arXiv AI
Know-Thy-Not: Training-Free Targeted Negation in Dual-Encoder Vision-Language Models
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv AI
Jul 28
What CLIP Knows but Cannot Say: Recovering Negation from Frozen Intermediate Features
arXiv:2607. 23271v1 Announce Type: cross Abstract: Contrastive vision-language models such as CLIP map semantically opposite phrases (e.
arXiv AI
Sep 15
Not all Negation Cues are Equal: Affixal Negations Yield Better Negation Understanding
The paper introduces NegCue, a large-scale dataset of 1.8 million samples that includes single-word, multi-word, and affixal negation cues, totaling over 200 unique forms. The authors pre-train encoder-only language models and large language models on this dataset to study how different negation types influence understanding. Experiments on five downstream benchmarks reveal that affixal negations provide the most significant performance gains, whereas single-word negations yield modest improvements, and that additional pre-training benefits both model types.
arXiv Machine Learning
Jun 30
Contrastive vision-language learning with paraphrasing and negation
arXiv:2511. 16527v2 Announce Type: replace-cross Abstract: Contrastive vision-language models continue to be the dominant approach for image-text retrieval.
arXiv Computer Vision
Sep 15
HANCLIP: A Family of Hyperbolic Angular Negation Vision Language Models
arXiv:2606.23843v2 Announce Type: replace Abstract: Vision-language models (VLMs) achieve strong cross-modal alignment but remain brittle to negation, often relying on shallow word associations rathe...
arXiv Computer Vision
Oct 9
Image Recognition with Vision and Language Embeddings of VLMs
The paper evaluates how vision-language models (VLMs) perform in image classification tasks, comparing language-guided and vision-only approaches across various dual-encoder VLMs such as SigLIP 2 and RADIOv2.5 on ImageNet-1k and its label-corrected variant. It identifies key factors influencing accuracy—including prompt design, class diversity, k‑NN neighbors, and reference set size—and demonstrates that language and vision provide complementary strengths. A simple, learning‑free fusion method based on per‑class precision is proposed to further improve classification performance.
arXiv Machine Learning
Aug 18
FuseLIP: Multimodal Embeddings via Early Fusion of Discrete Tokens
arXiv:2506. 03096v2 Announce Type: replace-cross Abstract: Contrastive language-image pre-training aligns features of text-image pairs in a common latent space via distinct encoders for each modality.