Search Results for author:
Found 28 papers, 6 papers with code
TutoAI: A Cross-domain Framework for AI-assisted Mixed-media Tutorial Creation on Physical Tasks
Mixed-media tutorials, which integrate videos, images, text, and diagrams to teach procedural skills, offer more browsable alternatives than timeline-based videos.
LayerDoc: Layer-wise Extraction of Spatial Hierarchical Structure in Visually-Rich Documents
Experiments show that our approach outperforms competitive baselines by 10-15% on three diverse datasets of forms and mobile app screen layouts for the tasks of spatial region classification, higher-order group identification, layout hierarchy extraction, reading order detection, and word grouping.
MGDoc: Pre-training with Multi-granular Hierarchy for Document Image Understanding
In contrast, region-level models attempt to encode regions corresponding to paragraphs or text blocks into a single embedding, but they perform worse with additional word-level features.
Unified Pretraining Framework for Document Understanding
Document intelligence automates the extraction of information from documents and supports many business applications.
Ranked #7 on
Document Layout Analysis
on PubLayNet val
SelfDoc: Self-Supervised Document Representation Learning
For downstream usage, we propose a novel modality-adaptive attention mechanism for multimodal feature fusion by adaptively emphasizing language and vision signals.
RPCL: A Framework for Improving Cross-Domain Detection with Auxiliary Tasks
Contrasted with prior work, this paper provides a complementary solution to align domains by learning the same auxiliary tasks in both domains simultaneously.
Black-box Explanation of Object Detectors via Saliency Maps
We propose D-RISE, a method for generating visual explanations for the predictions of object detectors.
Cross-Domain Document Object Detection: Benchmark Suite and Method
We establish a benchmark suite consisting of different types of PDF document datasets that can be utilized for cross-domain DOD model training and evaluation.
Referring to Objects in Videos using Spatio-Temporal Identifying Descriptions
This paper presents a new task, the grounding of spatio-temporal identifying descriptions in videos.
Learning Rich Features for Image Manipulation Detection
Image manipulation detection is different from traditional semantic object detection because it pays more attention to tampering artifacts than to image content, which suggests that richer features need to be learned.
Fused Deep Neural Networks for Efficient Pedestrian Detection
The classification system further classifies the generated candidates based on opinions of multiple deep verification networks and a fusion network which utilizes a novel soft-rejection fusion method to adjust the confidence in the detection results.
Layout-induced Video Representation for Recognizing Agent-in-Place Actions
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.
Two-Stream Neural Networks for Tampered Face Detection
We propose a two-stream network for face tampering detection.
NISP: Pruning Networks using Neuron Importance Score Propagation
In contrast, we argue that it is essential to prune neurons in the entire neuron network jointly based on a unified goal: minimizing the reconstruction error of important responses in the "final response layer" (FRL), which is the second-to-last layer before classification, for a pruned network to retrain its predictive power.
Dynamic Zoom-in Network for Fast Object Detection in Large Images
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 resolution images.
C-WSL: Count-guided Weakly Supervised Localization
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 localization (WSL).
Visual Relationship Detection with Internal and External Linguistic Knowledge Distillation
Understanding visual relationships involves identifying the subject, the object, and a predicate relating them.
Generalized Deep Image to Image Regression
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.
Learning a Discriminative Filter Bank within a CNN for Fine-grained Recognition
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 of CNNs.
Ranked #20 on
Fine-Grained Image Classification
on CUB-200-2011
Generating Holistic 3D Scene Abstractions for Text-based Image Retrieval
Since interactions between objects can be reduced to a limited set of atomic spatial relations in 3D, we study the possibility of inferring 3D structure from a text description rather than an image, applying physical relation models to synthesize holistic 3D abstract object layouts satisfying the spatial constraints present in a textual description.
The Role of Context Selection in Object Detection
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 which context provided by an oracle.
Modeling Context Between Objects for Referring Expression Understanding
Our approach uses an LSTM to learn the probability of a referring expression, with input features from a region and a context region.
Mining Discriminative Triplets of Patches for Fine-Grained Classification
Fine-grained classification involves distinguishing between similar sub-categories based on subtle differences in highly localized regions; therefore, accurate localization of discriminative regions remains a major challenge.
VRFP: On-the-fly Video Retrieval using Web Images and Fast Fisher Vector Products
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.
Searching for Objects using Structure in Indoor Scenes
However, we can use structure in the scene to search for objects without processing the entire image.
Selecting Relevant Web Trained Concepts for Automated Event Retrieval
Given a text description of an event, event retrieval is performed by selecting concepts linguistically related to the event description and fusing the concept responses on unseen videos.
Planar Structure Matching Under Projective Uncertainty for Geolocation
Image based geolocation aims to answer the question: where was this ground photograph taken?
Automatic online tuning for fast Gaussian summation
To solve the second problem, we present an online tuning approach that results in a black box method that automatically chooses the evaluation method and its parameters to yield the best performance for the input data, desired accuracy, and bandwidth.
