Visual Embeddings

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6 papers in the last four weeks, down 25% on the four weeks before. 0.1% of all new papers.

Jul 13Week of Sep 28

Latest papers 65

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MMCORE: MultiModal COnnection with Representation Aligned Latent Embeddings
Apr 21, 2026Zijie Li, Yichun Shi, Jingxiang Sun +8Multi-Image Editing Benchmarks Visual Embeddings
ByteDance Seed
Subject-Aware Multi-Granularity Alignment for Zero-Shot EEG-to-Image Retrieval
Apr 20, 2026Lin Jiang, Qingshan She, Jiale Xu +3Zero-Shot Composed Image Retrieval Visual Embeddings
School of Automation, Hangzhou Dianzi University, and also with Zhejiang Provincial Key Laboratory of Brain Computer Collaborative Intelligence Technology and Applications, Hangzhou, Zhejiang 310018, China
CrossFlowDG: Bridging the Modality Gap with Cross-modal Flow Matching for Domain Generalization
Apr 18, 2026Antonios Kritikos, Nikolaos Spanos, Athanasios VoulodimosContrastive Alignment Visual Embeddings
National Technical University of Athens
Project and Mix: Task-Semantic Prototypes for Few-Shot Image Classification
Mar 25, 2026Dipam Goswami, Simone Magistri, Gido M. van de Ven +4Few-Shot Learning Learnable Prototypes
Department of Computer Science, Universitat Autònoma de Barcelona, Spain · Computer Vision Center, Barcelona, Spain · Media Integration and Communication Center, University of Florence, Italy +3
TeD-Loc: Text Distillation for Weakly Supervised Object Localization
Jan 22, 2025Shakeeb Murtaza, Soufiane Belharbi, Alexis Guichemerre +2Object Localization Vision-Language Model Grounding
LIVIA, ILLS, Dept. of Systems Engineering, ETS Montreal, Canada

Apr 21, 2026, cs.CVJ/K move · Enter open · S save

MMCORE: MultiModal COnnection with Representation Aligned Latent Embeddings

Zijie Li, Yichun Shi, Jingxiang Sun, Ye Wang+7

ByteDance Seed

Multi-Image Editing Benchmarks Visual Embeddings

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We present MMCORE, a unified framework designed for multimodal image generation and editing. MMCORE leverages a pre-trained Vision-Language Model (VLM) to predict semantic visual embeddings via learnable query tokens, which subsequently serve as conditioning signals for a diffusion model. This streamlined design effectively transfers the rich understanding and reasoning capabilities of VLMs into the visual generation process. By obviating the need for deep fusion between autoregressive and diffusion models or training from scratch, MMCORE significantly reduces computational overhead while maintaining high-fidelity synthesis. MMCORE seamlessly integrates text-to-image synthesis with interleaved image generation, demonstrating robust multimodal comprehension in complex scenarios such as spatial reasoning and visual grounding. Comprehensive evaluations indicate that MMCORE consistently outperforms state-of-the-art baselines across a broad spectrum of text-to-image and single/multi-image editing benchmarks.

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Related topics

Latest papers 65

MMCORE: MultiModal COnnection with Representation Aligned Latent Embeddings

Subject-Aware Multi-Granularity Alignment for Zero-Shot EEG-to-Image Retrieval

CrossFlowDG: Bridging the Modality Gap with Cross-modal Flow Matching for Domain Generalization

Project and Mix: Task-Semantic Prototypes for Few-Shot Image Classification

TeD-Loc: Text Distillation for Weakly Supervised Object Localization