3 citations · 4 across the 9 of their papers we have counts for
11 papers · 1 filter
Stream3D-VLM: Online 3D Spatial Understanding with Incremental Geometry Priors
Hanxun Yu, Xuan Qu, Lei Ke +4
Despite advances in 3D scene understanding, existing 3D Large Multimodal Models operate in offline settings, requiring complete scene observations or predefined video clips. In thi…
Penguin-VL: Exploring the Efficiency Limits of VLM with LLM-based Vision Encoders
Boqiang Zhang, Lei Ke, Ruihan Yang +5
Vision Language Model (VLM) development has largely relied on scaling model size, which hinders deployment on compute-constrained mobile and edge devices such as smartphones and ro…
Boosting Segment Anything Model to Generalize Visually Non-Salient Scenarios
Guangqian Guo, Pengfei Chen, Yong Guo +3
Segment Anything Model (SAM), known for its remarkable zero-shot segmentation capabilities, has garnered significant attention in the community. Nevertheless, its performance is ch…
N3D-VLM: Native 3D Grounding Enables Accurate Spatial Reasoning in Vision-Language Models
Yuxin Wang, Lei Ke, Boqiang Zhang +6
While current multimodal models can answer questions based on 2D images, they lack intrinsic 3D object perception, limiting their ability to comprehend spatial relationships and de…
CAPability: A Comprehensive Visual Caption Benchmark for Evaluating Both Correctness and Thoroughness
Zhihang Liu, Chen-Wei Xie, Bin Wen +9
Visual captioning benchmarks have become outdated with the emergence of modern multimodal large language models (MLLMs), as the brief ground-truth sentences and traditional metrics…
How Control Information Influences Multilingual Text Image Generation and Editing?
Boqiang Zhang, Zuan Gao, Yadong Qu +1
Visual text generation has significantly advanced through diffusion models aimed at producing images with readable and realistic text. Recent works primarily use a ControlNet-based…
Focus on the Whole Character: Discriminative Character Modeling for Scene Text Recognition
Bangbang Zhou, Yadong Qu, Zixiao Wang +3
Recently, scene text recognition (STR) models have shown significant performance improvements. However, existing models still encounter difficulties in recognizing challenging text…
Self-Supervised Pre-training with Symmetric Superimposition Modeling for Scene Text Recognition
Zuan Gao, Yuxin Wang, Yadong Qu +4
In text recognition, self-supervised pre-training emerges as a good solution to reduce dependence on expansive annotated real data. Previous studies primarily focus on local visual…
Choose What You Need: Disentangled Representation Learning for Scene Text Recognition, Removal and Editing
Boqiang Zhang, Hongtao Xie, Zuan Gao +1
Scene text images contain not only style information (font, background) but also content information (character, texture). Different scene text tasks need different information, bu…
Symmetrical Linguistic Feature Distillation with CLIP for Scene Text Recognition
Zixiao Wang, Hongtao Xie, Yuxin Wang +3
In this paper, we explore the potential of the Contrastive Language-Image Pretraining (CLIP) model in scene text recognition (STR), and establish a novel Symmetrical Linguistic Fea…
Linguistic More: Taking a Further Step toward Efficient and Accurate Scene Text Recognition
Boqiang Zhang, Hongtao Xie, Yuxin Wang +2
Vision model have gained increasing attention due to their simplicity and efficiency in Scene Text Recognition (STR) task. However, due to lacking the perception of linguistic know…