Tags → #vision

We introduce EgoLife, a project to develop an egocentric life assistant that accompanies and enhances personal efficiency through AI-powered wearable glasses 👓. To lay the foundation for this assistant, we conducted a comprehensive data collection study where six participants lived together for one week, continuously recording their daily activities—including discussions 💬, shopping 🛍️, cooking 🍳, socializing 👥, and entertainment 🎮 - using AI glasses for multimodal egocentric video capture, along with synchronized third-person-view video references. This effort resulted in the EgoLife Dataset 📖, a comprehensive 300-hour egocentric, interpersonal, multiview, and multimodal daily life dataset with intensive annotation. Leveraging this dataset, we introduce EgoLifeQA❓, a suite of 3K long-context, life-oriented question-answering tasks designed to provide meaningful assistance in daily life by addressing practical questions such as recalling past relevant events, monitoring health habits, and offering personalized recommendations.

To address the key technical challenges of 1) developing robust visual-audio models for egocentric data, 2) enabling identity recognition, and 3) facilitating long-context question answering over extensive temporal information, we introduce EgoBulter 🫡, an integrated system comprising EgoGPT 🧠 and EgoRAG 🔍. EgoGPT is a vision-language model trained on egocentric datasets, achieving state-of-the-art performance on egocentric video understanding. EgoRAG is a retrieval-based component that supports answering ultra-long-context questions. Our experimental studies verify their working mechanisms and reveal critical factors and bottlenecks, guiding future improvements. By releasing our datasets, models, and benchmarks, we aim to stimulate further research in egocentric AI assistants.

Humans acquire knowledge through three cognitive stages: perceiving information, comprehending knowledge, and adapting knowledge to solve novel problems. Videos serve as an effective medium for this learning process, facilitating a progression through these cognitive stages. However, existing video benchmarks fail to systematically evaluate the knowledge acquisition capabilities in Large Multimodal Models (LMMs).

To address this gap, we introduce Video-MMMU, a multi-modal, multi-disciplinary benchmark designed to assess LMMs’ ability to acquire and utilize knowledge from videos. Video-MMMU features a curated collection of 300 expert-level videos and 900 human-annotated questions across six disciplines, evaluating knowledge acquisition through stage-aligned question-answer pairs: Perception, Comprehension, and Adaptation. A proposed knowledge gain metric, Δ_knowledge, quantifies improvement in performance after video viewing. Evaluation of LMMs reveals a steep decline in performance as cognitive demands increase and highlights a significant gap between human and model knowledge acquisition, underscoring the need for methods to enhance LMMs’ capability to learn and adapt from videos.

For the first time in the multimodal domain, we demonstrate that features learned by Sparse Autoencoders (SAEs) in a smaller Large Multimodal Model (LMM) can be effectively interpreted by a larger LMM. Our work introduces the use of SAEs to analyze the open-semantic features of LMMs, providing the solution for feature interpretation across various model scales.

This research is inspired by Anthropic’s remarkable work on applying SAEs to interpret features in large-scale language models. In multimdoal models, we discovered intriguing features that correlate with diverse semantics and can be leveraged to steer model behavior, enabling more precise control and understanding of LMM functionality.

The Sparse Autoencoder (SAE) is trained on LLaVA-NeXT data by integrating it into a specific layer of the model, with all other components frozen. The features learned by the SAE are subsequently interpreted through the proposed auto-explanation pipeline, which analyzes the visual features based on their activation regions.

These features can then be used to steer model’s behavior to output desire output. You can check our papers for more details.

The development of video large multimodal models (LMMs) has been hindered by the difficulty of curating large amounts of high-quality raw data from the web. To address this, we consider an alternative approach, creating a high-quality synthetic dataset specifically for video instruction-following, namely LLaVA-Video-178K. This dataset includes key tasks such as detailed captioning, open-ended question-answering (QA), and multiple-choice QA. By training on this proposed dataset, in combination with existing visual instruction tuning data, we introduce LLaVA-Video, a new video LMM. Our experiments demonstrate that LLaVA-Video achieves strong performance across various video benchmarks, highlighting the effectiveness of our dataset. We plan to release the dataset, its generation pipeline, and the model checkpoints.

Video Instruction-Following Data Synthesis

A high-quality dataset for video instruction-tuning is crucial for developing effective video-language models. We identify a key factor in building such datasets: ensuring richness and diversity in both video content and its language annotations. We perform comprehensive survey on the existing video benchmarks, covering across various public video captioning and question-answering datasets, then identify ten unique video sources that contribute to over 40 video-language benchmarks. From each source, we select videos that exhibit significant temporal dynamics. To maintain diversity in the annotations, we establish a pipeline capable of generating detailed captions for videos of any length. Additionally, we define 16 types of questions that guide GPT-4o in creating question-answer pairs to assess the perceptual and reasoning skills of the video-language models.

