4 citations · 12 across the 14 of their papers we have counts for
16 papers
ArtSociety: Multi-Agent Multimodal Collaboration for Art Emotion Understanding
Jian Li, Fanfan Ji, Jinxiang Lai +5
The AffectiveArt Multidimensional Art Emotion Understanding task asks to jointly predict an artwork's fine-grained emotion (12 classes, 1549:1 head-to-tail ratio), binary valence/a…
WorldCraft: From Camera Navigation to Object Manipulation in Interactive Video World Models
Bohai Gu, Taiyi Wu, Yueyang Yuan +9
Recent video world models enable interactive camera navigation but provide limited control over individual objects. We study persistent object-state control in autoregressive video…
What You Think is What You See: Driving Exploration in VLM Agents via Visual-Linguistic Curiosity
Haoxi Li, Qinglin Hou, Jianfei Ma +6
To navigate partially observable visual environments, recent VLM agents increasingly internalize world modeling capabilities into their policies via explicit CoT reasoning, enablin…
VisionCreator-R1: A Reflection-Enhanced Native Visual-Generation Agentic Model
Jinxiang Lai, Wenzhe Zhao, Zexin Lu +7
Visual content generation has advanced from single-image to multi-image workflows, yet existing agents remain largely plan-driven and lack systematic reflection mechanisms to corre…
VisionCreator: A Native Visual-Generation Agentic Model with Understanding, Thinking, Planning and Creation
Jinxiang Lai, Zexin Lu, Jiajun He +11
Visual content creation tasks demand a nuanced understanding of design conventions and creative workflows-capabilities challenging for general models, while workflow-based agents l…
Dr.V: A Hierarchical Perception-Temporal-Cognition Framework to Diagnose Video Hallucination by Fine-grained Spatial-Temporal Grounding
Meng Luo, Shengqiong Wu, Liqiang Jing +12
Recent advancements in large video models (LVMs) have significantly enhance video understanding. However, these models continue to suffer from hallucinations, producing content tha…
Decoupling Classifier for Boosting Few-shot Object Detection and Instance Segmentation
Bin-Bin Gao, Xiaochen Chen, Zhongyi Huang +6
This paper focus on few-shot object detection~(FSOD) and instance segmentation~(FSIS), which requires a model to quickly adapt to novel classes with a few labeled instances. The ex…
BoxSeg: Quality-Aware and Peer-Assisted Learning for Box-supervised Instance Segmentation
Jinxiang Lai, Wenlong Wu, Jiawei Zhan +5
Box-supervised instance segmentation methods aim to achieve instance segmentation with only box annotations. Recent methods have demonstrated the effectiveness of acquiring high-qu…
Spider: Any-to-Many Multimodal LLM
Jinxiang Lai, Jie Zhang, Jun Liu +3
Multimodal LLMs (MLLMs) have emerged as an extension of Large Language Models (LLMs), enabling the integration of various modalities. However, Any-to-Any MLLMs are limited to gener…
Enhancing Multi-Class Anomaly Detection via Diffusion Refinement with Dual Conditioning
Jiawei Zhan, Jinxiang Lai, Bin-Bin Gao +3
Anomaly detection, the technique of identifying abnormal samples using only normal samples, has attracted widespread interest in industry. Existing one-model-per-category methods o…
MatchDet: A Collaborative Framework for Image Matching and Object Detection
Jinxiang Lai, Wenlong Wu, Bin-Bin Gao +5
Image matching and object detection are two fundamental and challenging tasks, while many related applications consider them two individual tasks (i.e. task-individual). In this pa…
Clustered-patch Element Connection for Few-shot Learning
Jinxiang Lai, Siqian Yang, Junhong Zhou +5
Weak feature representation problem has influenced the performance of few-shot classification task for a long time. To alleviate this problem, recent researchers build connections…
SpatialFormer: Semantic and Target Aware Attentions for Few-Shot Learning
Jinxiang Lai, Siqian Yang, Wenlong Wu +8
Recent Few-Shot Learning (FSL) methods put emphasis on generating a discriminative embedding features to precisely measure the similarity between support and query sets. Current CN…
Rethinking the Metric in Few-shot Learning: From an Adaptive Multi-Distance Perspective
Jinxiang Lai, Siqian Yang, Guannan Jiang +9
Few-shot learning problem focuses on recognizing unseen classes given a few labeled images. In recent effort, more attention is paid to fine-grained feature embedding, ignoring the…
tSF: Transformer-based Semantic Filter for Few-Shot Learning
Jinxiang Lai, Siqian Yang, Wenlong Liu +6
Few-Shot Learning (FSL) alleviates the data shortage challenge via embedding discriminative target-aware features among plenty seen (base) and few unseen (novel) labeled samples. M…
nVFNet-RDC: Replay and Non-Local Distillation Collaboration for Continual Object Detection
Jinxiang Lai, Wenlong Liu, Jun Liu
Continual Learning (CL) focuses on developing algorithms with the ability to adapt to new environments and learn new skills. This very challenging task has generated a lot of inter…