arXiv AI

Spanning the Visual Analogy Space with a Weight Basis of LoRAs

arXiv:2602. 15727v2 Announce Type: replace-cross Abstract: Visual analogy learning enables image editing via demonstration rather than textual description, allowing users to specify complex transformations difficult to articulate in words.

arXiv Computer Vision

Sep 1

Enhancing Self-Supervised Visual Representation Learning via Low-Rank Adapted LLMs

arXiv:2507.00754v3 Announce Type: replace Abstract: The integration of Large Language Model (LLMs) blocks with Vision Transformers (ViTs) holds immense promise for vision-only tasks by leveraging the...

By Selim Kuzucu, Muhammad Ferjad Naeem, Anna Kukleva, Federico Tombari, Bernt Schiele

llms fine-tuning

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.

By Joseph Fioresi, Fabian Caba Heilbron, Pankaj Nathani, Mubarak Shah, Kushal Kafle

rag diffusion multimodal benchmarks

arXiv AI

Jun 6

Image Generators are Generalist Vision Learners

arXiv:2604. 20329v3 Announce Type: replace-cross Abstract: Recent works show that image and video generators exhibit zero-shot visual understanding behaviors, in a way reminiscent of how LLMs develop emergent capabilities of language understanding and reasoning from generative pretraining.

By Valentin Gabeur, Shangbang Long, Songyou Peng, Paul Voigtlaender, Shuyang Sun, Yanan Bao, Karen Truong, Zhicheng Wang, Wenlei Zhou, Jonathan T. Barron, Kyle Genova, Nithish Kannen, Sherry Ben, Yandong Li, Mandy Guo, Suhas Yogin, Yiming Gu, Huizhong Chen, Oliver Wang, Saining Xie, Howard Zhou, Kaiming He, Thomas Funkhouser, Jean-Baptiste Alayrac, Radu Soricut

llms diffusion computer-vision benchmarks

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.

By Cristian Sbrolli, Toshihiko Yamasaki, Matteo Matteucci

llms multimodal benchmarks

arXiv Computer Vision

Aug 27

FlowMimic: Mask-free Visual Editing and Generation with Pixel-pair Warped Flow Field for Online Video Editing Data Generation and Modality Mimicry

arXiv:2607.18227v2 Announce Type: replace Abstract: In line with the prevailing direction of vision research, we explore the integration of both generation and editing capabilities for video and imag...

By Dingyun Zhang, Lixue Gong, Wei Liu

computer-vision fine-tuning multimodal

arXiv Computer Vision

Sep 15

How and What to Imagine? Visual Thinking in Unified Multimodal Models for Cross-View Spatial Reasoning

arXiv:2605.27310v2 Announce Type: replace Abstract: Cross-view spatial reasoning remains a weak spot for vision-language models (VLMs): they reason in language and discard the fine-grained geometry t...

By Qian Yang, Ankur Sikarwar, Huy Le, Le Zhang, Zhuan Shi, Perouz Taslakian, Aishwarya Agrawal

llms multimodal benchmarks

arXiv Machine Learning

Aug 10

Symbolic Graphics Programming with Large Language Models

arXiv:2509. 05208v2 Announce Type: replace-cross Abstract: Large language models (LLMs) excel at program synthesis, yet their ability to produce symbolic graphics programs (SGPs) that render into precise visual content remains underexplored.

By Yamei Chen, Haoquan Zhang, Yangyi Huang, Zeju Qiu, Kaipeng Zhang, Yandong Wen, Weiyang Liu

llms reinforcement-learning multimodal benchmarks

arXiv Computer Vision

Aug 24

Prompt2Effect: Training-Free Image-to-Video Model Specialization via LoRA Generation

arXiv:2606.13971v3 Announce Type: replace Abstract: While personalizing Image-to-Video (I2V) diffusion models with specific visual effects is increasingly demanded for high-end generation, current pr...

By Xiaomeng Yang, Yanyu Li, Gordon Guocheng Qian, Ivan Skorokhodov, Viacheslav Ivanov, Avalon Vinella, Xuan Zhang, Yanzhi Wang, Sergey Tulyakov, Anil Kag

diffusion fine-tuning safety

arXiv Machine Learning

Jul 2

Domain Arithmetic: One-Shot VLA Adaptation under Environmental Shifts

arXiv:2607. 00666v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models often fail to perform the same learned tasks under environmental shifts, such as changes in camera pose and shifts to a different but similar robot (e.

By Taewook Kang, Taeheon Kim, Donghyun Shin, Jonghyun Choi

robotics multimodal safety

arXiv AI

Jun 12

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers

arXiv:2606. 13289v1 Announce Type: cross Abstract: Holistic visual tokenizers are fundamental to unified multimodal models (UMMs) as they map diverse visual inputs into a unified representation space.

By Guozhen Zhang, Xuerui Qiu, Yutao Cui, Tianhui Song, Changlin Li, Junzhe Li, Tao Huang, Xiao Zhang, Yang Li, Jianbing Wu, Miles Yang, Zhao Zhong, Liefeng Bo, Limin Wang

llms rag diffusion computer-vision nlp multimodal

arXiv AI

Aug 11

UniSpace: Unified Visual Representation and Scalable Multimodal Modeling

arXiv:2608. 08676v1 Announce Type: cross Abstract: Semantic vision encoders have become a central visual interface for multimodal understanding and semantic conditioning in image generation.

By Jinbo Yan, Limeng Qiao, Jie Qin, Junyan He, Feize Wu, Guanglu Wan

llms rag diffusion multimodal

arXiv Machine Learning

Jun 25

BOFA: Bridge-Layer Orthogonal Low-Rank Fusion for CLIP-Based Class-Incremental Learning

arXiv:2511. 11421v2 Announce Type: replace-cross Abstract: Class-Incremental Learning (CIL) aims to continually learn new categories without forgetting previously acquired knowledge.

By Lan Li, Tao Hu, Da-Wei Zhou, Jia-Qi Yang, Han-Jia Ye, De-Chuan Zhan

llms multimodal benchmarks

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