84 citations · 172 across the 43 of their papers we have counts for
37 papers · 1 filter
BridgeAfford: Bridging Query-Target Gap for Generalizable 3D Affordance Grounding
Yijie Su, Yakun Zhu, Yi Bin +5
Language-guided 3D affordance grounding aims to localize functional regions on 3D objects according to interaction intents. However, existing methods exhibit poor generalization to…
Janus-LoRA: A Balanced Low-Rank Adaptation for Continual Learning
Cheng Chen, Pengpeng Zeng, Yuyu Guo +3
Low-Rank Adaptation (LoRA) has emerged as a promising paradigm for Continual Learning. It independently updates its low-rank factors ( and ), creating a composite update to t…
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 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…
Generalizing vision-language models to novel domains: A comprehensive survey
Xinyao Li, Jingjing Li, Fengling Li +3
Recently, vision-language pretraining has emerged as a transformative technique that integrates the strengths of both visual and textual modalities, resulting in powerful vision-la…
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…
Truth in the Few: High-Value Data Selection for Efficient Multi-Modal Reasoning
Shenshen Li, Xing Xu, Kaiyuan Deng +3
While multi-modal large language models (MLLMs) have made significant progress in complex reasoning tasks via reinforcement learning, it is commonly believed that extensive trainin…
Towards Generalized and Training-Free Text-Guided Semantic Manipulation
Yu Hong, Xiao Cai, Pengpeng Zeng +4
Text-guided semantic manipulation refers to semantically editing an image generated from a source prompt to match a target prompt, enabling the desired semantic changes (e.g., addi…
Attention Hijackers: Detect and Disentangle Attention Hijacking in LVLMs for Hallucination Mitigation
Beitao Chen, Xinyu Lyu, Lianli Gao +2
Despite their success, Large Vision-Language Models (LVLMs) remain vulnerable to hallucinations. While existing studies attribute the cause of hallucinations to insufficient visual…
CFReID: Continual Few-shot Person Re-Identification
Hao Ni, Lianli Gao, Pengpeng Zeng +2
Real-world surveillance systems are dynamically evolving, requiring a person Re-identification model to continuously handle newly incoming data from various domains. To cope with t…
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…
GT23D-Bench: A Comprehensive General Text-to-3D Generation Benchmark
Xiao Cai, Sitong Su, Jingkuan Song +6
Text-to-3D (T23D) generation has emerged as a crucial visual generation task, aiming at synthesizing 3D content from textual descriptions. Studies of this task are currently shifti…
SeMv-3D: Towards Concurrency of Semantic and Multi-view Consistency in General Text-to-3D Generation
Xiao Cai, Pengpeng Zeng, Lianli Gao +3
General Text-to-3D (GT23D) generation is crucial for creating diverse 3D content across objects and scenes, yet it faces two key challenges: 1) ensuring semantic consistency betwee…
VQ-Flow: Taming Normalizing Flows for Multi-Class Anomaly Detection via Hierarchical Vector Quantization
Yixuan Zhou, Xing Xu, Zhe Sun +3
Normalizing flows, a category of probabilistic models famed for their capabilities in modeling complex data distributions, have exhibited remarkable efficacy in unsupervised anomal…
Text-Video Retrieval with Global-Local Semantic Consistent Learning
Haonan Zhang, Pengpeng Zeng, Lianli Gao +4
Adapting large-scale image-text pre-training models, e.g., CLIP, to the video domain represents the current state-of-the-art for text-video retrieval. The primary approaches involv…
CoIN: A Benchmark of Continual Instruction tuNing for Multimodel Large Language Model
Cheng Chen, Junchen Zhu, Xu Luo +3
Instruction tuning represents a prevalent strategy employed by Multimodal Large Language Models (MLLMs) to align with human instructions and adapt to new tasks. Nevertheless, MLLMs…
ProS: Prompting-to-simulate Generalized knowledge for Universal Cross-Domain Retrieval
Kaipeng Fang, Jingkuan Song, Lianli Gao +4
The goal of Universal Cross-Domain Retrieval (UCDR) is to achieve robust performance in generalized test scenarios, wherein data may belong to strictly unknown domains and categori…
MotionZero:Exploiting Motion Priors for Zero-shot Text-to-Video Generation
Sitong Su, Litao Guo, Lianli Gao +2
Zero-shot Text-to-Video synthesis generates videos based on prompts without any videos. Without motion information from videos, motion priors implied in prompts are vital guidance.…
Continual Referring Expression Comprehension via Dual Modular Memorization
Heng Tao Shen, Cheng Chen, Peng Wang +3
Referring Expression Comprehension (REC) aims to localize an image region of a given object described by a natural-language expression. While promising performance has been demonst…
Prototype-based Aleatoric Uncertainty Quantification for Cross-modal Retrieval
Hao Li, Jingkuan Song, Lianli Gao +2
Cross-modal Retrieval methods build similarity relations between vision and language modalities by jointly learning a common representation space. However, the predictions are ofte…
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…
CIParsing: Unifying Causality Properties into Multiple Human Parsing
Xiaojia Chen, Xuanhan Wang, Lianli Gao +3
Existing methods of multiple human parsing (MHP) apply statistical models to acquire underlying associations between images and labeled body parts. However, acquired associations o…
ImbSAM: A Closer Look at Sharpness-Aware Minimization in Class-Imbalanced Recognition
Yixuan Zhou, Yi Qu, Xing Xu +1
Class imbalance is a common challenge in real-world recognition tasks, where the majority of classes have few samples, also known as tail classes. We address this challenge with th…
Generalized Unbiased Scene Graph Generation
Xinyu Lyu, Lianli Gao, Junlin Xie +4
Existing Unbiased Scene Graph Generation (USGG) methods only focus on addressing the predicate-level imbalance that high-frequency classes dominate predictions of rare ones, while…