# Visual Question Answering
Fresh Picks

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Smolvlm 500M Instruct
SmolVLM-500M, developed by Hugging Face, is a lightweight multimodal model that belongs to the SmolVLM series. Based on the Idefics3 architecture, it focuses on efficient image and text processing tasks. The model can accept image and text inputs in any order and generate text outputs, making it suitable for tasks such as image description and visual question answering. Its lightweight design allows it to operate on resource-constrained devices while maintaining strong performance in multimodal tasks. The model is licensed under the Apache 2.0 license, enabling open-source and flexible usage scenarios.
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Omagent.com
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Paligemma2 3b Pt 224
Developed by Google, PaliGemma 2 is a vision-language model that combines the capabilities of the SigLIP visual model and the Gemma 2 language model. It is capable of processing both image and text inputs to generate corresponding text outputs. This model excels in various vision-language tasks such as image description and visual question answering. Its main advantages include robust multilingual support, an efficient training architecture, and outstanding performance across diverse tasks. PaliGemma 2 was developed to tackle complex interactions between vision and language, aiding researchers and developers in achieving breakthroughs in their respective fields.
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Paligemma2 3b Pt 448
PaliGemma 2 is a vision-language model developed by Google, inheriting the capabilities of the Gemma 2 model, enabling it to handle image and text inputs to generate text outputs. The model excels in various visual language tasks such as image description and visual question answering. Its main advantages include robust multilingual support, an efficient training architecture, and extensive applicability. This model is suitable for a wide range of applications that require processing visual and textual data, such as social media content generation and intelligent customer service.
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Internvl2 5 26B MPO
InternVL2_5-26B-MPO is a multimodal large language model (MLLM) that builds upon InternVL2.5 and improves model performance through Mixed Preference Optimization (MPO). The model can handle multimodal data, including images and text, and is widely applied in scenarios such as image captioning and visual question answering. Its significance lies in its ability to understand and generate text closely related to image content, pushing the boundaries of multimodal AI. Background information on the product includes its exceptional performance in multimodal tasks and evaluation results on the OpenCompass Leaderboard. This model provides researchers and developers with a powerful tool to explore and realize the potential of multimodal AI.
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Internvl2 5 1B MPO
InternVL2_5-1B-MPO is a multimodal large language model (MLLM) built on InternVL2.5 and Mixed Preference Optimization (MPO), showcasing superior overall performance. This model integrates incrementally pre-trained InternViT with various pre-trained large language models (LLMs), including InternLM 2.5 and Qwen 2.5, utilizing a randomly initialized MLP projector. InternVL2.5-MPO retains the ‘ViT-MLP-LLM’ paradigm from InternVL 2.5 and its predecessors while introducing support for multiple images and video data. The model excels in multimodal tasks, capable of handling a variety of visual-language tasks including image captioning and visual question answering.
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Deepseek VL2 Small
DeepSeek-VL2 is a series of advanced large-scale mixture of experts (MoE) visual language models, significantly improved compared to its predecessor DeepSeek-VL. This model series demonstrates exceptional capabilities across various tasks, including visual question answering, optical character recognition, document/table/chart understanding, and visual localization. Comprising three variants: DeepSeek-VL2-Tiny, DeepSeek-VL2-Small, and DeepSeek-VL2, with 1 billion, 2.8 billion, and 4.5 billion active parameters respectively, DeepSeek-VL2 achieves competitive or state-of-the-art performance against existing dense and MoE-based open-source models, even with a similar or fewer number of active parameters.
AI Model
55.2K

Deepseek VL2
DeepSeek-VL2 is a series of large Mixture-of-Experts visual language models, showing significant improvements over its predecessor, DeepSeek-VL. This series exhibits exceptional performance in tasks such as visual question answering, optical character recognition, document/table/chart understanding, and visual localization. DeepSeek-VL2 includes three variants: DeepSeek-VL2-Tiny, DeepSeek-VL2-Small, and DeepSeek-VL2, with 1.0B, 2.8B, and 4.5B active parameters, respectively. Compared to existing open-source dense and MoE base models with similar or fewer active parameters, DeepSeek-VL2 achieves competitive or state-of-the-art performance.
AI Model
66.5K

Pixtral 12B 2409
Pixtral-12B-2409 is a multimodal model developed by the Mistral AI team, featuring a 12 billion parameter multimodal decoder and a 400 million parameter visual encoder. The model excels in multimodal tasks, supports images of varying sizes, and maintains cutting-edge performance on text benchmarks. It is suitable for advanced applications requiring the processing of image and text data, such as image description generation and visual question answering.
AI image generation
51.1K

Videollama2 7B
Developed by the DAMO-NLP-SG team, VideoLLaMA2-7B is a multimodal large language model focused on video content understanding and generation. This model demonstrates significant performance in video question answering and video captioning, capable of handling complex video content and generating accurate and natural language descriptions. It has been optimized for spatio-temporal modeling and audio understanding, providing powerful support for intelligent analysis and processing of video content.
AI video generation
72.0K

Videollama2 7B Base
VideoLLaMA2-7B-Base, developed by DAMO-NLP-SG, is a large video language model focused on understanding and generating video content. This model demonstrates exceptional performance in visual question answering and video captioning. Through advanced spatiotemporal modeling and audio understanding capabilities, it provides users with a new tool for analyzing video content. Based on the Transformer architecture, it can process multi-modal data, combining textual and visual information to generate accurate and insightful outputs.
AI video generation
75.9K

Idefics 80b
HuggingFaceM4/idefics-80b-instruct is an open-source multimodal model that can accept both image and text input and generate relevant text output. It excels in tasks like visual question answering and image description, making it a versatile intelligent assistant model. Developed by the Hugging Face team, it's trained on open datasets and is available for free use.
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Kosmos 2
Kosmos-2 is a multi-modal large language model that can associate natural language with various input forms like images and videos. It can be used for tasks such as phrase localization, referential understanding, referential expression generation, image description, and visual question answering. Kosmos-2 is trained and evaluated using the GRIT dataset, which contains a large amount of image-text pairs. Kosmos-2's strength lies in its ability to associate natural language with visual information, thereby enhancing model performance.
AI Model
54.9K

SEED
SEED is a large-scale pre-trained model that, through pre-training and guided fine-tuning on interwoven text and visual data, demonstrates outstanding performance in a wide range of multi-modal understanding and generation tasks. SEED also possesses emerging combinatorial capabilities, such as multi-turn contextual multi-modal generation, much like your AI assistant. SEED also includes SEED Tokenizer v1 and SEED Tokenizer v2, which can convert text into images.
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59.3K
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Chinese Picks

Tencent Hunyuan Image 2.0
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Fastvlm
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Liblibai
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