TV100: A TV Series Dataset that Pre-Trained CLIP Has Not Seen
- URL: http://arxiv.org/abs/2404.12407v1
- Date: Tue, 16 Apr 2024 17:47:45 GMT
- Title: TV100: A TV Series Dataset that Pre-Trained CLIP Has Not Seen
- Authors: Da-Wei Zhou, Zhi-Hong Qi, Han-Jia Ye, De-Chuan Zhan,
- Abstract summary: We make publicly available a novel dataset comprised of images from TV series released post- 2021.
This dataset holds significant potential for use in various research areas, including the evaluation of incremental learning.
- Score: 59.41896032227508
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The era of pre-trained models has ushered in a wealth of new insights for the machine learning community. Among the myriad of questions that arise, one of paramount importance is: 'Do pre-trained models possess comprehensive knowledge?' This paper seeks to address this crucial inquiry. In line with our objective, we have made publicly available a novel dataset comprised of images from TV series released post-2021. This dataset holds significant potential for use in various research areas, including the evaluation of incremental learning, novel class discovery, and long-tailed learning, among others. Project page: https://tv-100.github.io/
Related papers
- CinePile: A Long Video Question Answering Dataset and Benchmark [55.30860239555001]
We present a novel dataset and benchmark, CinePile, specifically designed for authentic long-form video understanding.
Our comprehensive dataset comprises 305,000 multiple-choice questions (MCQs), covering various visual and multimodal aspects.
We fine-tuned open-source Video-LLMs on the training split and evaluated both open-source and proprietary video-centric LLMs on the test split of our dataset.
arXiv Detail & Related papers (2024-05-14T17:59:02Z) - Continual Learning with Pre-Trained Models: A Survey [61.97613090666247]
Continual Learning aims to overcome the catastrophic forgetting of former knowledge when learning new ones.
This paper presents a comprehensive survey of the latest advancements in PTM-based CL.
arXiv Detail & Related papers (2024-01-29T18:27:52Z) - Large-scale Multi-Modal Pre-trained Models: A Comprehensive Survey [66.18478838828231]
Multi-modal pre-trained big models have drawn more and more attention in recent years.
This paper introduces the background of multi-modal pre-training by reviewing the conventional deep, pre-training works in natural language process, computer vision, and speech.
Then, we introduce the task definition, key challenges, and advantages of multi-modal pre-training models (MM-PTMs), and discuss the MM-PTMs with a focus on data, objectives, network, and knowledge enhanced pre-training.
arXiv Detail & Related papers (2023-02-20T15:34:03Z) - Revisiting Classifier: Transferring Vision-Language Models for Video
Recognition [102.93524173258487]
Transferring knowledge from task-agnostic pre-trained deep models for downstream tasks is an important topic in computer vision research.
In this study, we focus on transferring knowledge for video classification tasks.
We utilize the well-pretrained language model to generate good semantic target for efficient transferring learning.
arXiv Detail & Related papers (2022-07-04T10:00:47Z) - Can Population-based Engagement Improve Personalisation? A Novel Dataset
and Experiments [21.12546768556595]
VLE is a novel dataset that consists of content and video based features extracted from publicly available scientific video lectures.
Our experimental results indicate that the newly proposed VLE dataset leads to building context-agnostic engagement prediction models.
Experiments in combining the built model with a personalising algorithm show promising improvements in addressing the cold-start problem encountered in educational recommenders.
arXiv Detail & Related papers (2022-06-22T15:53:24Z) - PEEK: A Large Dataset of Learner Engagement with Educational Videos [20.49299110732228]
We release a large, novel dataset of learners engaging with educational videos in-the-wild.
The dataset, named Personalised Educational Engagement with Knowledge Topics PEEK, is the first publicly available dataset of this nature.
We believe that granular learner engagement signals in unison with rich content representations will pave the way to building powerful personalization algorithms.
arXiv Detail & Related papers (2021-09-03T11:23:02Z) - Reasoning-Modulated Representations [85.08205744191078]
We study a common setting where our task is not purely opaque.
Our approach paves the way for a new class of data-efficient representation learning.
arXiv Detail & Related papers (2021-07-19T13:57:13Z) - VLEngagement: A Dataset of Scientific Video Lectures for Evaluating
Population-based Engagement [23.078055803229912]
Video lectures have become one of the primary modalities to impart knowledge to masses in the current digital age.
There is still an important need for data and research aimed at understanding learner engagement with scientific video lectures.
This paper introduces VLEngagement, a novel dataset that consists of content-based and video-specific features extracted from publicly available scientific video lectures.
arXiv Detail & Related papers (2020-11-02T14:20:19Z) - OvA-INN: Continual Learning with Invertible Neural Networks [0.0]
OvA-INN is able to learn one class at a time and without storing any of the previous data.
We show that we can take advantage of pretrained models by stacking an Invertible Network on top of a feature extractor.
arXiv Detail & Related papers (2020-06-24T14:40:05Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.