CIM-Embed is a framework that learns instruction‑conditioned multimodal embeddings for clustering. It uses supervised contrastive learning with same‑group positives and negatives from other groups, and handles tasks with varying group counts via heterogeneous structured batches. At inference, the model maps an object’s image, text, and instruction to an embedding, and HDBSCAN clusters these embeddings without requiring a pre‑specified cluster count. The authors introduce ProductMIC, a benchmark of 5,373 Amazon products and 449 grouping tasks, and report a mean per‑task V‑measure of 95.01, outperforming the best general‑purpose baseline.
arXiv:2511. 12449v3 Announce Type: replace-cross Abstract: Recent Multimodal Large Language Models (MLLMs) have significantly advanced e-commerce product understanding.
HMGCLIP is a unified multimodal embedding framework that uses a heterogeneous hypergraph to capture both fine‑grained and coarse‑grained product attributes. By mining structure‑aware hard negatives and aligning multi‑granular semantics at relation and hyperedge levels, it enables a dual‑granularity inference mechanism that dynamically fuses attribute evidence. Experiments on a new fine‑grained e‑commerce dataset and the public MAVE benchmark show that HMGCLIP outperforms strong multimodal encoders, MLLMs, and e‑commerce baselines.
arXiv:2607. 22919v1 Announce Type: cross Abstract: Multimodal embedding spaces in models like CLIP enable powerful capabilities such as semantic similarity retrieval and cross-modal zero-shot classification.
arXiv:2604. 00513v3 Announce Type: replace-cross Abstract: With the rapid growth of e-commerce, exploring general representations rather than task-specific ones has attracted increasing attention.
Although recent Multimodal Large Language Models (MLLMs) have advanced general product understanding, they implicitly encode product information into global embeddings, thereby limiting their ability...
The paper introduces Lens, a training‑free framework that aligns multimodal representations with the semantic perspective required by downstream tasks. Lens uses a task‑specific readout phrase to anchor the perspective and then aggregates token states after the full input, ensuring the extracted representation reflects task‑conditioned evidence integration rather than generic salient content. The method achieves a Precision@1 of 63.9 across 36 MMEB datasets, outperforming the nearest training‑free baseline by 10.2 points.
arXiv:2608.28696v1 Announce Type: new Abstract: Visual in-context learning (ICL) with multimodal large language models (MLLMs) is effective for fine-grained visual classification, but each retrieved...
RegRet is a large multimodal model framework that improves region-level retrieval by adding a Region‑Aware Encoder and a multi‑stage training pipeline featuring localized captioning and regional contrastive learning. It also introduces the REGMB benchmark, containing 225k contrastive pairs across four multimodal retrieval tasks. Experiments show RegRet surpasses strong baselines in zero‑shot settings and gains over 20% improvement on REGMB and public benchmarks while maintaining global retrieval performance.
UVU is a vision-language unified autoregressive framework that integrates visual supervision directly into the pre-training stage of multimodal large language models. By using continuous visual encoding and a large-scale iterative hierarchical clustering algorithm to build a pixel-level visual codebook, UVU enables lossless representation of visual inputs and autoregressive generation of pixel-level image tokens alongside textual tokens. This approach synergizes pixel-level visual perception with semantic-level visual understanding, allowing models to internalize visual reconstruction capabilities and improve multimodal understanding performance.
The paper introduces GradCIR, a method for training composed image retrieval (CIR) systems on graded relevance rather than binary relevance. It uses a vision‑language model to generate queries and 4‑level relevance labels, an iterative feedback loop to mine hard negatives, and a hierarchy‑aware angular objective to directly optimize graded labels. Experiments on a Walmart catalog and FashionIQ show significant NDCG improvements and the system is deployed in Walmart’s live visual‑search traffic.
arXiv:2609.37225v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) have shown strong potential for universal multimodal representation learning. However, existing methods eith...
arXiv:2608.29313v1 Announce Type: cross Abstract: CLIP-like vision-language models (VLMs) trained with contrastive objectives learn strong global image-text representations, but their Euclidean embed...