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Found 11 papers, 7 papers with code
A Large Cross-Modal Video Retrieval Dataset with Reading Comprehension
Most existing cross-modal language-to-video retrieval (VR) research focuses on single-modal input from video, i. e., visual representation, while the text is omnipresent in human environments and frequently critical to understand video.
FlowText: Synthesizing Realistic Scene Text Video with Optical Flow Estimation
This paper introduces a novel video text synthesis technique called FlowText, which utilizes optical flow estimation to synthesize a large amount of text video data at a low cost for training robust video text spotters.
ICDAR 2023 Video Text Reading Competition for Dense and Small Text
In this competition report, we establish a video text reading benchmark, DSText, which focuses on dense and small text reading challenges in the video with various scenarios.
Audience Expansion for Multi-show Release Based on an Edge-prompted Heterogeneous Graph Network
In the offline stage, to construct the graph, user IDs and specific side information combinations of the shows are chosen to be the nodes, and click/co-click relations and view time are used to build the edges.
Real-time End-to-End Video Text Spotter with Contrastive Representation Learning
Our contributions are three-fold: 1) CoText simultaneously address the three tasks (e. g., text detection, tracking, recognition) in a real-time end-to-end trainable framework.
Domain Generalization via Shuffled Style Assembly for Face Anti-Spoofing
A novel Shuffled Style Assembly Network (SSAN) is proposed to extract and reassemble different content and style features for a stylized feature space.
Contrastive Learning of Semantic and Visual Representations for Text Tracking
Semantic representation is of great benefit to the video text tracking(VTT) task that requires simultaneously classifying, detecting, and tracking texts in the video.
A Bilingual, OpenWorld Video Text Dataset and End-to-end Video Text Spotter with Transformer
Most existing video text spotting benchmarks focus on evaluating a single language and scenario with limited data.
Consistency Regularization for Deep Face Anti-Spoofing
Face anti-spoofing (FAS) plays a crucial role in securing face recognition systems.
Frequency-aware Discriminative Feature Learning Supervised by Single-Center Loss for Face Forgery Detection
Face forgery detection is raising ever-increasing interest in computer vision since facial manipulation technologies cause serious worries.
Data Augmentation in Emotion Classification Using Generative Adversarial Networks
In this paper, we propose a data augmentation method using generative adversarial networks (GAN).
