Active learning for object detection commonly estimates sampling uncertainty using detector confidence and localization deviation，which struggles to identify high-confidence yet semantically wrong false positives. Meanwhile，diversity sampling typically relies on global visual descriptors at the image level，making it difficult to capture semantic structures at the category/instance level and often leading to redundant samples. To address these issues，we introduce cross-modal priors so that the selection signals can be informed not only by the detector’s visual evidence but also by language-aligned semantics. The goal is to improve the reliability （robustness against semantic mismatch） and effectiveness （utility per labeled image） of active sample selection under constrained annotation budgets.

We propose semantic enhanced active learning （SEAL），a cross-modal prior-driven active learning framework that leverages the contrastive language-image pretraining （CLIP） vision-language model to enhance uncertainty and diversity criteria without any additional supervision. In uncertainty sampling，for each image，SEAL extracts CLIP features on candidate detection boxes and compares the detector’s predicted category with CLIP’s category evidence. We design a visual-semantic consistency score that jointly reflects the detector’s confidence，its localization quality，and the agreement between visual predictions and cross-modal semantics. Instances with low consistency are prioritized，yielding a more robust instance-level uncertainty measure that explicitly penalizes high-confidence semantic mismatches，which is precisely the failure mode that frustrates conventional uncertainty heuristics in diversity sampling. To reduce redundancy while preserving category coverage，SEAL aggregates CLIP embeddings of all objects within an image and builds a category-level structural representation. We then compute inter-image structural similarity in this semantic space and perform category-aligned diversity selection，encouraging batches that simultaneously cover rare or under-represented classes and span heterogeneous intraclass appearances. This process mitigates the tendency of global-feature methods to overselect visually similar images that contribute limited new information.

We conduct comprehensive experiments on MS COCO and Pascal VOC under multiple baselines and annotation budgets. Across settings，SEAL consistently outperforms strong active learning methods，yielding higher detection accuracy per labeled image. Representative results include the following： RetinaNet on Pascal VOC （20% labeled data），mAP@0.5 = 72.4%，exceeding the strongest prior method by 0.8%； RetinaNet on MS COCO （10% labeled data），AP@（0.50：0.95） = 23.9%，an improvement of 0.5% over the best baseline under the same budget； Faster R-CNN on MS COCO （40% labeled data），AP@（0.50：0.95） = 33.3%，surpassing the best competing approach by 0.22%. These gains are attributable to two complementary effects. First，the consistency-aware uncertainty ranking correctly downweights detections that are visually confident but semantically unreliable，thereby allocating labels to genuinely informative instances. Second，the category-aligned diversity explicitly controls semantic coverage and inter-image variation，reducing selection redundancy and ensuring that annotation efforts expand the breadth （more classes covered） and depth （richer intraclass variation） of the training set. In practice，we observe that SEAL improves data efficiency： for a fixed target accuracy，fewer labels are required relative to visual-only strategies，and for a fixed budget，the achieved AP is higher. The approach remains effective across different detectors and budgets，indicating good robustness and transferability. Beyond headline metrics，qualitative analyses show that SEAL preferentially selects images containing semantically ambiguous contexts （e.g.，objects with occlusion，atypical viewpoints，or confusing backgrounds）. This selection behavior accelerates learning in regions of the data distribution where detectors typically underperform. Because CLIP embodies large-scale language-vision alignment，the semantic signal complements the detector’s purely visual evidence and provides a principled handle on semantic correctness，which traditional confidence/localization surrogates cannot fully capture.

SEAL demonstrates that cross-modal priors can be harnessed to make active learning for object detection more reliable and more cost effective. By combining a visual-semantic consistency measure for uncertainty with a category-aligned structural criterion for diversity，SEAL systematically reduces semantic mismatch errors during selection and curbs redundancy，leading to higher AP under the same labeling budget and，equivalently，lower labeling cost to reach a target accuracy. Overall，our results on COCO and VOC indicate that infusing active selection with language-aligned semantics is a practical and effective path toward scalable，label-efficient detection.