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Qin Wang

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12 papers
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12

AAAI Conference 2026 Conference Paper

Self-Supervised Learning Based on Transformed Image Reconstruction for Equivariance-Coherent Feature Representation

  • Qin Wang
  • Alessio Quercia
  • Benjamin Bruns
  • Abigail Morrison
  • Hanno Scharr
  • Kai Krajsek

Self-supervised learning (SSL) methods have achieved remarkable success in learning image representations allowing invariances in them — but therefore discarding transformation information that some computer vision tasks actually require. While recent approaches attempt to address this limitation by learning equivariant features using linear operators in feature space, they impose restrictive assumptions that constrain flexibility and generalization. We introduce a weaker definition for the transformation relation between image and feature space denoted as equivariance-coherence. We propose a novel SSL auxillary task that learns equivariance-coherent representations through intermediate transformation reconstruction, which can be integrated with existing joint embedding SSL methods. Our key idea is to reconstruct images at intermediate points along transformation paths, e.g. when training on 30° rotations, we reconstruct the 10° and 20° rotation states. Reconstructing intermediate states requires the transformation information used in augmentations, rather than suppressing it, and therefore fosters features containing the augmented transformation information. Our method decomposes feature vectors into invariant and equivariant parts, training them with standard SSL losses and reconstruction losses, respectively. We demonstrate substantial improvements on synthetic equivariance benchmarks while maintaining competitive performance on downstream tasks requiring invariant representations. The approach seamlessly integrates with existing SSL methods (iBOT, DINOv2) and consistently enhances performance across diverse tasks, including segmentation, detection, depth estimation, and video dense prediction. Our framework provides a practical way for augmenting SSL methods with equivariant capabilities while preserving invariant performance.

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JBHI Journal 2026 Journal Article

TinyUSFM: Towards Compact and Efficient Ultrasound Foundation Models

  • Chen Ma
  • Jing Jiao
  • Shuyu Liang
  • Junhu Fu
  • Qin Wang
  • Zeju Li
  • Yuanyuan Wang
  • Yi Guo

Foundation models for medical imaging demonstrate superior generalization capabilities across diverse anatomical structures and clinical applications. Their outstanding performance relies on substantial computational resources, limiting deployment in resourceconstrained clinical environments. This paper presents TinyUSFM, the first lightweight ultrasound foundation model that maintains superior organ versatility and task adaptability of our large-scale Ultrasound Foundation Model (USFM) through knowledge distillation with strategically curated small datasets, delivering significant computational efficiency without sacrificing performance. Considering the limited capacity and representation ability of lightweight models, we propose a feature-gradient driven coreset selection strategy to curate high-quality compact training data, avoiding training degradation from lowquality redundant images. To preserve the essential spatial and frequency domain characteristics during knowledge transfer, we develop domain-separated masked image modeling assisted consistency-driven dynamic distillation. This novel framework adaptively transfers knowledge from large foundation models by leveraging teacher model consistency across different domain masks, specifically tailored for ultrasound interpretation. For evaluation, we establish the UniUS-Bench, the largest publicly available ultrasound benchmark comprising 8 classification and 10 segmentation datasets across 15 organs. Using only 200K images in distillation, TinyUSFM matches USFM's performance with just 6. 36% of parameters and 6. 40% of GFLOPs. TinyUSFM significantly outperforms the vanilla model by 9. 45% in classification and 7. 72% in segmentation, surpassing all state-of-the-art lightweight models, and achieving 84. 91% average classification accuracy and 85. 78% average segmentation Dice score across diverse medical devices and centers. This work successfully bridges the gap between high-performance foundation models and practical clinical deployment, winning the first place in MICCAI2025 IUGC Challenge.

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ICRA Conference 2023 Conference Paper

HAT: Head-Worn Assistive Teleoperation of Mobile Manipulators

  • Akhil Padmanabha
  • Qin Wang
  • Daphne Han
  • Jashkumar Diyora
  • Kriti Kacker
  • Hamza Khalid
  • Liang-Jung Chen
  • Carmel Majidi

Mobile manipulators in the home can provide increased autonomy to individuals with severe motor impairments, who often cannot complete activities of daily living (ADLs) without the help of a caregiver. Teleoperation of an assistive mobile manipulator could enable an individual with motor impairments to independently perform self-care and household tasks, yet limited motor function can impede one's ability to interface with a robot. In this work, we present a unique inertial-based wearable assistive interface, embedded in a familiar head-worn garment, for individuals with severe motor impairments to teleoperate and perform physical tasks with a mobile manipulator. We evaluate this wearable interface with both able-bodied ( $\mathrm{N}=16$ ) and individuals with motor impairments ( $\mathrm{N}=2$ ) for performing ADLs and everyday household tasks. Our results show that the wearable interface enabled participants to complete physical tasks with low error rates, high perceived ease of use, and low workload measures. Overall, this inertial-based wearable serves as a new assistive interface option for control of mobile manipulators in the home.

