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Found 32 papers, 13 papers with code
TSD-SR: One-Step Diffusion with Target Score Distillation for Real-World Image Super-Resolution
Pre-trained text-to-image diffusion models are increasingly applied to real-world image super-resolution (Real-ISR) task.
AuthFace: Towards Authentic Blind Face Restoration with Face-oriented Generative Diffusion Prior
In this paper, we propose a novel framework, namely AuthFace that achieves highly authentic face restoration results by exploring a face-oriented generative diffusion prior.
LIPE: Learning Personalized Identity Prior for Non-rigid Image Editing
In this paper, we explore a novel task: learning the personalized identity prior for text-based non-rigid image editing.
FreeDiff: Progressive Frequency Truncation for Image Editing with Diffusion Models
Precise image editing with text-to-image models has attracted increasing interest due to their remarkable generative capabilities and user-friendly nature.
Tuning-Free Adaptive Style Incorporation for Structure-Consistent Text-Driven Style Transfer
In this work, we propose a novel solution to the text-driven style transfer task, namely, Adaptive Style Incorporation~(ASI), to achieve fine-grained feature-level style incorporation.
InstructBrush: Learning Attention-based Instruction Optimization for Image Editing
Two key techniques are introduced into InstructBrush, Attention-based Instruction Optimization and Transformation-oriented Instruction Initialization, to address the limitations of the previous method in terms of inversion effects and instruction generalization.
C$\cdot$ASE: Learning Conditional Adversarial Skill Embeddings for Physics-based Characters
We present C$\cdot$ASE, an efficient and effective framework that learns conditional Adversarial Skill Embeddings for physics-based characters.
3D-Aware Object Goal Navigation via Simultaneous Exploration and Identification
However, leveraging 3D scene representation can be prohibitively unpractical for policy learning in this floor-level task, due to low sample efficiency and expensive computational cost.
DualAfford: Learning Collaborative Visual Affordance for Dual-gripper Manipulation
It is essential yet challenging for future home-assistant robots to understand and manipulate diverse 3D objects in daily human environments.
Multi-Robot Active Mapping via Neural Bipartite Graph Matching
Previous approaches either choose the frontier as the goal position via a myopic solution that hinders the time efficiency, or maximize the long-term value via reinforcement learning to directly regress the goal position, but does not guarantee the complete map construction.
ADeLA: Automatic Dense Labeling With Attention for Viewpoint Shift in Semantic Segmentation
We describe a method to deal with performance drop in semantic segmentation caused by viewpoint changes within multi-camera systems, where temporally paired images are readily available, but the annotations may only be abundant for a few typical views.
RoboAssembly: Learning Generalizable Furniture Assembly Policy in a Novel Multi-robot Contact-rich Simulation Environment
Part assembly is a typical but challenging task in robotics, where robots assemble a set of individual parts into a complete shape.
AdaAfford: Learning to Adapt Manipulation Affordance for 3D Articulated Objects via Few-shot Interactions
Perceiving and interacting with 3D articulated objects, such as cabinets, doors, and faucets, pose particular challenges for future home-assistant robots performing daily tasks in human environments.
ADeLA: Automatic Dense Labeling with Attention for Viewpoint Adaptation in Semantic Segmentation
Furthermore, to resolve ambiguities in converting the semantic images to semantic labels, we treat the view transformation network as a functional representation of an unknown mapping implied by the color images and propose functional label hallucination to generate pseudo-labels in the target domain.
Contrastive Multimodal Fusion with TupleInfoNCE
Another approach is to concatenate all the modalities into a tuple and then contrast positive and negative tuple correspondences.
Ranked #76 on
Semantic Segmentation
on NYU Depth v2
VAT-Mart: Learning Visual Action Trajectory Proposals for Manipulating 3D ARTiculated Objects
In this paper, we propose object-centric actionable visual priors as a novel perception-interaction handshaking point that the perception system outputs more actionable guidance than kinematic structure estimation, by predicting dense geometry-aware, interaction-aware, and task-aware visual action affordance and trajectory proposals.
Generating Manga From Illustrations via Mimicking Manga Creation Workflow
To this end, we create a large-scale dataset with these three components annotated by artists in a human-in-the-loop manner.
CAPTRA: CAtegory-level Pose Tracking for Rigid and Articulated Objects from Point Clouds
For the first time, we propose a unified framework that can handle 9DoF pose tracking for novel rigid object instances as well as per-part pose tracking for articulated objects from known categories.
P4Contrast: Contrastive Learning with Pairs of Point-Pixel Pairs for RGB-D Scene Understanding
Self-supervised representation learning is a critical problem in computer vision, as it provides a way to pretrain feature extractors on large unlabeled datasets that can be used as an initialization for more efficient and effective training on downstream tasks.
Robust Neural Routing Through Space Partitions for Camera Relocalization in Dynamic Indoor Environments
Localizing the camera in a known indoor environment is a key building block for scene mapping, robot navigation, AR, etc.
Towards Accurate Active Camera Localization
These approaches localize the camera in the discrete pose space and are agnostic to the localization-driven scene property, which restricts the camera pose accuracy in the coarse scale.
IF-Defense: 3D Adversarial Point Cloud Defense via Implicit Function based Restoration
The former aims to recover the surface of point cloud through implicit function, while the latter encourages evenly-distributed points.
Generative 3D Part Assembly via Dynamic Graph Learning
Analogous to buying an IKEA furniture, given a set of 3D parts that can assemble a single shape, an intelligent agent needs to perceive the 3D part geometry, reason to propose pose estimations for the input parts, and finally call robotic planning and control routines for actuation.
Single image reflection removal via learning with multi-image constraints
Reflections are very common phenomena in our daily photography, which distract people's attention from the scene behind the glass.
GraphX$^{NET}-$ Chest X-Ray Classification Under Extreme Minimal Supervision
The task of classifying X-ray data is a problem of both theoretical and clinical interest.
A General Decoupled Learning Framework for Parameterized Image Operators
To overcome this limitation, we propose a new decoupled learning algorithm to learn from the operator parameters to dynamically adjust the weights of a deep network for image operators, denoted as the base network.
Gated Context Aggregation Network for Image Dehazing and Deraining
Image dehazing aims to recover the uncorrupted content from a hazy image.
Ranked #2 on
Rain Removal
on DID-MDN
Image Smoothing via Unsupervised Learning
Image smoothing represents a fundamental component of many disparate computer vision and graphics applications.
Decouple Learning for Parameterized Image Operators
Many different deep networks have been used to approximate, accelerate or improve traditional image operators, such as image smoothing, super-resolution and denoising.
Mirror, Mirror, on the Wall, Who's Got the Clearest Image of Them All? - A Tailored Approach to Single Image Reflection Removal
Removing reflection artefacts from a single image is a problem of both theoretical and practical interest, which still presents challenges because of the massively ill-posed nature of the problem.
A Generic Deep Architecture for Single Image Reflection Removal and Image Smoothing
This paper proposes a deep neural network structure that exploits edge information in addressing representative low-level vision tasks such as layer separation and image filtering.
Revisiting Deep Intrinsic Image Decompositions
While invaluable for many computer vision applications, decomposing a natural image into intrinsic reflectance and shading layers represents a challenging, underdetermined inverse problem.
