Search Results for author:
Found 19 papers, 6 papers with code
Sparse Semi-DETR: Sparse Learnable Queries for Semi-Supervised Object Detection
In this paper, we address the limitations of the DETR-based semi-supervised object detection (SSOD) framework, particularly focusing on the challenges posed by the quality of object queries.
FocusCLIP: Multimodal Subject-Level Guidance for Zero-Shot Transfer in Human-Centric Tasks
We propose FocusCLIP, integrating subject-level guidance--a specialized mechanism for target-specific supervision--into the CLIP framework for improved zero-shot transfer on human-centric tasks.
Ranked #1 on
Emotion Recognition
on EMOTIC
Introducing Language Guidance in Prompt-based Continual Learning
While the model faces a disjoint set of classes in each task in this setting, we argue that these classes can be encoded to the same embedding space of a pre-trained language encoder.
Bridging the Performance Gap between DETR and R-CNN for Graphical Object Detection in Document Images
Upon integrating query modifications in the DETR, we outperform prior works and achieve new state-of-the-art results with the mAP of 96. 9\%, 95. 7\% and 99. 3\% on TableBank, PubLaynet, PubTables, respectively.
Ranked #3 on
Document Layout Analysis
on PubLayNet val
Object Detection with Transformers: A Review
The astounding performance of transformers in natural language processing (NLP) has motivated researchers to explore their applications in computer vision tasks.
Towards End-to-End Semi-Supervised Table Detection with Deformable Transformer
Table detection is the task of classifying and localizing table objects within document images.
Robust and Fast Vehicle Detection using Augmented Confidence Map
Vehicle detection in real-time scenarios is challenging because of the time constraints and the presence of multiple types of vehicles with different speeds, shapes, structures, etc.
I2MVFormer: Large Language Model Generated Multi-View Document Supervision for Zero-Shot Image Classification
Our proposed model, I2MVFormer, learns multi-view semantic embeddings for zero-shot image classification with these class views.
Learning Attention Propagation for Compositional Zero-Shot Learning
CAPE learns to identify this structure and propagates knowledge between them to learn class embedding for all seen and unseen compositions.
SemAttNet: Towards Attention-based Semantic Aware Guided Depth Completion
Depth completion involves recovering a dense depth map from a sparse map and an RGB image.
Ranked #1 on
Depth Completion
on KITTI Depth Completion
Current Status and Performance Analysis of Table Recognition in Document Images with Deep Neural Networks
Table understanding has substantially benefited from the recent breakthroughs in deep neural networks.
Guided Table Structure Recognition through Anchor Optimization
Subsequently, these anchors are exploited to locate the rows and columns in tabular images.
Recognizing Challenging Handwritten Annotations with Fully Convolutional Networks
This paper introduces a very challenging dataset of historic German documents and evaluates Fully Convolutional Neural Network (FCNN) based methods to locate handwritten annotations of any kind in these documents.
Real-Time Document Image Classification using Deep CNN and Extreme Learning Machines
This paper presents an approach for real-time training and testing for document image classification.
Cutting the Error by Half: Investigation of Very Deep CNN and Advanced Training Strategies for Document Image Classification
We present an exhaustive investigation of recent Deep Learning architectures, algorithms, and strategies for the task of document image classification to finally reduce the error by more than half.
Ranked #27 on
Document Image Classification
on RVL-CDIP
TAC-GAN - Text Conditioned Auxiliary Classifier Generative Adversarial Network
In this work, we present the Text Conditioned Auxiliary Classifier Generative Adversarial Network, (TAC-GAN) a text to image Generative Adversarial Network (GAN) for synthesizing images from their text descriptions.
Multilevel Context Representation for Improving Object Recognition
In this work, we propose the combined usage of low- and high-level blocks of convolutional neural networks (CNNs) for improving object recognition.
A Generic Method for Automatic Ground Truth Generation of Camera-captured Documents
The method is generic, language independent and can be used for generation of labeled documents datasets (both scanned and cameracaptured) in any cursive and non-cursive language, e. g., English, Russian, Arabic, Urdu, etc.
DeXpression: Deep Convolutional Neural Network for Expression Recognition
The proposed architecture achieves 99. 6% for CKP and 98. 63% for MMI, therefore performing better than the state of the art using CNNs.
Ranked #1 on
Facial Expression Recognition (FER)
on MMI
