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Found 12 papers, 5 papers with code
Multi-Architecture Multi-Expert Diffusion Models
In this paper, we address the performance degradation of efficient diffusion models by introducing Multi-architecturE Multi-Expert diffusion models (MEME).
ScoreCL: Augmentation-Adaptive Contrastive Learning via Score-Matching Function
Self-supervised contrastive learning (CL) has achieved state-of-the-art performance in representation learning by minimizing the distance between positive pairs while maximizing that of negative ones.
Addressing Negative Transfer in Diffusion Models
Through this, our approach addresses the issue of negative transfer in diffusion models by allowing for efficient computation of MTL methods.
Cross Encoding as Augmentation: Towards Effective Educational Text Classification
To mitigate these issues, here we propose a novel retrieval approach CEAA that provides effective learning in educational text classification.
Evaluation of Question Generation Needs More References
To this end, we propose to paraphrase the reference question for a more robust QG evaluation.
Towards Practical Plug-and-Play Diffusion Models
For that, the existing practice is to fine-tune the guidance models with labeled data corrupted with noises.
Towards Flexible Inductive Bias via Progressive Reparameterization Scheduling
In addition, through Fourier analysis of feature maps, the model's response patterns according to signal frequency changes, we observe which inductive bias is advantageous for each data scale.
HoughCL: Finding Better Positive Pairs in Dense Self-supervised Learning
Compared to previous works, our method shows better or comparable performance on dense prediction fine-tuning tasks.
CATs: Cost Aggregation Transformers for Visual Correspondence
We propose a novel cost aggregation network, called Cost Aggregation Transformers (CATs), to find dense correspondences between semantically similar images with additional challenges posed by large intra-class appearance and geometric variations.
Ranked #5 on
Semantic correspondence
on PF-WILLOW
SWAD: Domain Generalization by Seeking Flat Minima
Domain generalization (DG) methods aim to achieve generalizability to an unseen target domain by using only training data from the source domains.
Ranked #17 on
Domain Generalization
on TerraIncognita
Robust Long-Term Object Tracking via Improved Discriminative Model Prediction
We propose an improved discriminative model prediction method for robust long-term tracking based on a pre-trained short-term tracker.
Reference-Based Sketch Image Colorization using Augmented-Self Reference and Dense Semantic Correspondence
However, it is difficult to prepare for a training data set that has a sufficient amount of semantically meaningful pairs of images as well as the ground truth for a colored image reflecting a given reference (e. g., coloring a sketch of an originally blue car given a reference green car).
