512 citations · 761 across the 20 of their papers we have counts for
20 papers
Turbocharging Web Automation: The Impact of Compressed History States
Xiyue Zhu, Peng Tang, Haofu Liao +1
Language models have led to a leap forward in web automation. The current web automation approaches take the current web state, history actions, and language instruction as inputs…
R-VLM: Region-Aware Vision Language Model for Precise GUI Grounding
Joonhyung Park, Peng Tang, Sagnik Das +4
Visual agent models for automating human activities on Graphical User Interfaces (GUIs) have emerged as a promising research direction, driven by advances in large Vision Language…
DocKD: Knowledge Distillation from LLMs for Open-World Document Understanding Models
Sungnyun Kim, Haofu Liao, Srikar Appalaraju +6
Visual document understanding (VDU) is a challenging task that involves understanding documents across various modalities (text and image) and layouts (forms, tables, etc.). This s…
Synthesize Step-by-Step: Tools, Templates and LLMs as Data Generators for Reasoning-Based Chart VQA
Zhuowan Li, Bhavan Jasani, Peng Tang +1
Understanding data visualizations like charts and plots requires reasoning about both visual elements and numerics. Although strong in extractive questions, current chart visual qu…
DEED: Dynamic Early Exit on Decoder for Accelerating Encoder-Decoder Transformer Models
Peng Tang, Pengkai Zhu, Tian Li +3
Encoder-decoder transformer models have achieved great success on various vision-language (VL) tasks, but they suffer from high inference latency. Typically, the decoder takes up m…
Multiple-Question Multiple-Answer Text-VQA
Peng Tang, Srikar Appalaraju, R. Manmatha +2
We present Multiple-Question Multiple-Answer (MQMA), a novel approach to do text-VQA in encoder-decoder transformer models. The text-VQA task requires a model to answer a question…
DocFormerv2: Local Features for Document Understanding
Srikar Appalaraju, Peng Tang, Qi Dong +3
We propose DocFormerv2, a multi-modal transformer for Visual Document Understanding (VDU). The VDU domain entails understanding documents (beyond mere OCR predictions) e.g., extrac…
Learning Inductive Attention Guidance for Partially Supervised Pancreatic Ductal Adenocarcinoma Prediction
Yan Wang, Peng Tang, Yuyin Zhou +3
Pancreatic ductal adenocarcinoma (PDAC) is the third most common cause of cancer death in the United States. Predicting tumors like PDACs (including both classification and segment…
Shape-Texture Debiased Neural Network Training
Yingwei Li, Qihang Yu, Mingxing Tan +5
Shape and texture are two prominent and complementary cues for recognizing objects. Nonetheless, Convolutional Neural Networks are often biased towards either texture or shape, dep…
Look Closer to Ground Better: Weakly-Supervised Temporal Grounding of Sentence in Video
Zhenfang Chen, Lin Ma, Wenhan Luo +2
In this paper, we study the problem of weakly-supervised temporal grounding of sentence in video. Specifically, given an untrimmed video and a query sentence, our goal is to locali…
Proposal Learning for Semi-Supervised Object Detection
Peng Tang, Chetan Ramaiah, Yan Wang +2
In this paper, we focus on semi-supervised object detection to boost performance of proposal-based object detectors (a.k.a. two-stage object detectors) by training on both labeled…
Robustness of Object Recognition under Extreme Occlusion in Humans and Computational Models
Hongru Zhu, Peng Tang, Jeongho Park +2
Most objects in the visual world are partially occluded, but humans can recognize them without difficulty. However, it remains unknown whether object recognition models like convol…
PCL: Proposal Cluster Learning for Weakly Supervised Object Detection
Peng Tang, Xinggang Wang, Song Bai +4
Weakly Supervised Object Detection (WSOD), using only image-level annotations to train object detectors, is of growing importance in object recognition. In this paper, we propose a…
Training Multi-organ Segmentation Networks with Sample Selection by Relaxed Upper Confident Bound
Yan Wang, Yuyin Zhou, Peng Tang +3
Deep convolutional neural networks (CNNs), especially fully convolutional networks, have been widely applied to automatic medical image segmentation problems, e.g., multi-organ seg…
Semi-Supervised Multi-Organ Segmentation via Deep Multi-Planar Co-Training
Yuyin Zhou, Yan Wang, Peng Tang +4
In multi-organ segmentation of abdominal CT scans, most existing fully supervised deep learning algorithms require lots of voxel-wise annotations, which are usually difficult, expe…
Object Detection in Videos by High Quality Object Linking
Peng Tang, Chunyu Wang, Xinggang Wang +3
Compared with object detection in static images, object detection in videos is more challenging due to degraded image qualities. An effective way to address this problem is to expl…
Deep Patch Learning for Weakly Supervised Object Classification and Discovery
Peng Tang, Xinggang Wang, Zilong Huang +2
Patch-level image representation is very important for object classification and detection, since it is robust to spatial transformation, scale variation, and cluttered background.…
Multiple Instance Detection Network with Online Instance Classifier Refinement
Peng Tang, Xinggang Wang, Xiang Bai +1
Of late, weakly supervised object detection is with great importance in object recognition. Based on deep learning, weakly supervised detectors have achieved many promising results…
Revisiting Multiple Instance Neural Networks
Xinggang Wang, Yongluan Yan, Peng Tang +2
Recently neural networks and multiple instance learning are both attractive topics in Artificial Intelligence related research fields. Deep neural networks have achieved great succ…
Deep FisherNet for Object Classification
Peng Tang, Xinggang Wang, Baoguang Shi +3
Despite the great success of convolutional neural networks (CNN) for the image classification task on datasets like Cifar and ImageNet, CNN's representation power is still somewhat…