9 citations · 33 across the 18 of their papers we have counts for
19 papers
Self-Ensembling Vision-Language Models for Chart Data Extraction
Thomas Berkane, Qianyi Wang, Maimuna S. Majumder
Charts effectively convey quantitative information, but the underlying data are often locked in image form, hindering reuse and analysis. Manually digitizing charts is time-consumi…
EgoIntrospect: An Egocentric Dataset and Benchmark for User-Centric Internal State Reasoning
Zeyu Wang, Chang Liu, Eduardus Tjitrahardja +22
Despite extensive efforts on egocentric video datasets and benchmarks, understanding users' internal states, which is crucial for enabling seamless AI assistant experiences, remain…
Unleashing the Potential of Model Bias for Generalized Category Discovery
Wenbin An, Haonan Lin, Jiahao Nie +5
Generalized Category Discovery is a significant and complex task that aims to identify both known and undefined novel categories from a set of unlabeled data, leveraging another la…
Schedule Your Edit: A Simple yet Effective Diffusion Noise Schedule for Image Editing
Haonan Lin, Mengmeng Wang, Jiahao Wang +7
Text-guided diffusion models have significantly advanced image editing, enabling high-quality and diverse modifications driven by text prompts. However, effective editing requires…
Flipped Classroom: Aligning Teacher Attention with Student in Generalized Category Discovery
Haonan Lin, Wenbin An, Jiahao Wang +6
Recent advancements have shown promise in applying traditional Semi-Supervised Learning strategies to the task of Generalized Category Discovery (GCD). Typically, this involves a t…
GoT-CQA: Graph-of-Thought Guided Compositional Reasoning for Chart Question Answering
Lingling Zhang, Muye Huang, QianYing Wang +3
Chart Question Answering (CQA) aims at answering questions based on the visual chart content, which plays an important role in chart sumarization, business data analysis, and data…
Knowledge Acquisition Disentanglement for Knowledge-based Visual Question Answering with Large Language Models
Wenbin An, Feng Tian, Jiahao Nie +7
Knowledge-based Visual Question Answering (KVQA) requires both image and world knowledge to answer questions. Current methods first retrieve knowledge from the image and external k…
Mitigating Object Hallucinations in Large Vision-Language Models with Assembly of Global and Local Attention
Wenbin An, Feng Tian, Sicong Leng +6
Despite great success across various multimodal tasks, Large Vision-Language Models (LVLMs) often encounter object hallucinations with generated textual responses being inconsisten…
OmniHands: Towards Robust 4D Hand Mesh Recovery via A Versatile Transformer
Dixuan Lin, Yuxiang Zhang, Mengcheng Li +5
In this paper, we introduce OmniHands, a universal approach to recovering interactive hand meshes and their relative movement from monocular or multi-view inputs. Our approach addr…
DreamSalon: A Staged Diffusion Framework for Preserving Identity-Context in Editable Face Generation
Haonan Lin, Mengmeng Wang, Yan Chen +6
While large-scale pre-trained text-to-image models can synthesize diverse and high-quality human-centered images, novel challenges arise with a nuanced task of "identity fine editi…
Transfer and Alignment Network for Generalized Category Discovery
Wenbin An, Feng Tian, Wenkai Shi +4
Generalized Category Discovery is a crucial real-world task. Despite the improved performance on known categories, current methods perform poorly on novel categories. We attribute…
Generalized Category Discovery with Large Language Models in the Loop
Wenbin An, Wenkai Shi, Feng Tian +8
Generalized Category Discovery (GCD) is a crucial task that aims to recognize both known and novel categories from a set of unlabeled data by utilizing a few labeled data with only…
A Diffusion Weighted Graph Framework for New Intent Discovery
Wenkai Shi, Wenbin An, Feng Tian +3
New Intent Discovery (NID) aims to recognize both new and known intents from unlabeled data with the aid of limited labeled data containing only known intents. Without considering…
DNA: Denoised Neighborhood Aggregation for Fine-grained Category Discovery
Wenbin An, Feng Tian, Wenkai Shi +4
Discovering fine-grained categories from coarsely labeled data is a practical and challenging task, which can bridge the gap between the demand for fine-grained analysis and the hi…
Generalized Category Discovery with Decoupled Prototypical Network
Wenbin An, Feng Tian, Qinghua Zheng +3
Generalized Category Discovery (GCD) aims to recognize both known and novel categories from a set of unlabeled data, based on another dataset labeled with only known categories. Wi…
Fine-grained Category Discovery under Coarse-grained supervision with Hierarchical Weighted Self-contrastive Learning
Wenbin An, Feng Tian, Ping Chen +3
Novel category discovery aims at adapting models trained on known categories to novel categories. Previous works only focus on the scenario where known and novel categories are of…
MatchVIE: Exploiting Match Relevancy between Entities for Visual Information Extraction
Guozhi Tang, Lele Xie, Lianwen Jin +6
Visual Information Extraction (VIE) task aims to extract key information from multifarious document images (e.g., invoices and purchase receipts). Most previous methods treat the V…
Towards Robust Visual Information Extraction in Real World: New Dataset and Novel Solution
Jiapeng Wang, Chongyu Liu, Lianwen Jin +6
Visual information extraction (VIE) has attracted considerable attention recently owing to its various advanced applications such as document understanding, automatic marking and i…
Decoupled Attention Network for Text Recognition
Tianwei Wang, Yuanzhi Zhu, Lianwen Jin +5
Text recognition has attracted considerable research interests because of its various applications. The cutting-edge text recognition methods are based on attention mechanisms. How…