81 citations · 387 across the 28 of their papers we have counts for
26 papers · 1 filter
TextInVision: Text and Prompt Complexity Driven Visual Text Generation Benchmark
Forouzan Fallah, Maitreya Patel, Agneet Chatterjee +3
Generating images with embedded text is crucial for the automatic production of visual and multimodal documents, such as educational materials and advertisements. However, existing…
DocSynthv2: A Practical Autoregressive Modeling for Document Generation
Sanket Biswas, Rajiv Jain, Vlad I. Morariu +5
While the generation of document layouts has been extensively explored, comprehensive document generation encompassing both layout and content presents a more complex challenge. Th…
MGDoc: Pre-training with Multi-granular Hierarchy for Document Image Understanding
Zilong Wang, Jiuxiang Gu, Chris Tensmeyer +5
Document images are a ubiquitous source of data where the text is organized in a complex hierarchical structure ranging from fine granularity (e.g., words), medium granularity (e.g…
End-to-end Document Recognition and Understanding with Dessurt
Brian Davis, Bryan Morse, Bryan Price +3
We introduce Dessurt, a relatively simple document understanding transformer capable of being fine-tuned on a greater variety of document tasks than prior methods. It receives a do…
SelfDoc: Self-Supervised Document Representation Learning
Peizhao Li, Jiuxiang Gu, Jason Kuen +5
We propose SelfDoc, a task-agnostic pre-training framework for document image understanding. Because documents are multimodal and are intended for sequential reading, our framework…
RPCL: A Framework for Improving Cross-Domain Detection with Auxiliary Tasks
Kai Li, Curtis Wigington, Chris Tensmeyer +5
Cross-Domain Detection (XDD) aims to train an object detector using labeled image from a source domain but have good performance in the target domain with only unlabeled images. Ex…
Black-box Explanation of Object Detectors via Saliency Maps
Vitali Petsiuk, Rajiv Jain, Varun Manjunatha +4
We propose D-RISE, a method for generating visual explanations for the predictions of object detectors. Utilizing the proposed similarity metric that accounts for both localization…
Cross-Domain Document Object Detection: Benchmark Suite and Method
Kai Li, Curtis Wigington, Chris Tensmeyer +6
Decomposing images of document pages into high-level semantic regions (e.g., figures, tables, paragraphs), document object detection (DOD) is fundamental for downstream tasks like…
Referring to Objects in Videos using Spatio-Temporal Identifying Descriptions
Peratham Wiriyathammabhum, Abhinav Shrivastava, Vlad I. Morariu +1
This paper presents a new task, the grounding of spatio-temporal identifying descriptions in videos. Previous work suggests potential bias in existing datasets and emphasizes the n…
Learning Rich Features for Image Manipulation Detection
Peng Zhou, Xintong Han, Vlad I. Morariu +1
Image manipulation detection is different from traditional semantic object detection because it pays more attention to tampering artifacts than to image content, which suggests tha…
Fused Deep Neural Networks for Efficient Pedestrian Detection
Xianzhi Du, Mostafa El-Khamy, Vlad I. Morariu +2
In this paper, we present an efficient pedestrian detection system, designed by fusion of multiple deep neural network (DNN) systems. Pedestrian candidates are first generated by a…
Layout-induced Video Representation for Recognizing Agent-in-Place Actions
Ruichi Yu, Hongcheng Wang, Ang Li +3
We address the recognition of agent-in-place actions, which are associated with agents who perform them and places where they occur, in the context of outdoor home surveillance. We…
Two-Stream Neural Networks for Tampered Face Detection
Peng Zhou, Xintong Han, Vlad I. Morariu +1
We propose a two-stream network for face tampering detection. We train GoogLeNet to detect tampering artifacts in a face classification stream, and train a patch based triplet netw…
NISP: Pruning Networks using Neuron Importance Score Propagation
Ruichi Yu, Ang Li, Chun-Fu Chen +6
To reduce the significant redundancy in deep Convolutional Neural Networks (CNNs), most existing methods prune neurons by only considering statistics of an individual layer or two…
C-WSL: Count-guided Weakly Supervised Localization
Mingfei Gao, Ang Li, Ruichi Yu +2
We introduce count-guided weakly supervised localization (C-WSL), an approach that uses per-class object count as a new form of supervision to improve weakly supervised localizatio…
Dynamic Zoom-in Network for Fast Object Detection in Large Images
Mingfei Gao, Ruichi Yu, Ang Li +2
We introduce a generic framework that reduces the computational cost of object detection while retaining accuracy for scenarios where objects with varied sizes appear in high resol…
Visual Relationship Detection with Internal and External Linguistic Knowledge Distillation
Ruichi Yu, Ang Li, Vlad I. Morariu +1
Understanding visual relationships involves identifying the subject, the object, and a predicate relating them. We leverage the strong correlations between the predicate and the (s…
Generalized Deep Image to Image Regression
Venkataraman Santhanam, Vlad I. Morariu, Larry S. Davis
We present a Deep Convolutional Neural Network architecture which serves as a generic image-to-image regressor that can be trained end-to-end without any further machinery. Our pro…
Learning a Discriminative Filter Bank within a CNN for Fine-grained Recognition
Yaming Wang, Vlad I. Morariu, Larry S. Davis
Compared to earlier multistage frameworks using CNN features, recent end-to-end deep approaches for fine-grained recognition essentially enhance the mid-level learning capability o…
Generating Holistic 3D Scene Abstractions for Text-based Image Retrieval
Ang Li, Jin Sun, Joe Yue-Hei Ng +3
Spatial relationships between objects provide important information for text-based image retrieval. As users are more likely to describe a scene from a real world perspective, usin…
The Role of Context Selection in Object Detection
Ruichi Yu, Xi Chen, Vlad I. Morariu +1
We investigate the reasons why context in object detection has limited utility by isolating and evaluating the predictive power of different context cues under ideal conditions in…
Modeling Context Between Objects for Referring Expression Understanding
Varun K. Nagaraja, Vlad I. Morariu, Larry S. Davis
Referring expressions usually describe an object using properties of the object and relationships of the object with other objects. We propose a technique that integrates context b…
Mining Discriminative Triplets of Patches for Fine-Grained Classification
Yaming Wang, Jonghyun Choi, Vlad I. Morariu +1
Fine-grained classification involves distinguishing between similar sub-categories based on subtle differences in highly localized regions; therefore, accurate localization of disc…
VRFP: On-the-fly Video Retrieval using Web Images and Fast Fisher Vector Products
Xintong Han, Bharat Singh, Vlad I. Morariu +1
VRFP is a real-time video retrieval framework based on short text input queries, which obtains weakly labeled training images from the web after the query is known. The retrieved w…