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Found 22 papers, 10 papers with code
Multi-Modal Prompt Learning on Blind Image Quality Assessment
Image Quality Assessment (IQA) models benefit significantly from semantic information, which allows them to treat different types of objects distinctly.
RESTORE: Towards Feature Shift for Vision-Language Prompt Learning
Prompt learning is effective for fine-tuning foundation models to improve their generalization across a variety of downstream tasks.
Sinkhorn Distance Minimization for Knowledge Distillation
We propose the Sinkhorn Knowledge Distillation (SinKD) that exploits the Sinkhorn distance to ensure a nuanced and precise assessment of the disparity between teacher and student distributions.
MMICT: Boosting Multi-Modal Fine-Tuning with In-Context Examples
Although In-Context Learning (ICL) brings remarkable performance gains to Large Language Models (LLMs), the improvements remain lower than fine-tuning on downstream tasks.
Less is More: Learning Reference Knowledge Using No-Reference Image Quality Assessment
Concretely, by innovatively introducing a novel feature distillation method in IQA, we propose a new framework to learn comparative knowledge from non-aligned reference images.
Towards Robust Text Retrieval with Progressive Learning
However, existing embedding models for text retrieval usually have three non-negligible limitations.
SoftCLIP: Softer Cross-modal Alignment Makes CLIP Stronger
During the preceding biennium, vision-language pre-training has achieved noteworthy success on several downstream tasks.
Efficient Decoder-free Object Detection with Transformers
A natural usage of ViTs in detection is to replace the CNN-based backbone with a transformer-based backbone, which is straightforward and effective, with the price of bringing considerable computation burden for inference.
PyramidCLIP: Hierarchical Feature Alignment for Vision-language Model Pretraining
Large-scale vision-language pre-training has achieved promising results on downstream tasks.
Self-supervised Models are Good Teaching Assistants for Vision Transformers
These findings motivate us to introduce an self-supervised teaching assistant (SSTA) besides the commonly used supervised teacher to improve the performance of transformers.
DisCo: Remedy Self-supervised Learning on Lightweight Models with Distilled Contrastive Learning
Specifically, we find the final embedding obtained by the mainstream SSL methods contains the most fruitful information, and propose to distill the final embedding to maximally transmit a teacher's knowledge to a lightweight model by constraining the last embedding of the student to be consistent with that of the teacher.
High-throughput fast full-color digital pathology based on Fourier ptychographic microscopy via color transfer
However, the conventional full-color digital pathology based on FPM is still time-consuming due to the repeated experiments with tri-wavelengths.
An Empirical Study and Analysis on Open-Set Semi-Supervised Learning
Pseudo-labeling (PL) and Data Augmentation-based Consistency Training (DACT) are two approaches widely used in Semi-Supervised Learning (SSL) methods.
Association: Remind Your GAN not to Forget
Neural networks are susceptible to catastrophic forgetting.
Generative and Discriminative Learning for Distorted Image Restoration
Due to the uncertainty in the distortion variation, restoring distorted images caused by liquify filter is a challenging task.
Enhancing Unsupervised Video Representation Learning by Decoupling the Scene and the Motion
Specifically, we construct a positive clip and a negative clip for each video.
Removing the Background by Adding the Background: Towards Background Robust Self-supervised Video Representation Learning
Then we force the model to pull the feature of the distracting video and the feature of the original video closer, so that the model is explicitly restricted to resist the background influence, focusing more on the motion changes.
Edge effect removal in Fourier ptychographic microscopy via periodic plus smooth image decomposition
Fourier ptychographic microscopy (FPM) is a promising computational imaging technique with high resolution, wide field-of-view (FOV) and quantitative phase recovery.
Automatic Remaining Useful Life Estimation Framework with Embedded Convolutional LSTM as the Backbone
An essential task in predictive maintenance is the prediction of the Remaining Useful Life (RUL) through the analysis of multivariate time series.
Filter Grafting for Deep Neural Networks: Reason, Method, and Cultivation
To gain a universal improvement on both valid and invalid filters, we compensate grafting with distillation (\textbf{Cultivation}) to overcome the drawback of grafting .
Double Supervised Network with Attention Mechanism for Scene Text Recognition
In this paper, we propose Double Supervised Network with Attention Mechanism (DSAN), a novel end-to-end trainable framework for scene text recognition.
Fused Text Segmentation Networks for Multi-oriented Scene Text Detection
In this paper, we introduce a novel end-end framework for multi-oriented scene text detection from an instance-aware semantic segmentation perspective.
Ranked #12 on
Scene Text Detection
on MSRA-TD500
