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Found 7 papers, 3 papers with code
Asymmetric Decision-Making in Online Knowledge Distillation:Unifying Consensus and Divergence
Building on these findings, we propose Asymmetric Decision-Making (ADM) to enhance feature consensus learning for student models while continuously promoting feature diversity in teacher models.
Cascade Prompt Learning for Vision-Language Model Adaptation
Prompt learning has surfaced as an effective approach to enhance the performance of Vision-Language Models (VLMs) like CLIP when applied to downstream tasks.
Revisiting Prompt Pretraining of Vision-Language Models
To address the above issues, in this paper, we propose a general framework termed Revisiting Prompt Pretraining (RPP), which targets at improving the fitting and generalization ability from two aspects: prompt structure and prompt supervision.
HiDiffusion: Unlocking Higher-Resolution Creativity and Efficiency in Pretrained Diffusion Models
Extensive experiments demonstrate that our approach can address object duplication and heavy computation issues, achieving state-of-the-art performance on higher-resolution image synthesis tasks.
Boosting Semi-Supervised Learning by Exploiting All Unlabeled Data
ANL introduces the additional negative pseudo-label for all unlabeled data to leverage low-confidence examples.
GeoGCN: Geometric Dual-domain Graph Convolution Network for Point Cloud Denoising
We propose GeoGCN, a novel geometric dual-domain graph convolution network for point cloud denoising (PCD).
Lane Detection in Low-light Conditions Using an Efficient Data Enhancement : Light Conditions Style Transfer
Nowadays, deep learning techniques are widely used for lane detection, but application in low-light conditions remains a challenge until this day.
