Text-guided Visual Prompt DINO for Generic Segmentation

• URL: http://arxiv.org/abs/2508.06146v1
• Date: Fri, 08 Aug 2025 09:09:30 GMT
• Title: Text-guided Visual Prompt DINO for Generic Segmentation
• Authors: Yuchen Guan, Chong Sun, Canmiao Fu, Zhipeng Huang, Chun Yuan, Chen Li,
• Abstract summary: We propose Prompt-DINO, a text-guided visual Prompt DINO framework.<n>First, we introduce an early fusion mechanism that unifies text/visual prompts and backbone features.<n>Second, we design order-aligned query selection for DETR-based architectures.<n>Third, we develop a generative data engine powered by the Recognize Anything via Prompting (RAP) model.
• Score: 31.33676182634522
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advancements in multimodal vision models have highlighted limitations in late-stage feature fusion and suboptimal query selection for hybrid prompts open-world segmentation, alongside constraints from caption-derived vocabularies. To address these challenges, we propose Prompt-DINO, a text-guided visual Prompt DINO framework featuring three key innovations. First, we introduce an early fusion mechanism that unifies text/visual prompts and backbone features at the initial encoding stage, enabling deeper cross-modal interactions to resolve semantic ambiguities. Second, we design order-aligned query selection for DETR-based architectures, explicitly optimizing the structural alignment between text and visual queries during decoding to enhance semantic-spatial consistency. Third, we develop a generative data engine powered by the Recognize Anything via Prompting (RAP) model, which synthesizes 0.5B diverse training instances through a dual-path cross-verification pipeline, reducing label noise by 80.5% compared to conventional approaches. Extensive experiments demonstrate that Prompt-DINO achieves state-of-the-art performance on open-world detection benchmarks while significantly expanding semantic coverage beyond fixed-vocabulary constraints. Our work establishes a new paradigm for scalable multimodal detection and data generation in open-world scenarios. Data&Code are available at https://github.com/WeChatCV/WeVisionOne.

