as possible (e.g. retain 24×24 tokens every 8 frames)
  • Fast path: run at high frame rates, but Use a larger spatial pooling step size to reduce the resolution of the video to simulate a larger temporal context and focus more on understanding the coherence of actions

This is equivalent to the model having two "eyes": one Just look slowly and pay attention to the details; the other one is to look quickly and pay attention to the movements. This solves the pain points of most existing video LLMs and can capture both detailed spatial semantics and longer temporal context.

Add fast and slow eyes to the video model, Apples new training-free method surpasses everything SOTA in seconds

Paper link: https://arxiv.org/pdf/2407.15841

Experimental results show that SF-LLaVA surpasses existing training-free methods by significant advantages in all benchmark tests. Compared with carefully fine-tuned SFT models, SF-LLaVA achieves the same performance or even better.

Add fast and slow eyes to the video model, Apples new training-free method surpasses everything SOTA in seconds

Model architecture

As shown in the figure below, SF-LLaVA follows the standard training-free video LLM process. It takes a video V and a question Q as input and outputs the corresponding answer A.

Add fast and slow eyes to the video model, Apples new training-free method surpasses everything SOTA in seconds

對於輸入,要從每個視訊任意大小和長度中均勻取樣 N 幀,I = {I_1, I_2, ..., I_N},不需要對選取的視訊幀進行特別的組合或排列。以幀為單位視獨立提取頻特徵為 F_v ∈ R^N×H×W,其中 H 和 W 分別為幀特徵的高度和寬度。

下一步需要在慢速和快速兩個路徑中進一步處理 F_v,並將它們結合起來作為有效的視頻表示。慢速路徑從 F_v 中均勻取樣Add fast and slow eyes to the video model, Apples new training-free method surpasses everything SOTA in seconds的幀特徵,其中Add fast and slow eyes to the video model, Apples new training-free method surpasses everything SOTA in seconds

先前有研究發現,在空間維度上適當池化可以提高影片產生的效率和穩健性。因此,研究團隊在 F_v 上應用步長為 σ_h×σ_w 的池化過程,得到最終特徵:Add fast and slow eyes to the video model, Apples new training-free method surpasses everything SOTA in seconds,其中Add fast and slow eyes to the video model, Apples new training-free method surpasses everything SOTA in secondsAdd fast and slow eyes to the video model, Apples new training-free method surpasses everything SOTA in seconds。慢速路徑的整個過程如公式 2 所示。

Add fast and slow eyes to the video model, Apples new training-free method surpasses everything SOTA in seconds

快速路徑保留 F_v 中的所有幀特徵,以盡可能多地捕捉視訊的長程時間上下文。具體來說,研究團隊使用空間池化步長Add fast and slow eyes to the video model, Apples new training-free method surpasses everything SOTA in seconds對 F_v 進行激進的下取樣,得到最終特徵Add fast and slow eyes to the video model, Apples new training-free method surpasses everything SOTA in seconds。研究團隊設定Add fast and slow eyes to the video model, Apples new training-free method surpasses everything SOTA in secondsAdd fast and slow eyes to the video model, Apples new training-free method surpasses everything SOTA in seconds,讓快速路徑能專注於模擬時間脈絡和運動線索。慢速路徑的整個過程如公式 3 所示。

Add fast and slow eyes to the video model, Apples new training-free method surpasses everything SOTA in seconds

最後,得到聚合的視訊特徵:Add fast and slow eyes to the video model, Apples new training-free method surpasses everything SOTA in seconds,其中 flat 和 [, ] 分別表示展平和連接操作。如表達式所示,Add fast and slow eyes to the video model, Apples new training-free method surpasses everything SOTA in seconds不需要任何特殊的 token 來分隔慢速和快速路徑。 SF-LLaVA 總共使用Add fast and slow eyes to the video model, Apples new training-free method surpasses everything SOTA in seconds個影片 token。影片的視覺特徵Add fast and slow eyes to the video model, Apples new training-free method surpasses everything SOTA in seconds將和文字訊息(例如使用者提出的問題)將被組合在一起,作為輸入資料送入大型語言模型(LLM)進行處理。

SlowFast 流程如公式 4 所示。

Add fast and slow eyes to the video model, Apples new training-free method surpasses everything SOTA in seconds

