20 citations · 23 across the 12 of their papers we have counts for
13 papers
Fore-Mamba3D: Mamba-based Foreground-Enhanced Encoding for 3D Object Detection
Zhiwei Ning, Xuanang Gao, Jiaxi Cao +5
Linear modeling methods like Mamba have been merged as the effective backbone for the 3D object detection task. However, previous Mamba-based methods utilize the bidirectional enco…
CMF-IoU: Multi-Stage Cross-Modal Fusion 3D Object Detection with IoU Joint Prediction
Zhiwei Ning, Zhaojiang Liu, Xuanang Gao +4
Multi-modal methods based on camera and LiDAR sensors have garnered significant attention in the field of 3D detection. However, many prevalent works focus on single or partial sta…
Mobile U-ViT: Revisiting large kernel and U-shaped ViT for efficient medical image segmentation
Fenghe Tang, Bingkun Nian, Jianrui Ding +6
In clinical practice, medical image analysis often requires efficient execution on resource-constrained mobile devices. However, existing mobile models-primarily optimized for natu…
FIX-CLIP: Dual-Branch Hierarchical Contrastive Learning via Synthetic Captions for Better Understanding of Long Text
Bingchao Wang, Zhiwei Ning, Jianyu Ding +5
CLIP has shown promising performance across many short-text tasks in a zero-shot manner. However, limited by the input length of the text encoder, CLIP struggles on under-stream ta…
Efficient 4D Gaussian Stream with Low Rank Adaptation
Zhenhuan Liu, Shuai Liu, Yidong Lu +3
Recent methods have made significant progress in synthesizing novel views with long video sequences. This paper proposes a highly scalable method for dynamic novel view synthesis w…
OPUS: Occupancy Prediction Using a Sparse Set
Jiabao Wang, Zhaojiang Liu, Qiang Meng +6
Occupancy prediction, aiming at predicting the occupancy status within voxelized 3D environment, is quickly gaining momentum within the autonomous driving community. Mainstream occ…
CD-NGP: A Fast Scalable Continual Representation for Dynamic Scenes
Zhenhuan Liu, Shuai Liu, Zhiwei Ning +4
Novel view synthesis (NVS) in dynamic scenes faces persistent challenges in memory consumption, model complexity, training efficiency, and rendering quality. Offline methods offer…
MambaMIM: Pre-training Mamba with State Space Token Interpolation and its Application to Medical Image Segmentation
Fenghe Tang, Bingkun Nian, Yingtai Li +4
Recently, the state space model Mamba has demonstrated efficient long-sequence modeling capabilities, particularly for addressing long-sequence visual tasks in 3D medical imaging.…
FASTC: A Fast Attentional Framework for Semantic Traversability Classification Using Point Cloud
Yirui Chen, Pengjin Wei, Zhenhuan Liu +3
Producing traversability maps and understanding the surroundings are crucial prerequisites for autonomous navigation. In this paper, we address the problem of traversability assess…
GIM: A Million-scale Benchmark for Generative Image Manipulation Detection and Localization
Yirui Chen, Xudong Huang, Quan Zhang +9
The extraordinary ability of generative models emerges as a new trend in image editing and generating realistic images, posing a serious threat to the trustworthiness of multimedia…
T-Code: Simple Temporal Latent Code for Efficient Dynamic View Synthesis
Zhenhuan Liu, Shuai Liu, Jie Yang +1
Novel view synthesis for dynamic scenes is one of the spotlights in computer vision. The key to efficient dynamic view synthesis is to find a compact representation to store the in…
MobileUtr: Revisiting the relationship between light-weight CNN and Transformer for efficient medical image segmentation
Fenghe Tang, Bingkun Nian, Jianrui Ding +4
Due to the scarcity and specific imaging characteristics in medical images, light-weighting Vision Transformers (ViTs) for efficient medical image segmentation is a significant cha…
SRSNetwork: Siamese Reconstruction-Segmentation Networks based on Dynamic-Parameter Convolution
Bingkun Nian, Fenghe Tang, Jianrui Ding +4
Dynamic convolution demonstrates outstanding representation capabilities, which are crucial for natural image segmentation. However, it fails when applied to medical image segmenta…