arXiv:2606. 15786v1 Announce Type: cross Abstract: The advent of large pretrained foundation models for computer vision has significantly improved the efficiency of visual data interpretation.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.
arXiv:2507. 09562v2 Announce Type: replace-cross Abstract: The Segment Anything Model (SAM) has transformed image segmentation by introducing a prompt-based paradigm that enables strong zero-shot generalization.
Open-vocabulary remote sensing segmentation has recently emerged as a promising paradigm that enables pixel-level recognition of arbitrary categories specified by natural language, including classes unseen during training. However, geospatial domain shifts caused by heterogeneous regions, spatial resolutions, and acquisition platforms weaken visual-text matching and limit cross-dataset generalization.
Adapting CLIP for zero-shot sketch-based image retrieval (ZS-SBIR) via prompt learning faces a fundamental tension: the model must bridge the sketch-photo domain gap through task-specific adaptation, yet the added flexibility risks overfitting to seen training categories and eroding CLIP's zero-shot generalization. We present SeCo-SBIR, a semantically consistent prompt learning framework that resolves this tension from both sides.
arXiv:2507. 02288v2 Announce Type: replace-cross Abstract: Domain Generalization (DG) seeks to develop a versatile model capable of performing effectively on unseen target domains.
arXiv:2607. 28967v1 Announce Type: cross Abstract: Prompt tuning adapts vision--language models with few trainable parameters, but existing approaches trade off efficiency and adaptation: static textual prompts can overfit source classes, image-conditioned prompts add per-instance computation, and multimodal tuning modifies the visual branch.
arXiv:2607. 02269v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) have demonstrated immense promise in Spatio-Temporal Video Grounding (STVG).