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In multimodal tasks, visual language models (VLMs) play a crucial role, such as in image retrieval, image captioning, and medical diagnosis. These models aim to align visual data with language data for more efficient information processing. However, current VLMs still face significant challenges in understanding negation. Negation is critical in many applications, such as distinguishing between "a room without windows" and "a room with windows." Despite significant progress made by VLMs, existing models fall short when it comes to handling negated statements.

In today's technology landscape, CLIP (Contrastive Language-Image Pre-training) is an important multimodal foundational model. It combines visual signals and text signals into a shared feature space using contrastive learning loss on a large-scale dataset of image-text pairs. As a retriever, CLIP supports various tasks such as zero-shot classification, detection, segmentation, and image-text retrieval. Meanwhile, as a feature extractor, it performs well in nearly all...

Recently, an open source multimodal AI model named Molmo has drawn widespread attention in the industry. This AI system, based on Qwen2-72B and leveraging OpenAI's CLIP as the visual processing engine, is challenging the dominance of traditional commercial models with its outstanding performance and innovative features. Molmo's standout characteristic is its efficient performance. Despite its relatively small size, it can compete with competitors that are ten times larger in processing capability. This 'small but exquisite' design philosophy not only enhances the model's

Aishi Technology has launched PixVerse V2, an AI video generation tool designed to unleash user creativity. Utilizing a Diffusion+Transformer architecture with a spatiotemporal attention mechanism, it accurately understands and generates multimodal information, supporting efficient training and optimization. Key features include enhanced spatial and temporal perception, precise text alignment, and the ability to produce smooth, coherent video seg....

DeepSeek has open-sourced the RWKV-CLIP model, a visual-language representation learner that combines the strengths of both Transformer and RNN architectures. The model has significantly enhanced performance on vision and language tasks by pre-training on image-text pairs sourced from web data, expanding its dataset with information obtained from various websites.To address the issue of noisy data and improve data quality, the research team introduced a diverse description generation framework.