Search Results for author:
Found 24 papers, 9 papers with code
DocQueryNet: Value Retrieval with Arbitrary Queries for Form-like Documents
Unlike previous methods that only address a fixed set of field items, our method predicts target value for an arbitrary query based on the understanding of the layout and semantics of a form.
4M: Massively Multimodal Masked Modeling
Current machine learning models for vision are often highly specialized and limited to a single modality and task.
Mask-free OVIS: Open-Vocabulary Instance Segmentation without Manual Mask Annotations
In summary, an OV method learns task-specific information using strong supervision from base annotations and novel category information using weak supervision from image-captions pairs.
ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understanding
Then, ULIP learns a 3D representation space aligned with the common image-text space, using a small number of automatically synthesized triplets.
Ranked #3 on
Training-free 3D Point Cloud Classification
on ModelNet40
(using extra training data)
TAG: Boosting Text-VQA via Text-aware Visual Question-answer Generation
To address this deficiency, we develop a new method to generate high-quality and diverse QA pairs by explicitly utilizing the existing rich text available in the scene context of each image.
Value Retrieval with Arbitrary Queries for Form-like Documents
Unlike previous methods that only address a fixed set of field items, our method predicts target value for an arbitrary query based on the understanding of the layout and semantics of a form.
Burn After Reading: Online Adaptation for Cross-domain Streaming Data
In the context of online privacy, many methods propose complex privacy and security preserving measures to protect sensitive data.
Open Vocabulary Object Detection with Pseudo Bounding-Box Labels
To enlarge the set of base classes, we propose a method to automatically generate pseudo bounding-box annotations of diverse objects from large-scale image-caption pairs.
Robustness Evaluation of Transformer-based Form Field Extractors via Form Attacks
We propose a novel framework to evaluate the robustness of transformer-based form field extraction methods via form attacks.
Field Extraction from Forms with Unlabeled Data
We propose a novel framework to conduct field extraction from forms with unlabeled data.
Deep Co-Training with Task Decomposition for Semi-Supervised Domain Adaptation
To integrate the strengths of the two classifiers, we apply the well-established co-training framework, in which the two classifiers exchange their high confident predictions to iteratively "teach each other" so that both classifiers can excel in the target domain.
Semi-supervised Domain Adaptation
Unsupervised Domain Adaptation
InfoFocus: 3D Object Detection for Autonomous Driving with Dynamic Information Modeling
Results show that our framework achieves the state-of-the-art performance with 31 FPS and improves our baseline significantly by 9. 0% mAP on the nuScenes test set.
Ranked #328 on
3D Object Detection
on nuScenes
WOAD: Weakly Supervised Online Action Detection in Untrimmed Videos
Online action detection in untrimmed videos aims to identify an action as it happens, which makes it very important for real-time applications.
Ranked #5 on
Online Action Detection
on THUMOS'14
WSLLN:Weakly Supervised Natural Language Localization Networks
We propose weakly supervised language localization networks (WSLLN) to detect events in long, untrimmed videos given language queries.
Consistency-based Semi-supervised Active Learning: Towards Minimizing Labeling Cost
Active learning (AL) combines data labeling and model training to minimize the labeling cost by prioritizing the selection of high value data that can best improve model performance.
Consistency-Based Semi-Supervised Active Learning: Towards Minimizing Labeling Budget
Active learning (AL) aims to integrate data labeling and model training in a unified way, and to minimize the labeling budget by prioritizing the selection of high value data that can best improve model performance.
WSLLN: Weakly Supervised Natural Language Localization Networks
We propose weakly supervised language localization networks (WSLLN) to detect events in long, untrimmed videos given language queries.
Data augmentation in microscopic images for material data mining
Recent progress in material data mining has been driven by high-capacity models trained on large datasets.
Goal-oriented Object Importance Estimation in On-road Driving Videos
We propose a novel framework that incorporates both visual model and goal representation to conduct OIE.
StartNet: Online Detection of Action Start in Untrimmed Videos
We propose StartNet to address Online Detection of Action Start (ODAS) where action starts and their associated categories are detected in untrimmed, streaming videos.
Temporal Recurrent Networks for Online Action Detection
Most work on temporal action detection is formulated as an offline problem, in which the start and end times of actions are determined after the entire video is fully observed.
Ranked #12 on
Online Action Detection
on TVSeries
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.
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).
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.
