arXiv Computer Vision
What is the Right Embedding Space for Contrastive Learning in Referring Expression Counting?
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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 AI
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RefBench-PRO: Perceptual and Reasoning Oriented Benchmark for Referring Expression Comprehension
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arXiv Computer Vision
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WeakMCN: Multi-task Collaborative Network for Weakly Supervised Referring Expression Comprehension and Segmentation
WeakMCN introduces a multi-task collaborative network that jointly learns weakly supervised referring expression comprehension (WREC) and segmentation (WRES) using a dual-branch architecture. The WREC branch employs anchor-based contrastive learning and serves as a teacher for the WRES branch, while two novel modules—Dynamic Visual Feature Enhancement (DVFE) and Collaborative Consistency Module (CCM)—facilitate cross-task collaboration. Experiments on RefCOCO, RefCOCO+, and RefCOCOg show significant performance gains over single-task baselines, with up to 3.91% and 13.11% improvements on WREC and WRES respectively, and strong generalization in semi-supervised settings.
arXiv Computer Vision
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RECOUNT: Reference-guided Counting with Synthetic Visual Exemplars
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arXiv Machine Learning
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Contrastive vision-language learning with paraphrasing and negation
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arXiv Computer Vision
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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.