Detection-Guided Attention Steering for Vision Language Models

ConvCue: Complementary Visual Inductive Biases for Vision-Language Models

Results show that pretrained convolutional representations, when integrated through learned adaptation and fusion, can improve the visual understanding of existing VLMs without replacing their original visual encoders.

Toward Multimodal Understanding in Low-Level Vision: The Rise of Vision-Language Models

This paper presents the first comprehensive survey of VLM applications in low-level vision, proposing a unified taxonomy that organizes existing literature into two complementary paradigms, and identifies critical open challenges, including semantic-pixel misalignment and computational efficiency.

Inductive Visual Logic for Few-Shot Out-Of-Distribution Adaptation in VLMs

Generative vision-language models (VLMs) such as Qwen-VL and LLaVA achieve strong zero-shot performance on tasks overlapping with their pretraining distribution, yet fail on specialized domains where the required discriminative features were never learned, a regime we term distant out-of-distribution (OOD). Standard ad...

Gaze-Driven Efficient Inference for Vision-Language Models

UC-VLM: Consistency-Driven Learning for AI-Generated Image Detection with Vision-Language Large Models

UC-VLM is a unified multi-stage binary-supervised framework that consistently reuses the same authenticity labels for visual adaptation and label-conditioned text generation, while leveraging automatically optimized instructions to reduce prompt sensitivity without requiring human-written rationales or hand-crafted pro...

Text Capability Loss in Vision-Language Adaptation: An Attention-Sink Diagnosis

It is found that post-pretraining QK-RMSNorm injection fails to reproduce the protection of native QK-RMSNorm, while several off-the-shelf weight-merging settings fail to recover the lost capability after VL training, highlighting the value of screening backbones with Sink Strength before VL training and narrow the int...