Publications by Tong Zhang | EPFL Graph Search

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Texture modeling and synthesis are essential for enhancing the realism of virtual environments. Methods that directly synthesize textures in 3D offer distinct advantages to the UV-mapping-based methods as they can create seamless textures and align more cl ...

Current Dynamic Texture Synthesis (DyTS) models can synthesize realistic videos. However, they require a slow iterative optimization process to synthesize a single fixed-size short video, and they do not offer any post-training control over the synthesis p ...

Vision Transformers (ViTs) demonstrate remarkable performance in image classification through visual-token interaction learning, particularly when equipped with local information via region attention or convolutions. Although such architectures improve the ...

Self-supervised learning (SSL) has the potential to ben- efit many applications, particularly those where manually annotating data is cumbersome. One such situation is the semantic segmentation of point clouds. In this context, ex- isting methods employ co ...

We propose InNeRF360, an automatic system that accurately removes text-specified objects from 360 • Neural Radiance Fields (NeRF). The challenge is to effectively remove objects while inpainting perceptually consistent content for the missing regions, whic ...

Generative Adversarial Network (GAN) based localized image editing can suffer from ambiguity between semantic attributes. We thus present a novel objective function to evaluate the locality of an image edit. By introducing the supervision from a pre-traine ...

Finding accurate correspondences among different views is the Achilles’ heel of unsupervised Multi-View Stereo (MVS). Existing methods are built upon the assumption that corresponding pixels share similar photometric features. However, multi-view images in ...

We propose an end-to-end Multitask Learning Transformer framework, named MulT, to simultaneously learn multiple high-level vision tasks, including depth estimation, semantic segmentation, reshading, surface normal estimation, 2D keypoint detection, and edg ...

Visual salient object detection (SOD) aims at finding the salient object(s) that attract human attention, while camouflaged object detection (COD) on the contrary intends to discover the camouflaged object(s) that hidden in the surrounding. In this paper, ...

Accurately estimating 3D human pose (3D HPE) and joint locations using only 2D keypoints is challenging. The noise in the predictions produced by conventional 2D human pose estimators often impeded the accuracy. In this paper, we present a diffusion-based ...

Deep saliency prediction algorithms complement the object recognition features, they typically rely on additional information such as scene context, semantic relationships, gaze direction, and object dissimilarity. However, none of these models consider th ...

Directly regressing the non-rigid shape and camera pose from the individual 2D frame is ill-suited to the Non-Rigid Structure-from-Motion (NRSfM) problem. This frame-by-frame 3D reconstruction pipeline overlooks the inherent spatial-temporal nature of NRSf ...

Saliency prediction has made great strides over the past two decades, with current techniques modeling low-level information, such as color, intensity and size contrasts, and high-level ones, such as attention and gaze direction for entire objects. Despite ...

The success of the Neural Radiance Fields (NeRF) in novel view synthesis has inspired researchers to propose neural implicit scene reconstruction. However, most existing neural implicit reconstruction methods optimize perscene parameters and therefore lack ...

In the realm of point cloud scene understanding, particularly in indoor scenes, objects are arranged following human habits, resulting in objects of certain semantics being closely positioned and displaying notable inter-object correlations. This can creat ...

The core of active semi-supervised crowd counting is the sample selection criteria. However, the scale factor has been neglected in active learning approaches despite the fact that the scale of heads varies drastically in the crowd images. In this paper, w ...

Few-shot learning aims to correctly recognize query samples from unseen classes given a limited number of support samples, often by relying on global embeddings of images. In this paper, we propose to equip the backbone network with an attention agent, whi ...

Self-supervised learning (SSL) methods aim to learn view-invariant representations by maximizing the similarity between the features extracted from different crops of the same image regardless of cropping size and content. In essence, this strategy ignores ...

Tong Zhang

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Mesh Neural Cellular Automata

DyNCA: Real-time Dynamic Texture Synthesis Using Neural Cellular Automata

AdaNCA: Neural Cellular Automata as Adaptors for More Robust Vision Transformer

Spatiotemporal Self-supervised Learning for Point Clouds in the Wild

InNeRF360: Text-Guided 3D-Consistent Object Inpainting on 360° Neural Radiance Fields

Optimizing Latent Space Directions For GAN-based Local Image Editing

RC-MVSNet: Unsupervised Multi-View Stereo with Neural Rendering

MulT: An End-to-End Multitask Learning Transformer

Uncertainty-aware Joint Salient Object and Camouflaged Object Detection

Diff3DHPE: A Diffusion Model for 3D Human Pose Estimation

TempSAL - Uncovering Temporal Information for Deep Saliency Prediction

Deep Non-Rigid Structure-From-Motion: A Sequence-to-Sequence Translation Perspective

Modeling Object Dissimilarity for Deep Saliency Prediction

VolRecon: Volume Rendering of Signed Ray Distance Functions for Generalizable Multi-View Reconstruction

Mitigating Object Dependencies: Improving Point Cloud Self-Supervised Learning through Object Exchange

Boosting Semi-supervised Crowd Counting with Scale-based Active Learning

Reinforced Attention for Few-Shot Learning and Beyond

Leverage Your Local and Global Representations: A New Self-Supervised Learning Strategy