arXiv Computer Vision
0.5%>100%: Bidirectional Reciprocal Learning for Referring Image Segmentation
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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.
arXiv Computer Vision
Sep 22
DeCo: Efficient Decouple-to-Couple Learning for Multi-Task Visual Grounding
DeCo introduces an efficient decouple-to-couple learning framework for multi-task visual grounding, addressing conflicts between localization and segmentation tasks. It first applies Task-aware Semantic Decoupling (TSD) to separate shared visual cues into task-specific features guided by salient words, then uses Hybrid Prior Coupling (HPC) to merge sentence-level semantic priors with mask-derived spatial priors for improved grounding. Experiments across multiple natural and remote sensing datasets show that DeCo achieves state‑of‑the‑art performance while requiring only lightweight trainable parameters on a frozen multimodal encoder.
arXiv AI
Sep 18
Lens: Bringing the Right Semantic Perspective into Focus for Training-Free Multimodal Representation Learning
The paper introduces Lens, a training‑free framework that aligns multimodal representations with the semantic perspective required by downstream tasks. Lens uses a task‑specific readout phrase to anchor the perspective and then aggregates token states after the full input, ensuring the extracted representation reflects task‑conditioned evidence integration rather than generic salient content. The method achieves a Precision@1 of 63.9 across 36 MMEB datasets, outperforming the nearest training‑free baseline by 10.2 points.
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
Aug 25
Sa2VA: Marrying SAM2 with MLLM for Dense Grounded Understanding of Images and Videos
arXiv:2501.04001v4 Announce Type: replace Abstract: This work presents Sa2VA, the first comprehensive, unified model for dense grounded understanding of both images and videos. Unlike existing multi-...
arXiv Computation and Language
Sep 22
SalQ-VLM: Fine-Grained Saliency-Guided Quantization for Vision-Language Models
arXiv:2508.03351v3 Announce Type: replace-cross Abstract: Large language models (LLMs) have demonstrated remarkable capabilities across diverse language tasks, motivating their extension to vision-la...