Search Results for author:
Found 10 papers, 8 papers with code
Hierarchical Context Merging: Better Long Context Understanding for Pre-trained LLMs
Large language models (LLMs) have shown remarkable performance in various natural language processing tasks.
IFSeg: Image-free Semantic Segmentation via Vision-Language Model
In this paper, we introduce a novel image-free segmentation task where the goal is to perform semantic segmentation given only a set of the target semantic categories, but without any task-specific images and annotations.
Time Is MattEr: Temporal Self-supervision for Video Transformers
Understanding temporal dynamics of video is an essential aspect of learning better video representations.
TSPipe: Learn from Teacher Faster with Pipelines
The teacher-student (TS) framework, training a (student) network by utilizing an auxiliary superior (teacher) network, has been adopted as a popular training paradigm in many machine learning schemes, since the seminal work---Knowledge distillation (KD) for model compression and transfer learning.
Patch-level Representation Learning for Self-supervised Vision Transformers
Despite its simplicity, we demonstrate that it can significantly improve the performance of existing SSL methods for various visual tasks, including object detection and semantic segmentation.
PASS: Patch-Aware Self-Supervision for Vision Transformer
This paper aims to improve their performance further by utilizing the architectural advantages of the underlying neural network, as the current state-of-the-art visual pretext tasks for self-supervised learning do not enjoy the benefit, i. e., they are architecture-agnostic.
OpenCoS: Contrastive Semi-supervised Learning for Handling Open-set Unlabeled Data
Semi-supervised learning (SSL) has been a powerful strategy to incorporate few labels in learning better representations.
Regularizing Class-wise Predictions via Self-knowledge Distillation
Deep neural networks with millions of parameters may suffer from poor generalization due to overfitting.
Robust Determinantal Generative Classifier for Noisy Labels and Adversarial Attacks
For instance, on CIFAR-10 dataset containing 45% noisy training labels, we improve the test accuracy of a deep model optimized by the state-of-the-art noise-handling training method from33. 34% to 43. 02%.
Robust Inference via Generative Classifiers for Handling Noisy Labels
Large-scale datasets may contain significant proportions of noisy (incorrect) class labels, and it is well-known that modern deep neural networks (DNNs) poorly generalize from such noisy training datasets.
