23 accepted papers
Thinking with images has emerged as an effective paradigm for advancing visual reasoning, extending beyond text-only chains of thought by injecting visual evidence into intermediate reasoning steps. However, existing methods fall short of human-like abstract visual thinking, as their flexibility is
3D Gaussian Splatting (3DGS) enables real-time novel view synthesis with high visual quality. However, existing methods struggle with semi-transparent specular surfaces that exhibit both complex reflections and clear transmission, often producing blurry reflections or overly occluded transmission. T
In this paper, we propose a new multi-modal task, termed audio-visual instance segmentation (AVIS), which aims to simultaneously identify, segment and track individual sounding object instances in audible videos. To facilitate this research, we introduce a high-quality benchmark named AVISeg, contai…
In this work, we explore the mechanism of in-context learning (ICL) on out-of-distribution (OOD) tasks that were not encountered during training. To achieve this, we conduct synthetic experiments where the objective is to learn OOD mathematical functions through ICL using a GPT-2 model. We reveal th…
This paper presents FreEformer, a simple yet effective model that leverages a Frequency Enhanced Transformer for multivariate time series forecasting. Our work is based on the assumption that the frequency spectrum provides a global perspective on the composition of series across various frequencies
Neural Radiance Fields (NeRF) often struggle with reconstructing and rendering highly reflective scenes. Recent advancements have developed various reflection-aware appearance models to enhance NeRF's capability to render specular reflections. However, the robust reconstruction of highly reflective…
This paper presents $\mathbf{OLinear}$, a $\mathbf{linear}$-based multivariate time series forecasting model that operates in an $\mathbf{o}$rthogonally transformed domain. Recent forecasting models typically adopt the temporal forecast (TF) paradigm, which directly encode and decode time series in…
Enhancing node-level Out-Of-Distribution (OOD) generalization on graphs remains a crucial area. In this paper, we develop a Structural Causal Model (SCM) to theoretically dissect the performance of two prominent invariant learning methods--Invariant Risk Minimization (IRM) and Variance-Risk Extrapol…
"Source free domain adaptation (SFDA) aims to transfer the model trained on labeled source domain to unlabeled target domain without accessing source data. Recent SFDA methods predominantly rely on self-training, which supervise the model with pseudo labels generated from individual data samples. Ho…
Point cloud is a popular and widely used geometric representation, which has attracted significant attention in 3D vision. However, the geometric variability of point cloud representations across different datasets can cause domain discrepancies, which hinder knowledge transfer and model generalizat…
In this paper, we present the Sub-Adjacent Transformer with a novel attention mechanism for unsupervised time series anomaly detection. Unlike previous approaches that rely on all the points within some neighborhood for time point reconstruction, our method restricts the attention to regions not imm…
In the field of vanishing point detection, previous works commonly relied on extracting and clustering straight lines or classifying candidate points as vanishing points. This paper proposes a novel end-to-end framework, called VPDETR (Vanishing Point DEtection TRansformer), that views vanishing poi…
Domain adaptation for 3D point cloud has attracted a lot of interest since it can avoid the time-consuming labeling process of 3D data to some extent. A recent work named xMUDA leveraged multi-modal data to domain adaptation task of 3D semantic segmentation by mimicking the predictions between 2D an…
In this work, we investigate contrastive learning on perturbed point clouds and find that the contrasting process may widen the domain gap caused by random perturbations, making the pre-trained network fail to generalize on testing data. To this end, we propose the Equivariant COntrastive framework…
Knowledge graph embedding aims to represent entities and relations as low-dimensional vectors, which is an effective way for robotics to learn and reason about semantic knowledge. It is crucial for knowledge graph embedding models to infer various relation patterns, such as symmetry/antisymmetry. Ho
Knowledge graph embedding aims to represent entities and relations as low-dimensional vectors, which is an effective way for predicting missing links in knowledge graphs. Designing a strong and effective loss framework is essential for knowledge graph embedding models to distinguish between correct…
Knowledge graph embedding aims to represent entities and relations as low-dimensional vectors, which is an effective way for predicting missing links. It is crucial for knowledge graph embedding models to model and infer various relation patterns, such as symmetry/antisymmetry. However, many existin…
Previous works on vanishing point detection usually use geometric prior for line segment clustering. We find that image context can also contribute to accurate line classification. Based on this observation, we propose to classify line segments into three groups according to three unknown-but-sought…
To find a suitable embedding for a knowledge graph remains a big challenge nowadays. By using previous knowledge graph embedding methods, every entity in a knowledge graph is usually represented as a k-dimensional vector. As we know, an affine transformation can be expressed in the form of a matrix…
Several images are taken for the same scene with many view directions. Given a pixel in any one image of them, its correspondences may appear in the other images. However, by using existing semantic segmentation methods, we find that the pixel and its correspondences do not always have the same infe…
3D orientation estimation is a key component of many important computer vision tasks such as autonomous navigation and 3D scene understanding. This paper presents a new CNN architecture to estimate the 3D orientation of an omnidirectional camera with respect to the world coordinate system from a sin…
The effects of radial lens distortion often appear in wide-angle cameras of surveillance and safeguard systems, which may severely degrade performances of previous face recognition algorithms. Traditional methods for radial lens distortion correction usually employ line features in scenarios that ar…
Camera calibration directly from image sequences of a pedestrian without using any calibration object is a really challenging task and should be well solved in computer vision, especially in visual surveillance. In this paper, we propose a novel camera calibration method based on recovering the thre…