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Computer vision

Detection, segmentation, depth and recognition research, plus the vision backbones that keep displacing the last generation.

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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-visionfine-tuningmultimodalbenchmarkssafety
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arXiv Computation and Language
Sep 22

Extracting Arguments, Not Just Classifying Them: Instruction-Tuned LLMs for Generative Component Detection

arXiv:2609.24855v1 Announce Type: cross Abstract: Argumentative component detection (ACD) is a core subtask of Argument(ation) Mining (AM) and one of its most challenging aspects, as it requires join...

By Sofiane Elguendouze (UniCA, I3S, MARIANNE), Erwan Hain (UniCA, MARIANNE), Elena Cabrio (MARIANNE, UniCA), Serena Villata (CNRS, MARIANNE)
llmscomputer-visionfine-tuningbenchmarks
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arXiv Computer Vision
Sep 22

Document Retrieval-Aware Chunking (D-RAC): Universal Retrieval-Aware Ingestion of Enterprise Documents via PDF Normalization and Multimodal Markdown Conversion

arXiv:2609.24220v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) systems over enterprise knowledge bases must ingest heterogeneous document formats -- PDFs, Word documents, presen...

By Uday Allu (AI Research Team Yellow.ai), Abhivanth Sivaprakash (AI Research Team Yellow.ai), Pratik Singh (AI Research Team Yellow.ai), Aman Manocha (AI Research Team Yellow.ai)
llmsragagentscomputer-visionmultimodalbenchmarkssafety
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arXiv Computer Vision
Sep 22

MTMed3D: A Multi-Task Transformer-Based Model for 3D Medical Imaging

MTMed3D is a multi-task Transformer-based model that jointly performs 3D detection, segmentation, and classification in medical imaging. It uses a shared Transformer encoder to produce multi-scale features, with separate CNN decoders for each task. Evaluated on BraTS 2018 and 2019, it achieves strong results, especially in detection, while reducing computational cost and inference time compared to single-task models.

By Fan Li, Arun Iyengar, Lanyu Xu
llmscomputer-visionefficiency
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arXiv Machine Learning
Sep 22

Reinforcement Learning Inspired Black-box Adversarial Attacks for Computer Vision

The paper introduces RIBA, a reinforcement‑learning inspired black‑box adversarial attack that generates perturbations for neural networks with fewer queries than existing methods. RIBA achieves a 25.4% reduction in median queries on ResNet‑18/Cifar10 and a 22.5% reduction on Vit‑B/16/ImageNet, while matching white‑box attack performance on an adversarially trained model.

By Florian Krone, Elena Hoemann, Sven Hallerbach
llmscomputer-visionreinforcement-learningsafety
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arXiv Computer Vision
Sep 22

Enabling Vision and Cross-Modal Learning for Multimodal Stroke Recurrence Prediction: An Interpretable Two-Step Framework

arXiv:2609.22271v1 Announce Type: new Abstract: Multimodal stroke recurrence prediction requires effective integration of heterogeneous clinical and imaging data, yet modality imbalance often causes...

By Christian Gapp, Elias Tappeiner, Martin Welk, Karl Fritscher, Stephanie Mangesius, Constantin Eisenschink, Philipp Deisl, Michael Knoflach, Astrid E. Grams, Elke R. Gizewski, Rainer Schubert
llmscomputer-visionfine-tuningmultimodal
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