ResComEmb: Effective and Efficient Multimodal Embedding via Residual Homogeneity Compression

arXiv:2609.37225 · cs.CV, cs.AI · Submitted 2026-09-29 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-09-29

Updated: 2026-09-29

Terminology

• Benchmark — 21× in this paper · explained in 2 episodes
• Embeddings — 18× in this paper · explained in 2 episodes
• Ablation — 13× in this paper · explained in 1 episode
• Homogeneity — 12× in this paper · explained in 2 episodes
• Redundancy — 11× in this paper · explained in 2 episodes
• Residual Homogeneity Compression — 10× in this paper
• Aggregation — 8× in this paper · explained in 1 episode
• Conference on Computer Vision and Pattern Recognition — 5× in this paper
• Clustering — 4× in this paper · explained in 2 episodes
• Grounding — 4× in this paper · explained in 3 episodes
• Representation Learning — 4× in this paper · explained in 2 episodes
• Ablation Studies — 3× in this paper · explained in 1 episode
• Adaptive Weighting — 3× in this paper · explained in 1 episode
• Alignment — 3× in this paper · explained in 1 episode
• Generative AI — 3× in this paper · explained in 2 episodes
• Adam — 2× in this paper · explained in 2 episodes
• Pruning — 2× in this paper · explained in 4 episodes
• Visual Grounding — 2× in this paper · explained in 2 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Distribution Shift — 1× in this paper · explained in 5 episodes
• Domain Shift — 1× in this paper · explained in 4 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Gradient Flow — 1× in this paper · explained in 2 episodes
• Massive Multimodal Embedding Benchmark — 1× in this paper
• Matryoshka Representation Learning — 1× in this paper
• Multimodal Inputs — 1× in this paper · explained in 2 episodes
• Vector Representations — 1× in this paper · explained in 2 episodes

Sources

• Qwen2.5-VL Technical Report
• PaliGemma: A versatile 3B VLM for transfer
• Reducing the Footprint of Multi-Vector Retrieval with Minimal Performance Impact via Token Pooling
• E5-V: Universal Embeddings with Multimodal Large Language Models
• ViDoRe Benchmark V2: Raising the Bar for Visual Retrieval
• Do All Visual Tokens Matter Equally? Object-Evidence Preserving Token Merging for Vision-Language Retrieval
• ONE-PEACE: Exploring One General Representation Model Toward Unlimited Modalities
• Long-CLIP: Unlocking the Long-Text Capability of CLIP

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