Search Results for author:
Found 23 papers, 14 papers with code
iDAT: inverse Distillation Adapter-Tuning
Adapter-Tuning (AT) method involves freezing a pre-trained model and introducing trainable adapter modules to acquire downstream knowledge, thereby calibrating the model for better adaptation to downstream tasks.
VM-UNet: Vision Mamba UNet for Medical Image Segmentation
To our best knowledge, this is the first medical image segmentation model constructed based on the pure SSM-based model.
CLAPP: Contrastive Language-Audio Pre-training in Passive Underwater Vessel Classification
Existing research on audio classification faces challenges in recognizing attributes of passive underwater vessel scenarios and lacks well-annotated datasets due to data privacy concerns.
Learning Multi-axis Representation in Frequency Domain for Medical Image Segmentation
Specifically, our block performs a Fourier transform on the three axes of the input features and assigns the external weight in the frequency domain, which is generated by our External Weights Generator.
LAMM: Label Alignment for Multi-Modal Prompt Learning
We conduct experiments on 11 downstream vision datasets and demonstrate that our method significantly improves the performance of existing multi-modal prompt learning models in few-shot scenarios, exhibiting an average accuracy improvement of 2. 31(\%) compared to the state-of-the-art methods on 16 shots.
Supervised Contrastive Learning for Fine-grained Chromosome Recognition
Chromosome recognition is an essential task in karyotyping, which plays a vital role in birth defect diagnosis and biomedical research.
GIST: Improving Parameter Efficient Fine Tuning via Knowledge Interaction
2) They neglect the interaction between the intrinsic task-agnostic knowledge of pre-trained models and the task-specific knowledge in downstream tasks.
Towards Open-set Gesture Recognition via Feature Activation Enhancement and Orthogonal Prototype Learning
While there has been significant progress in gesture recognition based on surface electromyography (sEMG), accurate recognition of predefined gestures only within a closed set is still inadequate in practice.
Towards Discriminative Representation with Meta-learning for Colonoscopic Polyp Re-Identification
Colonoscopic Polyp Re-Identification aims to match the same polyp from a large gallery with images from different views taken using different cameras and plays an important role in the prevention and treatment of colorectal cancer in computer-aided diagnosis.
Learning Discriminative Visual-Text Representation for Polyp Re-Identification
Colonoscopic Polyp Re-Identification aims to match a specific polyp in a large gallery with different cameras and views, which plays a key role for the prevention and treatment of colorectal cancer in the computer-aided diagnosis.
Learning Robust Visual-Semantic Embedding for Generalizable Person Re-identification
In this paper, we propose a Multi-Modal Equivalent Transformer called MMET for more robust visual-semantic embedding learning on visual, textual and visual-textual tasks respectively.
Generalizable Person Re-identification
Representation Learning
Colo-SCRL: Self-Supervised Contrastive Representation Learning for Colonoscopic Video Retrieval
To seek a solution to this problem, we construct a large-scale colonoscopic dataset named Colo-Pair for medical practice.
AutoKary2022: A Large-Scale Densely Annotated Dataset for Chromosome Instance Segmentation
Automated chromosome instance segmentation from metaphase cell microscopic images is critical for the diagnosis of chromosomal disorders (i. e., karyotype analysis).
CluCDD:Contrastive Dialogue Disentanglement via Clustering
A huge number of multi-participant dialogues happen online every day, which leads to difficulty in understanding the nature of dialogue dynamics for both humans and machines.
MALUNet: A Multi-Attention and Light-weight UNet for Skin Lesion Segmentation
To address this challenge, we propose a light-weight model to achieve competitive performances for skin lesion segmentation at the lowest cost of parameters and computational complexity so far.
Deep Multimodal Fusion for Generalizable Person Re-identification
Person re-identification plays a significant role in realistic scenarios due to its various applications in public security and video surveillance.
Domain Generalization
Generalizable Person Re-identification
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MEW-UNet: Multi-axis representation learning in frequency domain for medical image segmentation
Specifically, our block performs a Fourier transform on the three axes of the input feature and assigns the external weight in the frequency domain, which is generated by our Weights Generator.
SubFace: Learning with Softmax Approximation for Face Recognition
The softmax-based loss functions and its variants (e. g., cosface, sphereface, and arcface) significantly improve the face recognition performance in wild unconstrained scenes.
Rethinking Person Re-Identification via Semantic-Based Pretraining
Pretraining is a dominant paradigm in computer vision.
Less is More: Learning from Synthetic Data with Fine-grained Attributes for Person Re-Identification
Moreover, aiming to fully exploit the potential of FineGPR and promote the efficient training from millions of synthetic data, we propose an attribute analysis pipeline called AOST, which dynamically learns attribute distribution in real domain, then eliminates the gap between synthetic and real-world data and thus is freely deployed to new scenarios.
Learning from Self-Discrepancy via Multiple Co-teaching for Cross-Domain Person Re-Identification
Employing clustering strategy to assign unlabeled target images with pseudo labels has become a trend for person re-identification (re-ID) algorithms in domain adaptation.
Attribute analysis with synthetic dataset for person re-identification
To address this problem, firstly, we develop a large-scale synthetic data engine, the salient characteristic of this engine is controllable.