實驗結果

研究團隊對 SF-LLaVA 進行了全面的性能評估,將其與當前 SOTA 免訓練模型(如 IG-VLM 和 LLoVi)在多個視訊問答任務中進行了對比。此外,他們還將其與經過視訊資料集監督微調(SFT)的視訊 LLM,例如 VideoLLaVA 和 PLLaVA 進行了比較。

開放式視訊問答

如下表所示,在開放式視訊問答任務中,SF-LLaVA 在所有基準測試中都比現有的免訓練方法表現得更好。具體來說,分別搭載7B 和34B 參數規模的LLM 時,SF-LLaVA 分別在MSRVTT-QA 上比IGVLM 高出2.1% 和5.0%,在TGIF-QA 上高出5.7% 和1.5%,在ActivityNet -QA 上高出2.0% 和0.8%。

即使與經過微調的SFT 方法相比,SF-LLaVA 在大多數基準測試中也展現了可比的性能,只有在ActivityNet-QA 這一基准上,PLLaVA 和LLaVA-NeXT-VideoDPO 略勝一籌。

Add fast and slow eyes to the video model, Apples new training-free method surpasses everything SOTA in seconds

多項選擇視訊問答

從下表中可見,在所有基準測試中,SF-LLaVA 在多項選擇視訊問答的表現都優於其他免費訓練方法。在要求複雜長時序推理的 EgoSchema 資料集中,SF-LLaVA7B 和 34B 的版本相較 IG-VLM 模型的得分分別高出 11.4% 和 2.2%。

雖然 VideoTree 在基準測試中領先,因為它是基於 GPT-4 的專有模型,因而性能遠高於開源 LLM。與 SFT 方法相比,SF-LLaVA 34B 模型在 EgoSchema 上也取得了更好的結果,這證實了 SlowFast 設計處理長影片的強大能力。
Text Generation 

Add fast and slow eyes to the video model, Apples new training-free method surpasses everything SOTA in seconds

文生影片

如表 3 所示,對於文字產生影片的任務,SF-LLaVA 也顯示出了一些優勢。 SF-LLaVA-34B 在整體表現上超越了所有免訓練的基準。儘管在細節取向方面,SF-LLaVA 略遜於 LLaVA-NeXT-Image。基於 SlowFast 設計,SF-LLaVA 可以用更少的視覺 token 覆蓋更長的時間上下文,因此在時間理解任務中表現得格外出色。

此外,在文生影片的表現上,SF-LLaVA-34B 也優於大多數 SFT 方法。

Add fast and slow eyes to the video model, Apples new training-free method surpasses everything SOTA in seconds

更多細節,請參考原論文。

The above is the detailed content of Add fast and slow eyes to the video model, Apple's new training-free method surpasses everything SOTA in seconds. For more information, please follow other related articles on the PHP Chinese website!

Statement
The content of this article is voluntarily contributed by netizens, and the copyright belongs to the original author. This site does not assume corresponding legal responsibility. If you find any content suspected of plagiarism or infringement, please contact admin@php.cn
The AI Skills Gap Is Slowing Down Supply ChainsThe AI Skills Gap Is Slowing Down Supply ChainsApr 26, 2025 am 11:13 AM

The term "AI-ready workforce" is frequently used, but what does it truly mean in the supply chain industry? According to Abe Eshkenazi, CEO of the Association for Supply Chain Management (ASCM), it signifies professionals capable of critic

How One Company Is Quietly Working To Transform AI ForeverHow One Company Is Quietly Working To Transform AI ForeverApr 26, 2025 am 11:12 AM

The decentralized AI revolution is quietly gaining momentum. This Friday in Austin, Texas, the Bittensor Endgame Summit marks a pivotal moment, transitioning decentralized AI (DeAI) from theory to practical application. Unlike the glitzy commercial

Nvidia Releases NeMo Microservices To Streamline AI Agent DevelopmentNvidia Releases NeMo Microservices To Streamline AI Agent DevelopmentApr 26, 2025 am 11:11 AM

