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arXiv:2610.28051v1 Announce Type: cross Abstract: Understanding how supervision signals shape semantic visual representations remains challenging, particularly in fine-grained, low-data settings. Sta...
arXiv:2610.32442v1 Announce Type: new Abstract: Fine-grained visual classification (FGVC) requires distinguishing between highly similar categories by focusing on subtle, localized visual differences...
arXiv:2607. 26107v1 Announce Type: cross Abstract: Dense vision-language understanding, including object localization, region recognition, and open-vocabulary semantic segmentation, requires associating language concepts with spatially grounded visual regions.
arXiv:2610.32220v1 Announce Type: new Abstract: Autoregressive vision-language models (VLMs) struggle to ground generated text in the correct image regions, and existing explainability methods, such...
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.
CARE introduces a constrained attention refinement framework that uses teacher‑student distillation to improve fine‑grained visual classification while maintaining interpretability. The method keeps the final prediction and explanation within a class‑specific attention student, while a training‑only auxiliary teacher reads selected intermediate DINOv2 layers with learnable queries to fuse multi‑level representations and transfer class‑discriminative knowledge. Additional diversity and sparsity regularizers refine the student’s attention heads, reducing redundancy and encouraging compact trait localization, achieving 78.5% Top‑1 accuracy on CUB and demonstrating faithful explanations via insertion and deletion metrics.