arXiv AI

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 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.

By Kwun Ho Ngan, Saman Sadeghi Afgeh, Joe Townsend, Artur d'Avila Garcez

llms rag diffusion computer-vision multimodal

arXiv AI

Sep 10

Bigger Text Encoders Can Hurt CLIP Zero-Shot Performance

arXiv:2609.05730v1 Announce Type: cross Abstract: Contrastive Language-Image Pretraining (CLIP) is a building block of many machine learning applications. Scaling laws have guided resource allocation...

By Samir Char, Carles Domingo-Enrich, Randall Balestriero

rag multimodal safety

arXiv AI

Jun 16

Decoupling Semantics from Distortions: Multi-Scale Two-Stream Vision-Language Alignment for AI-Generated Image Quality Assessment

arXiv:2606. 16799v1 Announce Type: cross Abstract: Existing vision-language model (VLM)-based AI-generated image quality assessment (AIGIQA) methods suffer from a fundamental semantic-distortion dimensional conflict: monolithic representations optimized for semantic discrimination inherently entangle compositional understanding with low-level perceptual sensitivity, rendering them blind to fine-grained quality degradations.

By Zijie Meng

llms efficiency multimodal benchmarks safety

arXiv AI

Sep 10

TDDN: Text-aligned Diffused DINO Network for Puzzle Understanding

arXiv:2609.07937v1 Announce Type: cross Abstract: Structured visual reasoning, such as image puzzles, demands fine-grained visual perception, an ability current Vision Language Models (VLMs) lack. VL...

By Harsha Patnala, Debopriyo Banerjee, Ayush Sunil Munot, Somak Aditya

llms computer-vision nlp multimodal benchmarks safety

arXiv Computer Vision

Sep 10

UOT-Gap: A Variational Principle for the Modality Gap in Vision-Language Models via Unbalanced Optimal Transport

arXiv:2609.10224v1 Announce Type: new Abstract: Vision-language models such as CLIP embed images and text in a shared space, where modality-specific distributions often remain separated. Existing acc...

By Zonglin Yang, Huilan Ma, Xudan Zheng, Yuejun Xie

llms rag multimodal safety

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...

By Hoang-Bao Le, Aiden Durrant, Thai Son Mai, Binh T. Nguyen, Liting Zhou, Cathal Gurrin

llms rag fine-tuning multimodal benchmarks safety

arXiv AI

Aug 18

Invariant Pretraining for Robust Code Representations

arXiv:2608. 15412v1 Announce Type: cross Abstract: Encoder-based code representation models remain widely deployed for discriminative tasks such as clone detection and code classification, where their small size and low inference cost are decisive.

By Yifeng He, Yundi Xu, Christopher Castro Gaw Gonzalo, Zili Wang, Hao Chen

arXiv Computer Vision

Sep 23

0.5%>100%: Bidirectional Reciprocal Learning for Referring Image Segmentation

arXiv:2609.24510v2 Announce Type: replace Abstract: Recent advances in vision foundation models (VFMs) have shown remarkable capabilities across diverse unimodal visual tasks. However, adapting VFMs...

By Xiaoqiang Lu, Licheng Jiao, Lingling Li, Yuting Yang, Long Sun, Wenping Ma, Xu Liu, Fang Liu

computer-vision fine-tuning multimodal benchmarks safety

arXiv Computer Vision

Sep 22

HyperCLIP++: Fine-tuning CLIP forOpen-vocabulary Semantic Segmentation in Hyperbolic Space

arXiv:2609.24564v1 Announce Type: new Abstract: CLIP, a foundational vision-language model, has emerged as a powerful tool for open-vocabulary semantic segmentation. While freezing CLIP's text encode...

By Zelin Peng, Zhengqin Xu, Changsong Wen, Yu Huang, Yaoming Wang, Xiaokang Yang, Wei Shen

llms rag computer-vision fine-tuning multimodal benchmarks safety

arXiv Computation and Language

Sep 22

SalQ-VLM: Fine-Grained Saliency-Guided Quantization for Vision-Language Models

arXiv:2508.03351v3 Announce Type: replace-cross Abstract: Large language models (LLMs) have demonstrated remarkable capabilities across diverse language tasks, motivating their extension to vision-la...

By Yufei Xue, Yushi Huang, Lunjie Zhu, Jiawei Shao, Jun Zhang

llms efficiency multimodal benchmarks

arXiv AI

Sep 10

STAR-Pro: Stage-Wise Token Adaptive Reduction with Progressive Refinement for Efficient Large Vision-Language Models

arXiv:2609.05916v1 Announce Type: cross Abstract: Large vision-language models (LVLMs) achieve strong multimodal understanding, but the hundreds to thousands of visual tokens they process impose subs...

By Yichen Guo, Tinghao Wang, Qizhe Zhang, Lingbei Meng, Yuan Zhang, Jiajun Cao, Hao Jiang, Chenwei Wu, Jixian Wu, Sixiang Chen, Tao Luo, Hongyang Cheng, Kai Tang, Chenxi Li, Renyuan Li, Xiande Huang, Wenya Wang, Shanghang Zhang

llms efficiency multimodal benchmarks

Hugging Face Trending Papers

Aug 31

NeoMME: A Single-Tower Multimodal-Native Multilingual Foundation Encoder for Efficient Fine-Tuning and Inference

Multimodal models often build on architectures designed for generative vision-language modeling, typically combining separately pretrained vision encoders with causal language models. Visual document...

llms rag diffusion fine-tuning efficiency multimodal benchmarks

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