arXiv AI
Aug 13
CORE-3D: Context-aware Open-vocabulary Retrieval by Embeddings in 3D
arXiv:2509. 24528v4 Announce Type: replace-cross Abstract: Object retrieval from a scene has become a new trend of research due to its numerous applications.
Text-to-seed generation: Training-free open-vocabulary seeded semantic segmentation via re-purposing diffusion as text-guided seed generator
The paper introduces Text-to-Seed (T2S), a training‑free framework for open‑vocabulary semantic segmentation that repurposes Stable Diffusion to generate attention‑based seed points from text queries. These sparse seeds serve as point prompts for the Segment Anything Model (SAM), enabling reliable region expansion without relying on inaccurate coarse masks. T2S achieves strong performance on standard OVSS benchmarks using only the text‑to‑region correspondence of diffusion models and no task‑specific training or extra annotations.
Related stories
arXiv Machine Learning
Jul 20
Rethinking the Global Knowledge of CLIP in Training-Free Open-Vocabulary Semantic Segmentation
arXiv:2502. 06818v4 Announce Type: replace Abstract: Recent works modify CLIP to perform open-vocabulary semantic segmentation in a training-free manner (TF-OVSS).
arXiv AI
Jul 31
TraceCLIP: Recovering Local Semantics from Patch-to-CLS Contributions
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 AI
Jun 2
A Lightweight Context-Driven Training-Free Network for Scene Text Segmentation and Recognition
arXiv:2503. 15639v2 Announce Type: replace-cross Abstract: Modern scene text recognition systems often depend on large end-to-end architectures that require extensive training and are prohibitively expensive for real-time scenarios.
arXiv AI
Jun 16
Sub-Semantic Image Segmentation
arXiv:2606. 14754v1 Announce Type: cross Abstract: Images can be segmented based on visual cues (i.
arXiv Computer Vision
Aug 25
ENCORE: Entropy-Guided Cropping and Attention Regularization for Robust Vision--Language Understanding
arXiv:2608.22996v1 Announce Type: new Abstract: Vision-Language Models (VLMs) perform well on diverse vision-language tasks, but transformer-based visual encoders split images into fixed-resolution s...
Hugging Face Trending Papers
Aug 24
ENCORE: Entropy-Guided Cropping and Attention Regularization for Robust Vision--Language Understanding
Vision-Language Models (VLMs) perform well on diverse vision-language tasks, but transformer-based visual encoders split images into fixed-resolution sub-images, compromising object integrity in light...
Hugging Face Trending Papers
Aug 4
UniEvo-RS: Omni-Prompt Unified Remote Sensing Segmentation with Representative Exemplar-Driven Prototype Evolution
Prompt-driven vision-language models (VLMs) hold immense promise for accelerating dense remote sensing (RS) annotation, but static models suffer from severe performance degradation when deployed on novel scenes, unseen categories, or visually confusing backgrounds. Moreover, existing unified paradigms primarily rely on intra-image specific prompts, lacking flexible task routing to adapt to multi-intent operational workflows.
arXiv AI
Jul 1
Preserve the Hard, Regenerate the Rest: Uncertainty-Guided Synthetic Training Data Augmentation with Diffusion Models
arXiv:2606. 31603v1 Announce Type: cross Abstract: Semantic segmentation models struggle with data sparsity and rare or visually diverse regions, e.
arXiv Computer Vision
5d ago
SEG-SAM: Semantic-Guided SAM for Unified Medical Image Segmentation
SEG-SAM is a unified medical image segmentation model that builds on the Segment Anything Model (SAM) by integrating semantic medical knowledge. It introduces a semantic‑aware decoder separate from SAM’s original decoder to handle both semantic segmentation of prompted objects and classification of unprompted objects. The model also incorporates key medical category characteristics from large language models via a text‑to‑vision semantic module and uses a cross‑mask spatial alignment strategy to improve overlap between predictions, achieving superior performance over existing SAM‑based and task‑specific methods.
arXiv AI
Jul 8
Rethinking Visual Autoregressive Sampling with Information-Grounding Guidance
arXiv:2509. 23876v3 Announce Type: replace-cross Abstract: Autoregressive (AR) models based on next-scale prediction have emerged as a powerful tool for image generation, but they face a critical weakness: information inconsistencies between patches across timesteps introduced by progressive resolution scaling.
arXiv Computer Vision
5d ago
ARGenSeg: Image Segmentation with Autoregressive Image Generation Model
ARGenSeg introduces an autoregressive generation-based approach for image segmentation that integrates seamlessly with multimodal large language models (MLLMs). Unlike prior methods that use boundary points or dedicated segmentation heads, ARGenSeg generates dense masks directly through visual token output and detokenization via a universal VQ‑VAE, enabling fine‑grained pixel‑level perception. The framework employs a next‑scale‑prediction strategy to parallelize token generation, resulting in faster inference while outperforming state‑of‑the‑art segmentation models on multiple datasets.