arXiv Computer Vision
STA-VPT: SpatioTemporally Aligned Visual Prompt Tuning
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arXiv Computer Vision
Aug 24
ES-VP : Energy-Shaped Dynamic Visual Prompting for Efficient Model Adaptation
ES‑VP introduces Energy‑Shaped Visual Prompting, a method that generates image‑specific prompts through low‑rank initialization and energy‑guided dynamic adaptation. It achieves higher performance than existing single‑prompt and diverse‑prompt approaches while using far fewer parameters. Experiments on five architectures and fifteen datasets show consistent superiority, including a 2.6% accuracy gain over DAM‑VP on CLIP with 590× fewer prompt parameters.
arXiv Computer Vision
4d ago
Imagine Before You Draw: Visual Prompt Engineering for Image Generation
arXiv:2606.04457v2 Announce Type: replace Abstract: Incorporating visual semantic representations as an intermediate step before image generation can reduce the modeling difficulty between text and i...
arXiv Computation and Language
Sep 1
ReVA: A Region-Aware Visual Assistant for Visually Grounded Question Answering
arXiv:2608.28707v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have achieved remarkable progress in Visual Question Answering (VQA), yet they continue to struggle with quest...
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 Machine Learning
Sep 11
Your Model Already Knows Don't Teach It, Learn to Ask It: Soft Prompting for Few-Shot Adaptation of Vision-Language Models
The paper explores soft prompting for few‑shot object detection with vision‑language models, showing that optimizing a small number of continuous prompt tokens—especially when placed at the cross‑modal boundary and initialized from an empty space token—can match LoRA performance while training far fewer parameters. Soft prompting also avoids catastrophic forgetting, transfers to newer models, and can be verbalized into readable prompts. The study extends these findings to manipulation tasks, indicating that VLMs already contain much of the necessary knowledge for specialized domains, and the main challenge is learning how to ask for it.
arXiv AI
Jun 16
Token Reduction Should Go Beyond Efficiency in Generative Models -- From Vision, Language to Multimodality
arXiv:2505. 18227v4 Announce Type: replace-cross Abstract: In Transformer architectures, tokens\textemdash discrete units derived from raw data\textemdash are formed by segmenting inputs into fixed-length chunks.