16 citations · 33 across the 17 of their papers we have counts for
18 papers
Structure-aware Prompt Adaptation from Seen to Unseen for Open-Vocabulary Compositional Zero-Shot Learning
Yihang Duan, Jiong Wang, Pengpeng Zeng +5
The goal of Open-Vocabulary Compositional Zero-Shot Learning (OV-CZSL) is to recognize attribute-object compositions in the open-vocabulary setting, where compositions of both seen…
TIMI: Training-Free Image-to-3D Multi-Instance Generation with Spatial Fidelity
Xiao Cai, Pengpeng Zeng, Ji Zhang +3
Precise spatial fidelity in Image-to-3D multi-instance generation is critical for downstream real-world applications. Recent work attempts to address this by fine-tuning pre-traine…
Benchmarking Few-shot Transferability of Pre-trained Models with Improved Evaluation Protocols
Xu Luo, Ji Zhang, Lianli Gao +2
Few-shot transfer has been revolutionized by stronger pre-trained models and improved adaptation algorithms.However, there lacks a unified, rigorous evaluation protocol that is bot…
Beyond the Majority: Long-tail Imitation Learning for Robotic Manipulation
Junhong Zhu, Ji Zhang, Jingkuan Song +2
While generalist robot policies hold significant promise for learning diverse manipulation skills through imitation, their performance is often hindered by the long-tail distributi…
Sim-and-Human Co-training for Data-Efficient and Scene-Generalizable Bimanual Manipulation
Kaipeng Fang, Weiqing Liang, Yuyang Li +5
Real-robot demonstrations are prohibitively expensive, while simulation data and real-world human demonstrations are both scalable but each leaves a distinct gap: simulation suffer…
Reversible Inversion for Training-Free Exemplar-guided Image Editing
Yuke Li, Lianli Gao, Ji Zhang +5
Exemplar-guided Image Editing (EIE) aims to modify a source image according to a visual reference. Existing approaches often require large-scale pre-training to learn relationships…
A Survey on Efficient Vision-Language-Action Models
Zhaoshu Yu, Bo Wang, Pengpeng Zeng +7
Vision-Language-Action models (VLAs) represent a significant frontier in embodied intelligence, aiming to bridge digital knowledge with physical-world interaction. Despite their re…
A Closer Look at Conditional Prompt Tuning for Vision-Language Models
Ji Zhang, Shihan Wu, Lianli Gao +3
Despite the great promise of Prompt Tuning (PT) in adapting large Vision-Language Pretrained Models (VLPMs) to downstream tasks, they often struggle to overcome the Base-New Tradeo…
Reliable Few-shot Learning under Dual Noises
Ji Zhang, Jingkuan Song, Lianli Gao +2
Recent advances in model pre-training give rise to task adaptation-based few-shot learning (FSL), where the goal is to adapt a pre-trained task-agnostic model for capturing task-sp…
InSpire: Vision-Language-Action Models with Intrinsic Spatial Reasoning
Ji Zhang, Shihan Wu, Xu Luo +4
Leveraging pretrained Vision-Language Models (VLMs) to map language instruction and visual observations to raw low-level actions, Vision-Language-Action models (VLAs) hold great pr…
Policy Contrastive Decoding for Robotic Foundation Models
Shihan Wu, Xu Luo, Ji Zhang +4
Robotic foundation models, or generalist robot policies, hold immense potential to enable flexible, general-purpose and dexterous robotic systems. Despite their advancements, our e…
Skip Tuning: Pre-trained Vision-Language Models are Effective and Efficient Adapters Themselves
Shihan Wu, Ji Zhang, Pengpeng Zeng +3
Prompt tuning (PT) has long been recognized as an effective and efficient paradigm for transferring large pre-trained vision-language models (VLMs) to downstream tasks by learning…
Class Gradient Projection For Continual Learning
Cheng Chen, Ji Zhang, Jingkuan Song +1
Catastrophic forgetting is one of the most critical challenges in Continual Learning (CL). Recent approaches tackle this problem by projecting the gradient update orthogonal to the…
From Channel Bias to Feature Redundancy: Uncovering the "Less is More" Principle in Few-Shot Learning
Ji Zhang, Xu Luo, Lianli Gao +3
Deep neural networks often fail to adapt representations to novel tasks under distribution shifts, especially when only a few examples are available. This paper identifies a core o…
DePT: Decoupled Prompt Tuning
Ji Zhang, Shihan Wu, Lianli Gao +2
This work breaks through the Base-New Tradeoff (BNT)dilemma in prompt tuning, i.e., the better the tuned model generalizes to the base (or target) task, the worse it generalizes to…
From Global to Local: Multi-scale Out-of-distribution Detection
Ji Zhang, Lianli Gao, Bingguang Hao +3
Out-of-distribution (OOD) detection aims to detect "unknown" data whose labels have not been seen during the in-distribution (ID) training process. Recent progress in representatio…
DETA: Denoised Task Adaptation for Few-Shot Learning
Ji Zhang, Lianli Gao, Xu Luo +2
Test-time task adaptation in few-shot learning aims to adapt a pre-trained task-agnostic model for capturing taskspecific knowledge of the test task, rely only on few-labeled suppo…
A Closer Look at Few-shot Classification Again
Xu Luo, Hao Wu, Ji Zhang +3
Few-shot classification consists of a training phase where a model is learned on a relatively large dataset and an adaptation phase where the learned model is adapted to previously…