21 citations · 28 across the 22 of their papers we have counts for
11 papers · 1 filter
The Consistency Critic: Correcting Inconsistencies in Generated Images via Reference-Guided Attentive Alignment
Ziheng Ouyang, Yiren Song, Yaoli Liu +4
Previous works have explored various customized generation tasks given a reference image, but they still face limitations in generating consistent fine-grained details. In this pap…
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…
OneVAE: Joint Discrete and Continuous Optimization Helps Discrete Video VAE Train Better
Yupeng Zhou, Zhen Li, Ziheng Ouyang +8
Encoding videos into discrete tokens could align with text tokens to facilitate concise and unified multi-modal LLMs, yet introducing significant spatiotemporal compression compare…
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.…
A Glimpse to Compress: Dynamic Visual Token Pruning for Large Vision-Language Models
Quan-Sheng Zeng, Yunheng Li, Qilong Wang +4
Visual token compression is critical for Large Vision-Language Models (LVLMs) to efficiently process high-resolution inputs. Existing methods that typically adopt fixed compression…
Point-Cache: Test-time Dynamic and Hierarchical Cache for Robust and Generalizable Point Cloud Analysis
Hongyu Sun, Qiuhong Ke, Ming Cheng +4
This paper proposes a general solution to enable point cloud recognition models to handle distribution shifts at test time. Unlike prior methods, which rely heavily on training dat…
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…