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Stuck on Shuffle: On Attribute-Object Binding in Vision-Language Models
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arXiv Computer Vision
6d ago
Stuck on Shuffle: On Attribute-Object Binding in Vision-Language Models
The paper introduces SABER, a benchmark that tests attribute–object binding in vision‑language models across all four modality pairings and scenes with up to ten objects. Experiments show significant binding failures in both CLIP‑style dual encoders and recent MLLM‑based embedders. The authors propose a binding‑focused fine‑tuning strategy using synthetic attribute‑binding text, which improves SABER performance and also boosts results on negation and instance‑retrieval benchmarks.
arXiv AI
Jul 28
Controlling Embedding Spaces with Text-Conditioned Transformations
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 AI
Jun 16
Beyond Scalar Distances: Semantic Attribute Gradients from Frozen MLLMs for Visual Embeddings
arXiv:2606. 15134v1 Announce Type: cross Abstract: Vision encoders for retrieval are typically trained with class-label supervision: each training pair reduces to a scalar that uniformly pushes the embedding apart or pulls it together, as if every visual attribute either differed or matched.
arXiv AI
Jun 12
Cross-Modal Masked Compositional Concept Modeling for Enhancing Visio-Linguistic Compositionality
arXiv:2606. 13288v1 Announce Type: cross Abstract: Contrastively trained vision-language models like CLIP, have made remarkable progress in learning joint image-text representations, but still face challenges in compositional understanding.
arXiv AI
Sep 28
Beyond Bag-of-Words: Diagnosing Compositional Binding Failures in Vision-Language Models
The paper introduces Auto-Comp, a fully automated, concept-driven pipeline that generates photorealistic compositional benchmarks for vision‑language models. Auto‑Comp creates paired Minimal and Contextual samples for each concept, enabling isolation of core binding abilities from visio‑linguistic complexity. Evaluations across 25 models reveal consistent failures in attribute and relational binding, with context helping relational tasks but hindering attribute tasks due to visual clutter.
arXiv AI
Oct 16
UniFGVC: Universal Training-Free Few-Shot Fine-Grained Vision Classification via Attribute-Aware Multimodal Retrieval
UniFGVC introduces a training‑free framework for few‑shot fine‑grained visual classification that reframes the task as a multimodal retrieval problem. It uses a Category‑Discriminative Visual Captioner to generate attribute‑aware textual descriptions, then matches image‑description pairs with category templates via pre‑trained encoders. Experiments on 12 datasets show that UniFGVC consistently outperforms existing CLIP‑based and fully‑supervised MLLM methods, demonstrating the effectiveness of the retrieval paradigm.