# Pseudo-labeling

Tencent Hunyuan released Hy ASR 3.0, a large-language-model-based speech recognition system covering Mandarin Chinese, English, Cantonese and 12 other Chinese dialect groups, with external context supplied as hotword lists, question-answer dialogues or summaries. On public Mandarin, English and Cantonese benchmarks it reaches aggregate error rates of 4.64%, 5.22% and 5.06%, each below the evaluated non-Hy systems; the Cantonese-only aggregate is about 11.0% lower than Qwen3 ASR Flash. The system also uses information inside the same utterance to disambiguate homophonic or phonetically similar keywords without a separate context input.

Tencent Hunyuan released Hy ASR 3.0, a large-language-model-based speech recognition system covering Mandarin Chinese, English, Cantonese and 12 other Chinese dialect groups, with external context supplied as hotword lists, question-answer dialogues or summaries. On public Mandarin, English and Cantonese benchmarks it reaches aggregate error rates of 4.64%, 5.22% and 5.06%, each below the evaluated non-Hy systems; the Cantonese-only aggregate is about 11.0% lower than Qwen3 ASR Flash. The system also uses information inside the same utterance to disambiguate homophonic or phonetically similar keywords without a separate context input.

Human annotation on the target dataset is replaced by an automatic pipeline: SAM produces segmentation proposals, CLIP pre-filters and matches labels, and a large vision-language model labels only cluster medoid images, after which an open-set detector is trained on those pseudo-labels and refined iteratively. Under zero-shot settings on COCO val2017 and LVIS minival, the YOLO-backbone variant reaches 60.8 AP and 47.4 AP, while the Swin-T variant reaches 57.2 AP and 46.2 AP, indicating the gain is not only from the detector backbone.

Human annotation on the target dataset is replaced by an automatic pipeline: SAM produces segmentation proposals, CLIP pre-filters and matches labels, and a large vision-language model labels only cluster medoid images, after which an open-set detector is trained on those pseudo-labels and refined iteratively. Under zero-shot settings on COCO val2017 and LVIS minival, the YOLO-backbone variant reaches 60.8 AP and 47.4 AP, while the Swin-T variant reaches 57.2 AP and 46.2 AP, indicating the gain is not only from the detector backbone.

Across four abdominal CT datasets (WORD, AMOS, CT-1K, AbdomenAtlas), the authors generated pseudo-label variants with seven models (nnU-Net, MedSAM, TotalSegmentator, and four STU-Net sizes), then trained DynUNet under identical deterministic settings for in-domain training and for pretraining followed by fine-tuning; in-domain performance rose strongly and non-linearly with label quality and dataset volume partly compensated for quality, whereas fine-tuned models significantly outperformed a no-pretrain baseline in the vast majority of settings and pretraining label quality no longer clearly affected downstream results.

Across four abdominal CT datasets (WORD, AMOS, CT-1K, AbdomenAtlas), the authors generated pseudo-label variants with seven models (nnU-Net, MedSAM, TotalSegmentator, and four STU-Net sizes), then trained DynUNet under identical deterministic settings for in-domain training and for pretraining followed by fine-tuning; in-domain performance rose strongly and non-linearly with label quality and dataset volume partly compensated for quality, whereas fine-tuned models significantly outperformed a no-pretrain baseline in the vast majority of settings and pretraining label quality no longer clearly affected downstream results.

The work proposes PC-Seg (Progressive Cross-view Segmentation), a five-stage curriculum learning framework in which a single 2D model first learns cross-view consistency between standard B-scans and orthogonal slices to generate reliable volumetric pseudo-labels, which are then distilled into a 3D model and followed by 2D/3D co-training with ensemble pseudo-labeling; on the public MSHC and Duke DME OCT datasets it reaches segmentation accuracy comparable to fully supervised learning using only about 0.7% of the labeled data, outperforming the semi-supervised and retinal layer segmentation methods it compares against.

The work proposes PC-Seg (Progressive Cross-view Segmentation), a five-stage curriculum learning framework in which a single 2D model first learns cross-view consistency between standard B-scans and orthogonal slices to generate reliable volumetric pseudo-labels, which are then distilled into a 3D model and followed by 2D/3D co-training with ensemble pseudo-labeling; on the public MSHC and Duke DME OCT datasets it reaches segmentation accuracy comparable to fully supervised learning using only about 0.7% of the labeled data, outperforming the semi-supervised and retinal layer segmentation methods it compares against.