Video Sources

We noticed that although different video-language datasets focus on various video understanding tasks , most are sourced from ten main video sources, which offer a wide range of video data from different websites, viewpoints, and domains. The relationship between these ten selected video datasets and others is shown in figure below. We select the dynamic video from these source, we detail the video selection logic in the paper.

Automated Generation for Video Detail Description

For selected videos, we use GPT-4o to systematically describe their content. We start by sampling video frames at one frame per second (fps). However, due to the input size constraints of GPT-4o, we cannot use all sampled frames. Instead, we describe the videos sequentially, as shown in figure below. We create descriptions at three distinct levels, detailed below.

Automated Generation for Video Question Answering

In addition to detailed video descriptions, our dataset includes a variety of question-answer pairs designed for complex interactions. This setup improves the video understanding model’s ability to handle real-life queries. We refer to public video question-answering benchmarks to organize these questions into 16 specific categories, as shown in Figure 3. Given a detailed video description, we use GPT-4o to generate at most one question-answer pair for each type of question. Please refer to the paper for more details of the question types and the generation process.

Dataset Statistics

We carefully select from our collected data sources to form a balanced and comprehensive collection, resulting in a total of 178K videos and 1.3M instruction-following samples. This includes 178K captions, 960K open-ended QAs, and 196K multiple-choice QAs.

Dataset Comparison

We provide a comparison of high-quality instruction-following video-language datasets, with a focus on synthetic data created with strong AI models, as shown in Table 1.

A broad collection of dynamic videos. In terms of video sources, although LLaVA-Hound contains the largest number of videos, 44% of its video data are sourced from WebVid, where most videos are static. ShareGPT4Video includes 30% of its videos from Pexels, ,Pixabay, and Mixkit, which are aesthetically good but also mostly static. Additionally, the majority of its videos come from Panda-70M, which are short clips from longer videos, suggesting simpler plots. In contrast, we carefully select video sources that offer dynamic, untrimmed videos with complex plots, which are crucial for developing a powerful video understanding model. High frames per second. Regarding frame sampling in language annotations, the proposed dataset considers 1 FPS, while other datasets consider much lower FPS. LLaVA-Hound uniformly samples 10 frames from videos of any length. The average FPS is 0.008, which may miss some fine details. ShareGPT4Video picks key frames using CLIP based on frame uniqueness. This method might also miss subtle changes in the video because CLIP embeddings do not capture fine-grained dynamics well. Our method samples FPS=1 without using key frame selection algorithms, ensuring that detailed temporal information can be expressed in annotations with high coverage. Diverse tasks. The proposed dataset considers three common task types, including caption, free-form, and closed-form QA, while existing datasets only consider a subset. Meanwhile, the quality and number of samples in our dataset is higher.

We present LLaVA-OneVision, a family of open large multimodal models (LMMs) developed by consolidating our insights into data, models, and visual representations in the LLaVA-NeXT blog series. Our experimental results demonstrate show that LLaVA-OneVision is the first single model that can simultaneously push the performance boundaries of open LMMs in three important computer vision scenarios: single-image, multi-image and video scenarios. Importantly, the design of LLaVA-OneVision allow strong transfer learning across different modalities/scenarios, yielding new emerging capabilities. In particular, strong video understanding and cross-scenario capabilities are demosntrated through task transfer from images to videos.

We open-source the LLaVA-OneVision to facilitate future development of LMM in the community.

Training Code: Cook a SOTA model with our released training code

🤗 Checkpoints: Access pre-trained model checkpoints (0.5B, 7B, 72B)

🤗 LLaVA-OneVision Data: Explore training datasets for Single-Image and OneVision stages

Gemini has amazed the world with its capability to understand hour-long videos. However, we still lack an open-source alternative with similar capabilities. Our latest research presents an innovative solution towards long video LMM, shifting the focus from reducing visual tokens per frame to leveraging the long context capabilities of language models. Here, we present our SoTA video model, Long Video Assistant (LongVA), and our novel benchmark, Visual Needle-In-A-Haystack (V-NIAH).

Long Context Transfer We discovered and verified that the long context capability of language models can be directly transferred to the video domain in modality-aligned multi-modal models. On V-NIAH, LongVA is the only open-source model capable of accurately retrieving visual information from inputs with 2000 frames or more than 200K visual tokens.

UniRes We proposed UniRes, a unified visual encoding scheme that encodes both images and videos. In UniRes, a video is encoded the same as multiple image crops in a sequence. Leveraging the Long Context Transfer property and UniRes, LongVA exhibits superior zero-shot performance in video tasks without any video-specific training data.

SoTA Performance LongVA achieves state-of-the-art performance on the comprehensive Video-MME benchmarks among 7B models. Its performance increases with denser sampling of video frames. We also conduct careful experiments to ablate where it improvements come from.