16 citations · 36 across the 17 of their papers we have counts for
24 papers · 1 filter
WinARDet: Compressed Window Auto-Regression for Long-Horizon Vision-Language Grounding
Chenxu Wang, Hangyi Kuang, Bo-Wen Yin +6
MLLM-based detectors formulate vision-language grounding as autoregressive coordinate generation, yet in dense scenes, they often collapse into a box repetition loop, repeatedly em…
OPUS-V2: Bridging the Gap between Sparse Points and Dense Voxels
Jiabao Wang, Qiang Meng, Liujiang Yan +3
The point-based occupancy prediction paradigm has achieved an attractive trade-off between accuracy and efficiency by modeling 3D space sparsely. However, its predictions inherentl…
Rethinking Token-Level Policy Optimization for Multimodal Chain-of-Thought
Yunheng Li, Hangyi Kuang, Hengrui Zhang +4
Multimodal Chain-of-Thought (CoT) reasoning requires large vision-language models to construct reasoning trajectories that interleave perceptual grounding with multi-step inference…
Towards Universal Video MLLMs with Attribute-Structured and Quality-Verified Instructions
Yunheng Li, Hengrui Zhang, Meng-Hao Guo +5
Universal video understanding requires modeling fine-grained visual and audio information over time in diverse real-world scenarios. However, the performance of existing models is…
GeoWorld: Providing Full-frame Geometry Features to Facilitate 3D Scene Generation
Yuhao Wan, Lijuan Liu, Jingzhi Zhou +6
Previous works that leverage video models for image-to-3D scene generation often suffer from geometric distortions and blurry content. Using video generation models to implicitly m…
TempSamp-R1: Effective Temporal Sampling with Reinforcement Fine-Tuning for Video LLMs
Yunheng Li, Jing Cheng, Shaoyong Jia +4
This paper introduces TempSamp-R1, a new reinforcement fine-tuning framework designed to improve the effectiveness of adapting multimodal large language models (MLLMs) to video tem…
OmniSegmentor: A Flexible Multi-Modal Learning Framework for Semantic Segmentation
Bo-Wen Yin, Jiao-Long Cao, Xuying Zhang +3
Recent research on representation learning has proved the merits of multi-modal clues for robust semantic segmentation. Nevertheless, a flexible pretrain-and-finetune pipeline for…
Revisiting Efficient Semantic Segmentation: Learning Offsets for Better Spatial and Class Feature Alignment
Shi-Chen Zhang, Yunheng Li, Yu-Huan Wu +2
Semantic segmentation is fundamental to vision systems requiring pixel-level scene understanding, yet deploying it on resource-constrained devices demands efficient architectures.…
Docopilot: Improving Multimodal Models for Document-Level Understanding
Yuchen Duan, Zhe Chen, Yusong Hu +9
Despite significant progress in multimodal large language models (MLLMs), their performance on complex, multi-page document comprehension remains inadequate, largely due to the lac…
DFormerv2: Geometry Self-Attention for RGBD Semantic Segmentation
Bo-Wen Yin, Jiao-Long Cao, Ming-Ming Cheng +1
Recent advances in scene understanding benefit a lot from depth maps because of the 3D geometry information, especially in complex conditions (e.g., low light and overexposed). Exi…
Re-Aligning Language to Visual Objects with an Agentic Workflow
Yuming Chen, Jiangyan Feng, Haodong Zhang +6
Language-based object detection (LOD) aims to align visual objects with language expressions. A large amount of paired data is utilized to improve LOD model generalizations. During…
KAC: Kolmogorov-Arnold Classifier for Continual Learning
Yusong Hu, Zichen Liang, Fei Yang +3
Continual learning requires models to train continuously across consecutive tasks without forgetting. Most existing methods utilize linear classifiers, which struggle to maintain a…
Strip R-CNN: Large Strip Convolution for Remote Sensing Object Detection
Xinbin Yuan, Zhaohui Zheng, Yuxuan Li +5
While witnessed with rapid development, remote sensing object detection remains challenging for detecting high aspect ratio objects. This paper shows that large strip convolutions…
SM3Det: A Unified Model for Multi-Modal Remote Sensing Object Detection
Yuxuan Li, Xiang Li, Yunheng Li +5
With the rapid advancement of remote sensing technology, high-resolution multi-modal imagery is now more widely accessible. Conventional Object detection models are trained on a si…
TAR3D: Creating High-Quality 3D Assets via Next-Part Prediction
Xuying Zhang, Yutong Liu, Yangguang Li +8
We present TAR3D, a novel framework that consists of a 3D-aware Vector Quantized-Variational AutoEncoder (VQ-VAE) and a Generative Pre-trained Transformer (GPT) to generate high-qu…
High-Quality Mask Tuning Matters for Open-Vocabulary Segmentation
Quan-Sheng Zeng, Yunheng Li, Daquan Zhou +3
Open-vocabulary image segmentation has been advanced through the synergy between mask generators and vision-language models like Contrastive Language-Image Pre-training (CLIP). Pre…
Unbiased Region-Language Alignment for Open-Vocabulary Dense Prediction
Yunheng Li, Yuxuan Li, Quansheng Zeng +3
Pre-trained vision-language models (VLMs), such as CLIP, have demonstrated impressive zero-shot recognition capability, but still underperform in dense prediction tasks. Self-disti…
OPUS: Occupancy Prediction Using a Sparse Set
Jiabao Wang, Zhaojiang Liu, Qiang Meng +6
Occupancy prediction, aiming at predicting the occupancy status within voxelized 3D environment, is quickly gaining momentum within the autonomous driving community. Mainstream occ…
Towards Stable 3D Object Detection
Jiabao Wang, Qiang Meng, Guochao Liu +4
In autonomous driving, the temporal stability of 3D object detection greatly impacts the driving safety. However, the detection stability cannot be accessed by existing metrics suc…
Cascade-CLIP: Cascaded Vision-Language Embeddings Alignment for Zero-Shot Semantic Segmentation
Yunheng Li, ZhongYu Li, Quansheng Zeng +2
Pre-trained vision-language models, e.g., CLIP, have been successfully applied to zero-shot semantic segmentation. Existing CLIP-based approaches primarily utilize visual features…
LSKNet: A Foundation Lightweight Backbone for Remote Sensing
Yuxuan Li, Xiang Li, Yimian Dai +5
Remote sensing images pose distinct challenges for downstream tasks due to their inherent complexity. While a considerable amount of research has been dedicated to remote sensing c…
SARDet-100K: Towards Open-Source Benchmark and ToolKit for Large-Scale SAR Object Detection
Yuxuan Li, Xiang Li, Weijie Li +4
Synthetic Aperture Radar (SAR) object detection has gained significant attention recently due to its irreplaceable all-weather imaging capabilities. However, this research field su…
Fast Window-Based Event Denoising with Spatiotemporal Correlation Enhancement
Huachen Fang, Jinjian Wu, Qibin Hou +2
Previous deep learning-based event denoising methods mostly suffer from poor interpretability and difficulty in real-time processing due to their complex architecture designs. In t…
Get What You Want, Not What You Don't: Image Content Suppression for Text-to-Image Diffusion Models
Senmao Li, Joost van de Weijer, Taihang Hu +4
The success of recent text-to-image diffusion models is largely due to their capacity to be guided by a complex text prompt, which enables users to precisely describe the desired c…