Luc Van Gool
Professor at ETH Zurich
Diffusion-based text-to-image models ignited immense attention from the
...
Modeling Neural Radiance Fields for fast-moving deformable objects from
...
Modern approaches have proved the huge potential of addressing semantic
...
3D visual grounding is the task of localizing the object in a 3D scene w...
Continual Learning aims to learn a single model on a sequence of tasks
w...
The Diffusion Model (DM) has emerged as the SOTA approach for image
synt...
VLN-CE is a recently released embodied task, where AI agents need to nav...
Super Resolution (SR) and Camouflaged Object Detection (COD) are two hot...
Google's Bard has emerged as a formidable competitor to OpenAI's ChatGPT...
The local road network information is essential for autonomous navigatio...
We propose a novel ECGAN for the challenging semantic image synthesis ta...
Autonomous driving requires accurate local scene understanding informati...
We present a novel approach to the generation of static and articulated ...
During training, supervised object detection tries to correctly match th...
While originally designed for image generation, diffusion models have
re...
We present an uncertainty learning framework for dense neural simultaneo...
Domain adaptive object detection aims to leverage the knowledge learned ...
Adaptation of semantic segmentation networks to different visual conditi...
Multi-modality image fusion is a technique used to combine information f...
Plug-and-play Image Restoration (IR) has been widely recognized as a fle...
We propose a discrete latent distribution for Generative Adversarial Net...
This paper presents an efficient online framework to solve the well-know...
With autonomous industries on the rise, domain adaptation of the visual
...
Unsupervised domain adaptation (UDA) and domain generalization (DG) enab...
Recently, indiscernible scene understanding has attracted a lot of atten...
Concealed scene understanding (CSU) is a hot computer vision topic aimin...
This paper proposes a quantum computing-based algorithm to solve the sin...
Generating a high-quality High Dynamic Range (HDR) image from dynamic sc...
Segmenting anything is a ground-breaking step toward artificial general
...
The burgeoning field of camouflaged object detection (COD) seeks to iden...
We propose a dense neural simultaneous localization and mapping (SLAM)
a...
Autonomous driving requires a structured understanding of the surroundin...
Neural-network-based single image depth prediction (SIDP) is a challengi...
We introduce an approach to enhance the novel view synthesis from images...
Deep point cloud registration methods face challenges to partial overlap...
Pose-conditioned convolutional generative models struggle with high-qual...
Lidar is a vital sensor for estimating the depth of a scene. Typical spi...
Diffusion model (DM) has achieved SOTA performance by modeling the image...
Guided depth map super-resolution (GDSR), as a hot topic in multi-modal ...
We present a novel graph Transformer generative adversarial network (GTG...
Multi-modality image fusion aims to combine different modalities to prod...
Standard unsupervised domain adaptation methods adapt models from a sour...
An emerging field of sequential decision problems is safe Reinforcement
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Data-driven simulation has become a favorable way to train and test
auto...
The aim of this paper is to propose a mechanism to efficiently and expli...
We introduce VA-DepthNet, a simple, effective, and accurate deep neural
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The performance of video frame interpolation is inherently correlated wi...
Depth cues are known to be useful for visual perception. However, direct...
How to identify and segment camouflaged objects from the background is
c...
Recent works have shown that unstructured text (documents) from online
s...