Enterprise AI faces data integration challenges The application of enterprise AI faces a major challenge: building systems that can maintain accuracy and practicality by continuously learning business data. NeMo microservices solve this problem by creating what Nvidia describes as "data flywheel", allowing AI systems to remain relevant through continuous exposure to enterprise information and user interaction. This newly launched toolkit contains five key microservices: NeMo Customizer handles fine-tuning of large language models with higher training throughput. NeMo Evaluator provides simplified evaluation of AI models for custom benchmarks. NeMo Guardrails implements security controls to maintain compliance and appropriateness

AI Paints A New Picture For The Future Of Art And DesignAI Paints A New Picture For The Future Of Art And DesignApr 26, 2025 am 11:10 AM

AI: The Future of Art and Design Artificial intelligence (AI) is changing the field of art and design in unprecedented ways, and its impact is no longer limited to amateurs, but more profoundly affecting professionals. Artwork and design schemes generated by AI are rapidly replacing traditional material images and designers in many transactional design activities such as advertising, social media image generation and web design. However, professional artists and designers also find the practical value of AI. They use AI as an auxiliary tool to explore new aesthetic possibilities, blend different styles, and create novel visual effects. AI helps artists and designers automate repetitive tasks, propose different design elements and provide creative input. AI supports style transfer, which is to apply a style of image

How Zoom Is Revolutionizing Work With Agentic AI: From Meetings To MilestonesHow Zoom Is Revolutionizing Work With Agentic AI: From Meetings To MilestonesApr 26, 2025 am 11:09 AM

Zoom, initially known for its video conferencing platform, is leading a workplace revolution with its innovative use of agentic AI. A recent conversation with Zoom's CTO, XD Huang, revealed the company's ambitious vision. Defining Agentic AI Huang d

The Existential Threat To UniversitiesThe Existential Threat To UniversitiesApr 26, 2025 am 11:08 AM

Will AI revolutionize education? This question is prompting serious reflection among educators and stakeholders. The integration of AI into education presents both opportunities and challenges. As Matthew Lynch of The Tech Edvocate notes, universit

The Prototype: American Scientists Are Looking For Jobs AbroadThe Prototype: American Scientists Are Looking For Jobs AbroadApr 26, 2025 am 11:07 AM

The development of scientific research and technology in the United States may face challenges, perhaps due to budget cuts. According to Nature, the number of American scientists applying for overseas jobs increased by 32% from January to March 2025 compared with the same period in 2024. A previous poll showed that 75% of the researchers surveyed were considering searching for jobs in Europe and Canada. Hundreds of NIH and NSF grants have been terminated in the past few months, with NIH’s new grants down by about $2.3 billion this year, a drop of nearly one-third. The leaked budget proposal shows that the Trump administration is considering sharply cutting budgets for scientific institutions, with a possible reduction of up to 50%. The turmoil in the field of basic research has also affected one of the major advantages of the United States: attracting overseas talents. 35

All About Open AI's Latest GPT 4.1 Family - Analytics VidhyaAll About Open AI's Latest GPT 4.1 Family - Analytics VidhyaApr 26, 2025 am 10:19 AM

OpenAI unveils the powerful GPT-4.1 series: a family of three advanced language models designed for real-world applications. This significant leap forward offers faster response times, enhanced comprehension, and drastically reduced costs compared t

See all articles

Hot AI Tools

Undresser.AI Undress

Undresser.AI Undress

AI-powered app for creating realistic nude photos

AI Clothes Remover

AI Clothes Remover

Online AI tool for removing clothes from photos.

Undress AI Tool

Undress AI Tool

Undress images for free

Clothoff.io

Clothoff.io

AI clothes remover

Video Face Swap

Video Face Swap

Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Tools

SublimeText3 Mac version

SublimeText3 Mac version

God-level code editing software (SublimeText3)

MinGW - Minimalist GNU for Windows

MinGW - Minimalist GNU for Windows

This project is in the process of being migrated to osdn.net/projects/mingw, you can continue to follow us there. MinGW: A native Windows port of the GNU Compiler Collection (GCC), freely distributable import libraries and header files for building native Windows applications; includes extensions to the MSVC runtime to support C99 functionality. All MinGW software can run on 64-bit Windows platforms.

PhpStorm Mac version

PhpStorm Mac version

The latest (2018.2.1) professional PHP integrated development tool

SublimeText3 Chinese version

SublimeText3 Chinese version

Chinese version, very easy to use

Notepad++7.3.1

Notepad++7.3.1

Easy-to-use and free code editor