3 citations · 6 across the 24 of their papers we have counts for
16 papers · 1 filter
MemVLN: Episodic and Procedural Memory for Vision-and-Language Navigation
Yuqi Liu, Shengju Qian, Tianyuan Qu +5
Vision-and-Language Navigation in Continuous Environments (VLN-CE) requires agents to maintain long-horizon visual history for trajectory consistency while executing actions with l…
RePlan: Reasoning-guided Region Planning for Complex Instruction-based Image Editing
Tianyuan Qu, Lei Ke, Xiaohang Zhan +6
Instruction-based image editing enables natural-language control over visual modifications, yet existing models falter under Instruction-Visual Complexity (IV-Complexity), where in…
DreamOmni2: Multimodal Instruction-based Editing and Generation
Bin Xia, Bohao Peng, Yuechen Zhang +10
Recent advancements in instruction-based image editing and subject-driven generation have garnered significant attention, yet both tasks still face limitations in meeting practical…
ViSurf: Visual Supervised-and-Reinforcement Fine-Tuning for Large Vision-and-Language Models
Yuqi Liu, Liangyu Chen, Jiazhen Liu +4
Post-training Large Vision-and-Language Models (LVLMs) typically involves Supervised Fine-Tuning (SFT) for knowledge injection or Reinforcement Learning with Verifiable Rewards (RL…
VisionThink: Smart and Efficient Vision Language Model via Reinforcement Learning
Senqiao Yang, Junyi Li, Xin Lai +3
Recent advancements in vision-language models (VLMs) have improved performance by increasing the number of visual tokens, which are often significantly longer than text tokens. How…
RTime-QA: A Benchmark for Atomic Temporal Event Understanding in Large Multi-modal Models
Yuqi Liu, Qin Jin, Tianyuan Qu +4
Understanding accurate atomic temporal event is essential for video comprehension. However, current video-language benchmarks often fall short to evaluate Large Multi-modal Models'…
VisionReasoner: Unified Reasoning-Integrated Visual Perception via Reinforcement Learning
Yuqi Liu, Tianyuan Qu, Zhisheng Zhong +4
Large vision-language models exhibit inherent capabilities to handle diverse visual perception tasks. In this paper, we introduce VisionReasoner, a unified framework capable of rea…
Does Your Vision-Language Model Get Lost in the Long Video Sampling Dilemma?
Tianyuan Qu, Longxiang Tang, Bohao Peng +3
The rise of Large Vision-Language Models (LVLMs) has significantly advanced video understanding. However, efficiently processing long videos remains a challenge due to the ``Sampli…
Modular Customization of Diffusion Models via Blockwise-Parameterized Low-Rank Adaptation
Mingkang Zhu, Xi Chen, Zhongdao Wang +3
Recent diffusion model customization has shown impressive results in incorporating subject or style concepts with a handful of images. However, the modular composition of multiple…
Seg-Zero: Reasoning-Chain Guided Segmentation via Cognitive Reinforcement
Yuqi Liu, Bohao Peng, Zhisheng Zhong +4
Traditional methods for reasoning segmentation rely on supervised fine-tuning with categorical labels and simple descriptions, limiting its out-of-domain generalization and lacking…
VisionZip: Longer is Better but Not Necessary in Vision Language Models
Senqiao Yang, Yukang Chen, Zhuotao Tian +4
Recent advancements in vision-language models have enhanced performance by increasing the length of visual tokens, making them much longer than text tokens and significantly raisin…
LogoSticker: Inserting Logos into Diffusion Models for Customized Generation
Mingkang Zhu, Xi Chen, Zhongdao Wang +2
Recent advances in text-to-image model customization have underscored the importance of integrating new concepts with a few examples. Yet, these progresses are largely confined to…
Mind the Interference: Retaining Pre-trained Knowledge in Parameter Efficient Continual Learning of Vision-Language Models
Longxiang Tang, Zhuotao Tian, Kai Li +5
This study addresses the Domain-Class Incremental Learning problem, a realistic but challenging continual learning scenario where both the domain distribution and target classes va…
Unified Language-driven Zero-shot Domain Adaptation
Senqiao Yang, Zhuotao Tian, Li Jiang +1
This paper introduces Unified Language-driven Zero-shot Domain Adaptation (ULDA), a novel task setting that enables a single model to adapt to diverse target domains without explic…
OA-CNNs: Omni-Adaptive Sparse CNNs for 3D Semantic Segmentation
Bohao Peng, Xiaoyang Wu, Li Jiang +4
The booming of 3D recognition in the 2020s began with the introduction of point cloud transformers. They quickly overwhelmed sparse CNNs and became state-of-the-art models, especia…
GroupContrast: Semantic-aware Self-supervised Representation Learning for 3D Understanding
Chengyao Wang, Li Jiang, Xiaoyang Wu +4
Self-supervised 3D representation learning aims to learn effective representations from large-scale unlabeled point clouds. Most existing approaches adopt point discrimination as t…