arXiv AI August 18, 2026 By Kareem Hassani, Chaymaa Abbas, Hadi Al Mubasher, Mariette Awad

RRFC: Recursive Refinement via Feedback Conditioning for Iterative Image-to-Image Generation

arXiv:2608. 15694v1 Announce Type: cross Abstract: Conditional image-to-image generators are single-shot: they map input features to an output in one forward pass and treat it as final, with no opportunity to improve on it.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Computer Vision

Sep 1

Improving Image-to-Image Translation via a Rectified Flow Reformulation

arXiv:2603.20186v2 Announce Type: replace Abstract: In this work, we propose Image-to-Image Rectified Flow Reformulation (I2I-RFR), a practical plug-in reformulation that recasts standard I2I regress...

By Satoshi Iizuka, Shun Okamoto, Kazuhiro Fukui

efficiency multimodal

arXiv AI

Jun 26

Reconstruction Alignment Improves Unified Multimodal Models

arXiv:2509. 07295v4 Announce Type: replace-cross Abstract: Unified multimodal models (UMMs) unify visual understanding and generation within a single architecture.

By Ji Xie, Trevor Darrell, Luke Zettlemoyer, XuDong Wang

rag diffusion multimodal benchmarks safety

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 AI

Jun 12

Iterative Visual Thinking: Teaching Vision-Language Models Spatial Self-Correction through Visual Feedback

arXiv:2606. 13156v1 Announce Type: cross Abstract: Vision-language models (VLMs) achieve strong singleshot spatial grounding, yet lack any mechanism to observe and correct their own predictions.

By Animesh Tripathy, Aswanth Krishnan

llms fine-tuning multimodal benchmarks

arXiv Computer Vision

Sep 22

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

The paper introduces Bidirectional Reciprocal Learning (BRL), a parameter‑efficient fine‑tuning framework for referring image segmentation that operates on frozen vision foundation models. BRL employs two lightweight adapters—Reciprocal Attention Adapter (RAA) for token‑level cross‑modal attention and Reciprocal Gate Adapter (RGA) for channel‑level gating—to enable hierarchical, bidirectional information flow between vision and language. Experiments on RefCOCO, RefCOCO+, and RefCOCOg show that BRL outperforms existing methods while updating fewer than 0.5% of backbone parameters.

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 Machine Learning

Jul 31

Explorative Modeling: Unlocking a Third Pretraining Axis and End-to-End Generation

arXiv:2607. 27372v1 Announce Type: new Abstract: The deep learning revolution, kicked off by AlexNet, taught us that end-to-end training beats decomposing a problem into hand-designed stages.

By Alexi Gladstone, Heng Ji, Yilun Du

diffusion benchmarks

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Related stories

Improving Image-to-Image Translation via a Rectified Flow Reformulation

Reconstruction Alignment Improves Unified Multimodal Models

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

Iterative Visual Thinking: Teaching Vision-Language Models Spatial Self-Correction through Visual Feedback

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

Explorative Modeling: Unlocking a Third Pretraining Axis and End-to-End Generation