382 citations · 455 across the 16 of their papers we have counts for
17 papers
Adaptive Keyframe Sampling for Long Video Understanding
Xi Tang, Jihao Qiu, Lingxi Xie +3
Multimodal large language models (MLLMs) have enabled open-world visual understanding by injecting visual input as extra tokens into large language models (LLMs) as contexts. Howev…
YOLOv12: Attention-Centric Real-Time Object Detectors
Yunjie Tian, Qixiang Ye, David Doermann
Enhancing the network architecture of the YOLO framework has been crucial for a long time, but has focused on CNN-based improvements despite the proven superiority of attention mec…
Personalized Large Vision-Language Models
Chau Pham, Hoang Phan, David Doermann +1
The personalization model has gained significant attention in image generation yet remains underexplored for large vision-language models (LVLMs). Beyond generic ones, with persona…
ClawMachine: Learning to Fetch Visual Tokens for Referential Comprehension
Tianren Ma, Lingxi Xie, Yunjie Tian +2
Aligning vision and language concepts at a finer level remains an essential topic of multimodal large language models (MLLMs), particularly for tasks such as referring and groundin…
Artemis: Towards Referential Understanding in Complex Videos
Jihao Qiu, Yuan Zhang, Xi Tang +6
Videos carry rich visual information including object description, action, interaction, etc., but the existing multimodal large language models (MLLMs) fell short in referential un…
Building Vision Models upon Heat Conduction
Zhaozhi Wang, Yue Liu, Yunjie Tian +3
Visual representation models leveraging attention mechanisms are challenged by significant computational overhead, particularly when pursuing large receptive fields. In this study,…
ChatterBox: Multi-round Multimodal Referring and Grounding
Yunjie Tian, Tianren Ma, Lingxi Xie +6
In this study, we establish a baseline for a new task named multimodal multi-round referring and grounding (MRG), opening up a promising direction for instance-level multimodal dia…
VMamba: Visual State Space Model
Yue Liu, Yunjie Tian, Yuzhong Zhao +6
Designing computationally efficient network architectures remains an ongoing necessity in computer vision. In this paper, we adapt Mamba, a state-space language model, into VMamba,…
Spatial Transform Decoupling for Oriented Object Detection
Hongtian Yu, Yunjie Tian, Qixiang Ye +1
Vision Transformers (ViTs) have achieved remarkable success in computer vision tasks. However, their potential in rotation-sensitive scenarios has not been fully explored, and this…
Fast-iTPN: Integrally Pre-Trained Transformer Pyramid Network with Token Migration
Yunjie Tian, Lingxi Xie, Jihao Qiu +4
We propose integrally pre-trained transformer pyramid network (iTPN), towards jointly optimizing the network backbone and the neck, so that transfer gap between representation mode…
HiViT: Hierarchical Vision Transformer Meets Masked Image Modeling
Xiaosong Zhang, Yunjie Tian, Wei Huang +4
Recently, masked image modeling (MIM) has offered a new methodology of self-supervised pre-training of vision transformers. A key idea of efficient implementation is to discard the…
Beyond Masking: Demystifying Token-Based Pre-Training for Vision Transformers
Yunjie Tian, Lingxi Xie, Jiemin Fang +6
The past year has witnessed a rapid development of masked image modeling (MIM). MIM is mostly built upon the vision transformers, which suggests that self-supervised visual represe…
Exploring Complicated Search Spaces with Interleaving-Free Sampling
Yunjie Tian, Lingxi Xie, Jiemin Fang +3
The existing neural architecture search algorithms are mostly working on search spaces with short-distance connections. We argue that such designs, though safe and stable, obstacle…
Semantic-Aware Generation for Self-Supervised Visual Representation Learning
Yunjie Tian, Lingxi Xie, Xiaopeng Zhang +6
In this paper, we propose a self-supervised visual representation learning approach which involves both generative and discriminative proxies, where we focus on the former part by…
GraFormer: Graph Convolution Transformer for 3D Pose Estimation
Weixi Zhao, Yunjie Tian, Qixiang Ye +2
Exploiting relations among 2D joints plays a crucial role yet remains semi-developed in 2D-to-3D pose estimation. To alleviate this issue, we propose GraFormer, a novel transformer…
Adaptive Linear Span Network for Object Skeleton Detection
Chang Liu, Yunjie Tian, Jianbin Jiao +1
Conventional networks for object skeleton detection are usually hand-crafted. Although effective, they require intensive priori knowledge to configure representative features for o…
Discretization-Aware Architecture Search
Yunjie Tian, Chang Liu, Lingxi Xie +2
The search cost of neural architecture search (NAS) has been largely reduced by weight-sharing methods. These methods optimize a super-network with all possible edges and operation…