The paper proposes a sketch-based visual grounding framework that converts text embeddings into topographic sketches to enhance visualization, explainability, and information retrieval. It evaluates the method on the MS-COCO dataset, finding that while SBERT outperforms the sketch-based approach in top‑1 retrieval, the two methods achieve comparable top‑5 results, and a cascaded combination improves performance further. The study suggests that sketch-based representations could be valuable for retrieval tasks if trained directly on text.
arXiv:2610.18047v1 Announce Type: new Abstract: In this paper, our goal is to represent text embeddings as sketches for three main reasons. First, to visualize text representation and to explore thei...
arXiv:2606. 17646v1 Announce Type: cross Abstract: Saliency map visualizations explain image-based AI predictions by pointing to regions, but these are often unintuitive and semantically unclear, leaving an interpretability gap.
Visual document retrieval is a critical component of multimodal retrieval-augmented generation, aiming to identify query-relevant pages from document collections where evidence is distributed across t...
arXiv:2608.29364v1 Announce Type: new Abstract: Early-stage product design retrieval often requires more than category recognition: designers may need reference examples that match both a short seman...
arXiv:2608.15698v2 Announce Type: replace Abstract: Visual document retrieval is a critical component of multimodal retrieval-augmented generation, aiming to identify query-relevant pages from docume...
Unified multi-modal large language models (MLLMs) have achieved strong text-to-image generation quality, but still struggle with structure-aware prompt following, where object counts, spatial relations, attribute bindings, and coarse layouts must be preserved. We attribute this limitation in part to the entanglement of structural planning and appearance rendering within a single conditioning stream.
arXiv:2606. 24849v1 Announce Type: cross Abstract: Unified multi-modal large language models (MLLMs) have achieved strong text-to-image generation quality, but still struggle with structure-aware prompt following, where object counts, spatial relations, attribute bindings, and coarse layouts must be preserved.
arXiv:2604.22875v3 Announce Type: replace-cross Abstract: When answering questions about images, humans naturally point, label, and draw to explain their reasoning. In contrast, modern vision-languag...
The paper introduces ExpArt-KG, a knowledge graph tailored to the artwork domain, and a retrieval‑augmented generation framework that alternates between generating answers and retrieving relevant facts from the graph. By using a correctness judgment to guide the search, the method efficiently gathers the necessary factual information, improving the detail of image explanations while reducing external knowledge retrieval costs. Experimental results demonstrate that the approach maintains generation quality comparable to fixed‑iteration methods.
arXiv:2607. 12678v1 Announce Type: cross Abstract: Computer-Aided Design (CAD) floor plan drawings contain both graphical primitives and textual annotations, which provide complementary geometric and semantic cues for intelligent design understanding.
arXiv:2609.37230v1 Announce Type: cross Abstract: Visual distinctions are often finer than those reflected in linguistic conceptualization. Vision-language models exhibit a similar asymmetry: a disti...
Adapting CLIP for zero-shot sketch-based image retrieval (ZS-SBIR) via prompt learning faces a fundamental tension: the model must bridge the sketch-photo domain gap through task-specific adaptation, yet the added flexibility risks overfitting to seen training categories and eroding CLIP's zero-shot generalization. We present SeCo-SBIR, a semantically consistent prompt learning framework that resolves this tension from both sides.