18 citations · 48 across the 12 of their papers we have counts for
14 papers
MM-OPD: Towards One More Bottleneck Between Perception and Reasoning
Jintao Tong, Yujing Lou, Zhanming Shen +6
Recent multimodal large language models (MLLMs) advance visual reasoning by strengthening both perception and reasoning, implicitly assuming a process that transitions seamlessly f…
CC-VQA: Conflict- and Correlation-Aware Method for Mitigating Knowledge Conflict in Knowledge-Based Visual Question Answering
Yuyang Hong, Jiaqi Gu, Yujin Lou +7
Knowledge-based visual question answering (KB-VQA) demonstrates significant potential for handling knowledge-intensive tasks. However, conflicts arise between static parametric kno…
Sketch-in-Latents: Eliciting Unified Reasoning in MLLMs
Jintao Tong, Jiaqi Gu, Yujing Lou +5
While Multimodal Large Language Models (MLLMs) excel at visual understanding tasks through text reasoning, they often fall short in scenarios requiring visual imagination. Unlike c…
SD-VLM: Spatial Measuring and Understanding with Depth-Encoded Vision-Language Models
Pingyi Chen, Yujing Lou, Shen Cao +6
While vision language models (VLMs) excel in 2D semantic visual understanding, their ability to quantitatively reason about 3D spatial relationships remains under-explored, due to…
HybridGS: Decoupling Transients and Statics with 2D and 3D Gaussian Splatting
Jingyu Lin, Jiaqi Gu, Lubin Fan +5
Generating high-quality novel view renderings of 3D Gaussian Splatting (3DGS) in scenes featuring transient objects is challenging. We propose a novel hybrid representation, termed…
CRIN: Rotation-Invariant Point Cloud Analysis and Rotation Estimation via Centrifugal Reference Frame
Yujing Lou, Zelin Ye, Yang You +5
Various recent methods attempt to implement rotation-invariant 3D deep learning by replacing the input coordinates of points with relative distances and angles. Due to the incomple…
Understanding Pixel-level 2D Image Semantics with 3D Keypoint Knowledge Engine
Yang You, Chengkun Li, Yujing Lou +5
Pixel-level 2D object semantic understanding is an important topic in computer vision and could help machine deeply understand objects (e.g. functionality and affordance) in our da…
Localization with Sampling-Argmax
Jiefeng Li, Tong Chen, Ruiqi Shi +3
Soft-argmax operation is commonly adopted in detection-based methods to localize the target position in a differentiable manner. However, training the neural network with soft-argm…
PRIN/SPRIN: On Extracting Point-wise Rotation Invariant Features
Yang You, Yujing Lou, Ruoxi Shi +5
Point cloud analysis without pose priors is very challenging in real applications, as the orientations of point clouds are often unknown. In this paper, we propose a brand new poin…
Canonical Voting: Towards Robust Oriented Bounding Box Detection in 3D Scenes
Yang You, Zelin Ye, Yujing Lou +5
3D object detection has attracted much attention thanks to the advances in sensors and deep learning methods for point clouds. Current state-of-the-art methods like VoteNet regress…
Semantic Correspondence via 2D-3D-2D Cycle
Yang You, Chengkun Li, Yujing Lou +4
Visual semantic correspondence is an important topic in computer vision and could help machine understand objects in our daily life. However, most previous methods directly train o…
KeypointNet: A Large-scale 3D Keypoint Dataset Aggregated from Numerous Human Annotations
Yang You, Yujing Lou, Chengkun Li +5
Detecting 3D objects keypoints is of great interest to the areas of both graphics and computer vision. There have been several 2D and 3D keypoint datasets aiming to address this pr…
Human Correspondence Consensus for 3D Object Semantic Understanding
Yujing Lou, Yang You, Chengkun Li +5
Semantic understanding of 3D objects is crucial in many applications such as object manipulation. However, it is hard to give a universal definition of point-level semantics that e…
Pointwise Rotation-Invariant Network with Adaptive Sampling and 3D Spherical Voxel Convolution
Yang You, Yujing Lou, Qi Liu +4
Point cloud analysis without pose priors is very challenging in real applications, as the orientations of point clouds are often unknown. In this paper, we propose a brand new poin…