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- 90
- Cross-Modality High-Frequency Transformer for MR Image Super-Resolution68
An early effort to build a Transformer-based MR image super-resolution framework, with careful designs on exploring valuable domain prior knowledge, and establishes a novel Transformer architecture, called Cross-modality high-frequency Transformer (Cohf-T), to introduce such priors into super-resolving the low-resolution MR images.
- Weakly Supervised Semantic Segmentation via Alternate Self-Dual Teaching54
A novel end-to-end learning framework, alternate self-dual teaching (ASDT), based on a dual-teacher single-student network architecture is built, inspired by the Pulse Width (PW) modulation, to alleviate the influence of the imperfect knowledge from either teacher model on the KD process.
- Comparing Machine Learning Models and Statistical Models for Predicting Heart Failure Events: A Systematic Review and Meta-Analysis53
ML models did not achieve a significant advantage in predicting events, and their clinical feasibility and reliability were worse, and the efficacy of ML models among different HF subgroups is still unclear.
- Revisiting the Power of Prompt for Visual Tuning51
This method significantly advances the adaptation for self-supervised pretraining, achieving impressive task performance gains of at least 10% to 30%.
- Intrinsic Image Transformation via Scale Space Decomposition43
A new network structure for decomposing an image into its intrinsic albedo and shading is introduced, and a multi-channel architecture that learns the image-to-image transformation function in successive frequency bands in parallel is a fully convolutional neural network.
- Unsupervised Pre-Training With Language-Vision Prompts for Low-Data Instance Segmentation42
A new method, Unsupervised Pre-training with Language-Vision Prompts (UPLVP), which improves QEIS models’ instance segmentation by bringing language-vision prompts to queries/kernels, and can converge faster and perform better than CNN-based models in low-data regimes.
- KG-Rank: Enhancing Large Language Models for Medical QA with Knowledge Graphs and Ranking Techniques42
- GP-NeRF: Generalized Perception NeRF for Context-Aware 3D Scene Understanding37
Generalized Perception NeRF is proposed, a novel pipeline that makes the widely used segmentation model and NeRF work compatibly under a unified framework, for facilitating context-aware 3D scene perception and introduces transformers to aggregate radiance as well as semantic embedding fields jointly for novel views and facilitates the joint volumetric rendering of both fields.
- ODMixer: Fine-Grained Spatial-Temporal MLP for Metro Origin-Destination Prediction33
This work learns traffic evolution from the perspective of all OD pairs and proposes a fine-grained spatial-temporal MLP architecture for metro OD prediction, namely ODMixer, which has double-branch structure and involves the Channel Mixer, the Multi-view Mixer, and the Bidirectional Trend Learner.
- Discrete to Continuous: Generating Smooth Transition Poses from Sign Language Observations31
This work proposes a novel framework, Sign-D2C, that employs a conditional diffusion model to synthesize contextually smooth transition frames, enabling the seamless construction of continuous sign language sequences.
- Improving Knowledge Distillation via Regularizing Feature Norm and Direction31
This work proposes a rather simple loss term (dubbed ND loss) to simultaneously encourage student to produce large-norm features, and align the direction of student features and teacher class-means, leading to the state-of-the-art performance on these benchmarks.
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- audeosynth25
A framework for automatically generating music-driven video montages is developed that extracts carefully designed temporal features from the input, and casts the synthesis problem as an optimization and solves the parameters through Markov Chain Monte Carlo sampling.
- Boosting Low-Data Instance Segmentation by Unsupervised Pre-training with Saliency Prompt24
A new pre-training method that boosts QEIS models by giving Saliency Prompt for queries/kernels and helps QE IS models achieve a similar convergence speed and comparable performance with CNN-based models in low-data regimes is introduced.
- ScrollTimes: Tracing the Provenance of Paintings as a Window Into History23
This work examines the handscroll, a traditional Chinese painting form that provides a rich source of historical data and a unique opportunity to explore history through cultural artifacts, and presents a three-tiered methodology encompassing artifact, contextual, and provenance levels, designed to create a "Biography" for handscroll.
- Adapting Object Size Variance and Class Imbalance for Semi-supervised Object Detection20
This work proposes an adaptive pseudo labeling strategy, which can assign thresholds to classes with respect to their “hardness”, and devise a joint feature-level and prediction-level consistency learning pipeline for transferring the information of the teacher model to the student model that achieves state-of-the-art performance.
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- Fast and effective molecular property prediction with transferability map19
A principal gradient-based measurement is developed to evaluate the quantitative transferability from the source property to the target property before applying transfer learning, significantly improving the transfer learning performance.
- Generalization Matters: Loss Minima Flattening via Parameter Hybridization for Efficient Online Knowledge Distillation19
This paper integrates HWM's loss into students' training and proposes a novel OKD framework via parameter hybridization (OKDPH), achieving higher performance with fewer parameters, benefiting OKD with lightweight and robust characteristics.
- Integrating UMLS Knowledge into Large Language Models for Medical Question Answering18
This research demonstrates the effectiveness of using UMLS-augmented LLMs and highlights the potential application value of LLMs in in medical question-answering.
- Masked Collaborative Contrast for Weakly Supervised Semantic Segmentation17
This study introduces an efficacious approach, Masked Collaborative Contrast (MCC), to highlight semantic regions in weakly supervised semantic segmentation and generates positive and negative samples in contrastive learning by utilizing the masked local output and contrasting it with the global output.
- De-biased Teacher: Rethinking IoU Matching for Semi-supervised Object Detection17
A general framework -- De-biased Teacher, which abandons both the IoU matching and pseudo labeling processes by directly generating favorable training proposals for consistency regularization between the weak/strong augmented image pairs and consistently outperforms other state-of-the-art methods on the MS-COCO and PASCAL VOC benchmarks.
- Re-Attention Transformer for Weakly Supervised Object Localization17
A re-attention mechanism termed token refinement transformer (TRT) that captures the object-level semantics to guide the localization well is presented that introduces a novel module named token priority scoring module (TPSM) to suppress the effects of background noise while focusing on the target object.
- Mix-DANN and Dynamic-Modal-Distillation for Video Domain Adaptation17
This paper proposes Mix-Domain-Adversarial Neural Network and Dynamic-Modal-Distillation (MD-DMD), a novel multi-modal adversarial learning framework for unsupervised video domain adaptation that incorporates the temporal information between source and target domains, as well as the diversity of adaptability between modalities.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-14. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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