YOLO-TVP: Real-time open-vocabulary object detection with Semantic-Target Soft Cross-Entropy and text-visual prompts
Open-vocabulary object detection (OVD) models generally leverage vision-language pre-trained models to recognize novel categories via arbitrary text prompts. Nevertheless, their performance is restricted by two core limitations: semantic discontinuity arising from hard binary supervision in contrastive learning, and the inflexibility of single prompts to convey complex detection intents in practical scenarios. To tackle these issues, this paper proposes YOLO-TVP (Text–Visual Prompt), an efficient OVD framework built on the YOLO architecture, with two key designs. First, a Semantic-Target Soft Cross-Entropy (ST-SoftCE) loss is introduced. It constructs semantic target distributions from inter-class similarities in the shared text embedding space for open-vocabulary inference and supervises the detector's classification branch. This design embeds semantic relevance into supervision and enhances fine-grained discrimination among semantically similar categories. Second, a unified class-prompt embedding interface is developed to support both textual and visual prompts. Text prompts are projected into the shared prompt space, while visual prompts are formed by learnable weighted fusion of CLIP semantic priors and backbone visual features, eliminating the need for multi-modal prompt concatenation during inference. Experiments validate the effectiveness: on Flickr30k image-text retrieval with ResNet-50, ST-SoftCE improves Recall@1 by 9.56% over standard cross-entropy. For open-vocabulary detection trained on COCO+Flickr30k and evaluated on LVIS, ST-SoftCE delivers a 13.15% relative mAP 50 improvement to YOLO-World. With scratch-trained detector weights and frozen vision-language encoders used only for prompt-side priors, YOLO-TVP achieves competitive prompt-conditioned detection while preserving real-time efficiency, verifying its advances in semantic alignment and prompt-adaptive detection.