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AAAI Conference 2022 Conference Paper

Contact-Distil: Boosting Low Homologous Protein Contact Map Prediction by Self-Supervised Distillation

  • Qin Wang
  • Jiayang Chen
  • Yuzhe Zhou
  • Yu Li
  • Liangzhen Zheng
  • Sheng Wang
  • Zhen Li
  • Shuguang Cui

Accurate protein contact map prediction (PCMP) is essential for precise protein structure estimation and further biological studies. Recent works achieve significant performance on this task with high quality multiple sequence alignment (MSA). However, the PCMP accuracy drops dramatically while only poor MSA (e. g. , absolute MSA count less than 10) is available. Therefore, in this paper, we propose the Contact-Distil to improve the low homologous PCMP accuracy through knowledge distillation on a self-supervised model. Particularly, two pre-trained transformers are exploited to learn the high quality and low quality MSA representation in parallel for the teacher and student model correspondingly. Besides, the co-evolution information is further extracted from pure sequence through a pretrained ESM-1b model, which provides auxiliary knowledge to improve student performance. Extensive experiments show Contact-Distil outperforms previous state-of-the-arts by large margins on CAMEO-L dataset for low homologous PCMP, i. e. , around 13. 3% and 9. 5% improvements against Alphafold2 and MSA Transformer respectively when MSA count less than 10.

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IJCAI Conference 2021 Conference Paper

Adaptive Residue-wise Profile Fusion for Low Homologous Protein Secondary Structure Prediction Using External Knowledge

  • Qin Wang
  • Jun Wei
  • Boyuan Wang
  • Zhen Li
  • Sheng Wang
  • Shuguang Cui

Protein secondary structure prediction (PSSP) is essential for protein function analysis. However, for low homologous proteins, the PSSP suffers from insufficient input features. In this paper, we explicitly import external self-supervised knowledge for low homologous PSSP under the guidance of residue-wise (amino acid wise) profile fusion. In practice, we firstly demonstrate the superiority of profile over Position-Specific Scoring Matrix (PSSM) for low homologous PSSP. Based on this observation, we introduce the novel self-supervised BERT features as the pseudo profile, which implicitly involves the residue distribution in all native discovered sequences as the complementary features. Furthermore, a novel residue-wise attention is specially designed to adaptively fuse different features (i. e. , original low-quality profile, BERT based pseudo profile), which not only takes full advantage of each feature but also avoids noise disturbance. Besides, the feature consistency loss is proposed to accelerate the model learning from multiple semantic levels. Extensive experiments confirm that our method outperforms state-of-the-arts (i. e. , 4. 7% for extremely low homologous cases on BC40 dataset).

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AAAI Conference 2021 Conference Paper

PSSM-Distil: Protein Secondary Structure Prediction (PSSP) on Low-Quality PSSM by Knowledge Distillation with Contrastive Learning

  • Qin Wang
  • Boyuan Wang
  • Zhenlei Xu
  • Jiaxiang Wu
  • Peilin Zhao
  • Zhen Li
  • Sheng Wang
  • Junzhou Huang

Protein secondary structure prediction (PSSP) is an essential task in computational biology. To achieve the accurate PSSP, the general and vital feature engineering is to use multiple sequence alignment (MSA) for Position-Specific Scoring Matrix (PSSM) extraction. However, when only low-quality PSSM can be obtained due to poor sequence homology, previous PSSP accuracy (merely around 65%) is far from practical usage for subsequent tasks. In this paper, we propose a novel PSSM-Distil framework for PSSP on low-quality PSSM, which not only enhances the PSSM feature at a lower level but also aligns the feature distribution at a higher level. In practice, the PSSM-Distil first exploits the proteins with high-quality PSSM to achieve a teacher network for PSSP in a full-supervised way. Under the guidance of the teacher network, the low-quality PSSM and corresponding student network with low discriminating capacity are effectively resolved by feature enhancement through EnhanceNet and distribution alignment through knowledge distillation with contrastive learning. Further, our PSSM-Distil supports the input from a pre-trained protein sequence language BERT model to provide auxiliary information, which is designed to address the extremely low-quality PSSM cases, i. e. , no homologous sequence. Extensive experiments demonstrate the proposed PSSM-Distil outperforms state-of-the-art models on PSSP by 6% on average and nearly 8% in extremely low-quality cases on public benchmarks, BC40 and CB513.

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IJCAI Conference 2007 Conference Paper

  • Qin Wang
  • Qin Iris Wang
  • Dekang Lin
  • Dale Schuurmans

Recently, significant progress has been made on learning structured predictors via coordinated training algorithms such as conditional random fields and maximum margin Markov networks. Unfortunately, these techniques are based on specialized training algorithms, are complex to implement, and expensive to run. We present a much simpler approach to training structured predictors by applying a boosting-like procedure to standard supervised training methods. The idea is to learn a local predictor using standard methods, such as logistic regression or support vector machines, but then achieve improved structured classification by "boosting" the influence of misclassified components after structured prediction, re-training the local predictor, and repeating. Further improvement in structured prediction accuracy can be achieved by incorporating "dynamic" features -i. e. an extension whereby the features for one predicted component can depend on the predictions already made for some other components.

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