1 citations · 1 across the 10 of their papers we have counts for
10 papers
TempoVLA: Learning Speed-Controllable Vision-Language-Action Policies
Dong Jing, Jingchen Nie, Tianqi Zhang +4
Robot manipulation alternates between low-risk transit phases that call for fast execution and high-risk contact stages that demand slow, precise motion. Yet existing Vision-Langua…
Learning Action Priors for Cross-embodiment Robot Manipulation
Dong Jing, Tianqi Zhang, Jiaqi Liu +5
Most Vision-Language-Action (VLA) models build on a Vision-Language Model (VLM) backbone by attaching an action module and optimizing the full policy jointly. This design inherits…
When Vision Overrides Language: Evaluating and Mitigating Counterfactual Failures in VLAs
Yu Fang, Yuchun Feng, Dong Jing +5
Vision-Language-Action models (VLAs) promise to ground language instructions in robot control, yet in practice often fail to faithfully follow language. When presented with instruc…
Say Cheese! Detail-Preserving Portrait Collection Generation via Natural Language Edits
Zelong Sun, Jiahui Wu, Ying Ba +2
As social media platforms proliferate, users increasingly demand intuitive ways to create diverse, high-quality portrait collections. In this work, we introduce Portrait Collection…
Mixture of Horizons in Action Chunking
Dong Jing, Gang Wang, Jiaqi Liu +7
Vision-language-action (VLA) models have shown remarkable capabilities in robotic manipulation, but their performance is sensitive to the used during…
Incentivizing Multimodal Reasoning in Large Models for Direct Robot Manipulation
Weiliang Tang, Dong Jing, Jia-Hui Pan +5
Recent Large Multimodal Models have demonstrated remarkable reasoning capabilities, especially in solving complex mathematical problems and realizing accurate spatial perception. O…
Bridging Writing Manner Gap in Visual Instruction Tuning by Creating LLM-aligned Instructions
Dong Jing, Nanyi Fei, Zhiwu Lu
In the realm of Large Multi-modal Models (LMMs), the instruction quality during the visual instruction tuning stage significantly influences the performance of modality alignment.…
CoTMR: Chain-of-Thought Multi-Scale Reasoning for Training-Free Zero-Shot Composed Image Retrieval
Zelong Sun, Dong Jing, Zhiwu Lu
Zero-Shot Composed Image Retrieval (ZS-CIR) aims to retrieve target images by integrating information from a composed query (reference image and modification text) without training…
Leveraging Large Vision-Language Model as User Intent-aware Encoder for Composed Image Retrieval
Zelong Sun, Dong Jing, Guoxing Yang +2
Composed Image Retrieval (CIR) aims to retrieve target images from candidate set using a hybrid-modality query consisting of a reference image and a relative caption that describes…
Adaptive Task Balancing for Visual Instruction Tuning via Inter-Task Contribution and Intra-Task Difficulty
Yanqi Dai, Yong Wang, Zebin You +3
Visual instruction tuning is a key training stage of large multimodal models. However, when learning multiple visual tasks simultaneously, this approach often results in suboptimal…