Progress in multimodal intelligence depend on processing and understanding images and videos. Images can reveal static scenes by providing information about details such as objects, text, and spatial relationships. However, this comes at the cost of being extremely challenging. Video understanding involves tracking changes over time, among other operations, while ensuring consistency across frames, which requires dynamic content management and temporal relationships. These tasks become more difficult because collecting and annotating video-text datasets is relatively difficult compared to caption datasets.
Traditional methods for multimodal large language models (MLLMs) faces challenges in video comprehension. Approaches such as sparsely sampled frames, basic connectors, and image-based encoders fail to effectively capture temporal dependencies and dynamic content. Techniques such as token compression and extended context windows struggle with long video complexity, while integration of audio and visual input often lacks seamless interaction. Efforts in real-time processing and scaling model sizes remain inefficient, and existing architectures are not optimized to handle long video tasks.
To address video comprehension challenges, researchers have from Alibaba Group suggested VideoLLaMA3 frames. This framework incorporates Vision Tokenization (AVT) in any resolution and Differential Frame Cutter (DiffFP). AVT improves upon traditional fixed-resolution tokenization by enabling vision encoders to dynamically process variable resolutions, reducing information loss. This is achieved by adapting ViT-based encoders with 2D-RoPE for flexible position embedding. To preserve vital information, DiffFP handles redundant and long video tokens by cropping frames with minimal differences as taken through a 1-norm spacing between patches. Dynamic resolution handling in combination with efficient token reduction improves representation while reducing cost.
The model consists of a vision encoder, video compressor, projector, and large language model (LLM)initializing the vision encoder using a pretrained SigLIP model. It extracts visual tokens while the video compressor reduces the video token representation. The projector connects the vision encoder to LLM, and Qwen2.5 models are used for LLM. Training takes place in four stages: Vision Encoder Adaptation, Vision-Language Alignment, Multi-task Fine-tuning and Video-centric Fine-tuning. The first three stages focus on image understanding, and the final stage improves video understanding by incorporating temporal information. The Vision Encoder Adaptation Stage focuses on fine-tuning the vision encoder, initialized with SigLIP, on a large-scale image dataset so that it can process images at different resolutions. The The sight-language adjustment stage introduces multimodal knowledge, making the LLM and the vision coder trainable to integrate vision and language understanding. In it Multi-task Fine-tuning Stageinstruction fine-tuning is performed using multimodal question-answering data, including image and video questions, improving the model’s ability to follow natural language instructions and process temporal information. The Video-centric Fine-tuning Stage frees all parameters to improve the model’s video understanding. The training data comes from various sources such as scene images, documents, diagrams, fine-grained images and video data, ensuring a comprehensive multimodal understanding.
Researchers conducted experiments to evaluate the performance of VideoLLaMA3 across image and video tasks. For image-based tasks, the model was tested on document comprehension, mathematical reasoning and multi-image comprehension, where it outperformed previous models and showed improvements in diagram comprehension and real-world knowledge. question answering (QA). In video-based tasks, VideoLLaMA3 performed strongly in benchmarks such as VideoMME and MVBenchdemonstrate proficiency in general video comprehension, long video comprehension and temporal reasoning. The 2B and 7B models performed very competitively, with 7B model-leading in most video tasks, which underlines the model’s effectiveness in multimodal tasks. Other areas where important improvements were reported were OCR, mathematical reasoning, multi-image understanding and long-term video understanding.
Finally, the proposed framework promotes vision-centric multimodal models that offer a strong framework for understanding images and videos. Using high-quality image-text datasets, it addresses video understanding challenges and temporal dynamics, achieving strong results across benchmarks. However, there are still challenges such as video-text dataset quality and real-time processing. Future research can improve video-text datasets, optimize for real-time performance, and integrate additional modalities such as audio and speech. This work can serve as a starting point for future advances in multimodal understanding, improving efficiency, generalization, and integration.
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Divyesh is a consulting intern at Marktechpost. He is pursuing a BTech in Agricultural and Food Engineering from the Indian Institute of Technology, Kharagpur. He is a Data Science and Machine learning enthusiast who wants to integrate these leading technologies in the agriculture domain and solve challenges.
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