Related papers

• Vision-Free Retrieval: Rethinking Multimodal Search with Textual Scene Descriptions [81.33113485830711]
  We introduce a vision-free, single-encoder retrieval pipeline for vision-language models.<n>We migrate to a text-to-text paradigm with the assistance of VLLM-generated structured image descriptions.<n>Our approach achieves state-of-the-art zero-shot performance on multiple retrieval and compositionality benchmarks.
  arXiv  Detail & Related papers  (2025-09-23T16:22:27Z)
• Prototype-Aware Multimodal Alignment for Open-Vocabulary Visual Grounding [11.244257545057508]
  Prototype-Aware Multimodal Learning (PAML) is an innovative framework that addresses imperfect alignment between visual and linguistic modalities, insufficient cross-modal feature fusion, and ineffective utilization of semantic prototype information.<n>Our framework shows competitive performance in standard scene while achieving state-of-the-art results in open-vocabulary scene.
  arXiv  Detail & Related papers  (2025-09-08T02:27:10Z)
• AttriPrompt: Dynamic Prompt Composition Learning for CLIP [41.37140060183439]
  AttriPrompt is a novel framework that enhances and refines textual semantic representations.<n>We introduce a Self-Regularization mechanism by applying explicit regularization constraints between the prompted and non-prompted text features.<n>Experiments demonstrate AttriPrompt's superiority over state-of-the-art methods, achieving up to 7.37% improvement in the base-to-novel setting.
  arXiv  Detail & Related papers  (2025-09-07T07:07:59Z)
• Constrained Prompt Enhancement for Improving Zero-Shot Generalization of Vision-Language Models [57.357091028792325]
  Vision-language models (VLMs) pre-trained on web-scale data exhibit promising zero-shot generalization but often suffer from semantic misalignment.<n>We propose a novel constrained prompt enhancement (CPE) method to improve visual-textual alignment.<n>Our approach consists of two key components: Topology-Guided Synonymous Semantic Generation (TGSSG) and Category-Agnostic Discriminative Region Selection (CADRS)
  arXiv  Detail & Related papers  (2025-08-24T15:45:22Z)
• GAID: Frame-Level Gated Audio-Visual Integration with Directional Perturbation for Text-Video Retrieval [12.483734449829235]
  GAID is a framework that integrates audio and visual features under textual guidance.<n>DASP injects structure-aware perturbations into text embeddings, enhancing robustness and discrimination without incurring multi-pass inference.<n>Experiments on MSR-VTT, DiDeMo, LSMDC, and VATEX show consistent state-of-the-art results with notable efficiency gains.
  arXiv  Detail & Related papers  (2025-08-03T10:44:24Z)
• DFR: A Decompose-Fuse-Reconstruct Framework for Multi-Modal Few-Shot Segmentation [21.536784761515968]
  DFR (Decompose, Fuse and Reconstruct) is a novel framework that addresses the challenge of effectively utilizing multi-modal guidance in few-shot segmentation.
  arXiv  Detail & Related papers  (2025-07-22T16:21:32Z)
• OmniParser V2: Structured-Points-of-Thought for Unified Visual Text Parsing and Its Generality to Multimodal Large Language Models [58.45517851437422]
  Visually-situated text parsing (VsTP) has recently seen notable advancements, driven by the growing demand for automated document understanding.<n>Existing solutions often rely on task-specific architectures and objectives for individual tasks.<n>In this paper, we introduce Omni V2, a universal model that unifies VsTP typical tasks, including text spotting, key information extraction, table recognition, and layout analysis.
  arXiv  Detail & Related papers  (2025-02-22T09:32:01Z)
• SEAL: Speech Embedding Alignment Learning for Speech Large Language Model with Retrieval-Augmented Generation [10.828717295018123]
  We propose a unified embedding framework that eliminates the need for intermediate text representations.<n>Our model reduces pipeline latency by 50% while achieving higher retrieval accuracy compared to traditional two-stage methods.
  arXiv  Detail & Related papers  (2025-01-26T15:04:02Z)
• More Pictures Say More: Visual Intersection Network for Open Set Object Detection [4.206612461069489]
  We introduce a strong DETR-based model, Visual Intersection Network for Open Set Object Detection (VINO) VINO constructs a multi-image visual bank to preserve the semantic intersections of each category across all time steps. Our approach guarantees a more precise alignment between target category semantics and region semantics, while significantly reducing pre-training time and resource demands.
  arXiv  Detail & Related papers  (2024-08-26T05:52:35Z)
• Harmonizing Visual Text Comprehension and Generation [31.605599298507293]
  We present TextHarmony, a unified and versatile multimodal generative model proficient in comprehending and generating visual text. We propose Slide-LoRA, which aggregates modality-specific and modality-agnostic LoRA experts, partially decoupling the multimodal generation space. Comprehensive experiments across various benchmarks demonstrate the effectiveness of the proposed approach.
  arXiv  Detail & Related papers  (2024-07-23T10:11:56Z)
• CART: A Generative Cross-Modal Retrieval Framework with Coarse-To-Fine Semantic Modeling [53.97609687516371]
  Cross-modal retrieval aims to search for instances, which are semantically related to the query through the interaction of different modal data.<n>Traditional solutions utilize a single-tower or dual-tower framework to explicitly compute the score between queries and candidates.<n>We propose a generative cross-modal retrieval framework (CART) based on coarse-to-fine semantic modeling.
  arXiv  Detail & Related papers  (2024-06-25T12:47:04Z)
• ESTextSpotter: Towards Better Scene Text Spotting with Explicit Synergy in Transformer [88.61312640540902]
  We introduce Explicit Synergy-based Text Spotting Transformer framework (ESTextSpotter) Our model achieves explicit synergy by modeling discriminative and interactive features for text detection and recognition within a single decoder. Experimental results demonstrate that our model significantly outperforms previous state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-08-20T03:22:23Z)
• TextFormer: A Query-based End-to-End Text Spotter with Mixed Supervision [61.186488081379]
  We propose TextFormer, a query-based end-to-end text spotter with Transformer architecture. TextFormer builds upon an image encoder and a text decoder to learn a joint semantic understanding for multi-task modeling. It allows for mutual training and optimization of classification, segmentation, and recognition branches, resulting in deeper feature sharing.
  arXiv  Detail & Related papers  (2023-06-06T03:37:41Z)
• VX2TEXT: End-to-End Learning of Video-Based Text Generation From Multimodal Inputs [103.99315770490163]
  We present a framework for text generation from multimodal inputs consisting of video plus text, speech, or audio. Experiments demonstrate that our approach based on a single architecture outperforms the state-of-the-art on three video-based text-generation tasks.
  arXiv  Detail & Related papers  (2021-01-28T15:22:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.