arXiv Computer Vision
ReferFlow: Beyond Final-Target Localization for Complex Referring Expressions
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arXiv AI
Jul 29
RefBench-PRO: Perceptual and Reasoning Oriented Benchmark for Referring Expression Comprehension
arXiv:2512. 06276v3 Announce Type: replace-cross Abstract: Referring Expression Comprehension (REC) is a vision-language task that localizes a specific image region based on a textual description.
arXiv Computer Vision
Oct 9
ContourVLA: A Closed-Loop Perception-Action Contour Policy for Generalized Referring Expression Segmentation
ContourVLA presents a closed‑loop vision‑language‑action framework for generalized referring expression segmentation. It uses editable contours as policy states, coupled with Evolution‑Aware Semantic Scheduling to adapt perception, and a Dustbin‑Augmented Entropic Credit Transport GRPO to jointly optimize grounding and contour actions. The method achieves significant gains in gIoU and mIoU over existing baselines on multiple RefCOCO datasets.
arXiv Computer Vision
Sep 3
Towards Open-World Referring Expression Comprehension: A Benchmark with Training-free Multi-task Consistency Checker
The paper introduces OpenRef, a benchmark for Referring Expression Comprehension (REC) designed for open‑world scenarios. OpenRef expands beyond simple settings by including diverse visual domains, variable target counts (multi‑target and none‑target), and a rich vocabulary with proper nouns, polysemous words, and ordinal terms. It also proposes new evaluation metrics—F1 for grounding accuracy and N3R for negative expression rejection—and presents a training‑free Multi‑task Consistency Checker (MCC) that improves model performance with a single click.
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...
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.
arXiv Computer Vision
Aug 25
Traj-VLN: Learning Pixel-Space Interaction via Autoregressive Trajectory Generation
arXiv:2607.10744v5 Announce Type: replace Abstract: Benefiting from the powerful priors embedded in large-scale pre-training data and the emerging commonsense reasoning ability, large language models...