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Found 43 papers, 14 papers with code
Technique Report of CVPR 2024 PBDL Challenges
The intersection of physics-based vision and deep learning presents an exciting frontier for advancing computer vision technologies.
Modeling Weather Uncertainty for Multi-weather Co-Presence Estimation
In this paper, we start with solid revisit of the physics definition of weather and how it can be described as a continuous machine learning and computer vision task.
Learning Generalized Segmentation for Foggy-scenes by Bi-directional Wavelet Guidance
We argue that an ideal segmentation model that can be well generalized to foggy-scenes need to simultaneously enhance the content, de-correlate the urban-scene style and de-correlate the fog style.
Atlantis: Enabling Underwater Depth Estimation with Stable Diffusion
Nonetheless, the performance of these methods is often constrained by the domain gap and looser constraints.
Exploring Different Levels of Supervision for Detecting and Localizing Solar Panels on Remote Sensing Imagery
The classifier excels in binary presence detection (0. 79 F1-score), while the object detector (0. 72) offers precise localization.
Interactive Learning of Intrinsic and Extrinsic Properties for All-day Semantic Segmentation
In this paper, in contrast to existing methods, we tackle this challenge from the perspective of image formulation itself, where the image appearance is determined by both intrinsic (e. g., semantic category, structure) and extrinsic (e. g., lighting) properties.
Ranked #1 on
Semantic Segmentation
on Mapillary val
Learning Content-enhanced Mask Transformer for Domain Generalized Urban-Scene Segmentation
Unlike domain gap challenges, USSS is unique in that the semantic categories are often similar in different urban scenes, while the styles can vary significantly due to changes in urban landscapes, weather conditions, lighting, and other factors.
Learning Rain Location Prior for Nighttime Deraining
This learned prior contains location information of rain streaks and, when injected into deraining models, can significantly improve their performance.
GTAV-NightRain: Photometric Realistic Large-scale Dataset for Night-time Rain Streak Removal
In this paper, we propose GTAV-NightRain dataset, which is a large-scale synthetic night-time rain streak removal dataset.
Multi-person 3D pose estimation from a single image captured by a fisheye camera
Multi-person 3D pose estimation with absolute depths for a fisheye camera is a challenging task but with valuable applications in daily life, especially for video surveillance.
Multitask AET with Orthogonal Tangent Regularity for Dark Object Detection
To enhance object detection in a dark environment, we propose a novel multitask auto encoding transformation (MAET) model which is able to explore the intrinsic pattern behind illumination translation.
Ranked #1 on
2D Object Detection
on ExDark
Learning Temporal Consistency for Low Light Video Enhancement From Single Images
Based on this idea, we propose our method which can infer motion prior for single image low light video enhancement and enforce temporal consistency.
Finding a Needle in a Haystack: Tiny Flying Object Detection in 4K Videos using a Joint Detection-and-Tracking Approach
Detecting tiny objects in a high-resolution video is challenging because the visual information is little and unreliable.
Weakly-supervised Semantic Segmentation in Cityscape via Hyperspectral Image
It is based on the idea that high-resolution HSIs in city scenes contain rich spectral information, which can be easily associated to semantics without manual labeling.
Realistic Large-Scale Fine-Depth Dehazing Dataset from 3D Videos
We select a large number of high-quality frames of real outdoor scenes and render haze on them using depth from stereo.
Kinship Identification through Joint Learning Using Kinship Verification Ensembles
The experiments further show significant performance improvement of kinship verification when trained on the same dataset with more realistic distributions.
Feedback Graph Convolutional Network for Skeleton-based Action Recognition
It transmits the high-level semantic features to the low-level layers and flows temporal information stage by stage to progressively model global spatial-temporal features for action recognition; (3) The FGCN model provides early predictions.
Ranked #37 on
Skeleton Based Action Recognition
on NTU RGB+D 120
Unsupervised Learning for Intrinsic Image Decomposition from a Single Image
Intrinsic image decomposition, which is an essential task in computer vision, aims to infer the reflectance and shading of the scene.
Semantic Guided Single Image Reflection Removal
Reflection is common in images capturing scenes behind a glass window, which is not only a disturbance visually but also influence the performance of other computer vision algorithms.
Hyperspectral City V1.0 Dataset and Benchmark
This document introduces the background and the usage of the Hyperspectral City Dataset and the benchmark.
Dunhuang Grottoes Painting Dataset and Benchmark
Follow the trend, we release the first public dataset for Dunhuang Grotto Painting restoration.
Hybrid Loss for Learning Single-Image-based HDR Reconstruction
It makes the reconstruction faithful to the input.
Loss Guided Activation for Action Recognition in Still Images
This requirement of bounding boxes as part of the input is needed to enable the methods to ignore irrelevant contexts and extract only human features.
Classification-Reconstruction Learning for Open-Set Recognition
Existing open-set classifiers rely on deep networks trained in a supervised manner on known classes in the training set; this causes specialization of learned representations to known classes and makes it hard to distinguish unknowns from knowns.
Detail Preserving Depth Estimation from a Single Image Using Attention Guided Networks
However, depth estimation is a dense prediction problem and low-resolution feature maps usually generate blurred depth map which is undesirable in application.
Single Image Water Hazard Detection using FCN with Reflection Attention Units
Water bodies, such as puddles and flooded areas, on and off road pose significant risks to autonomous cars.
Weakly-Supervised Semantic Segmentation by Iteratively Mining Common Object Features
Then in the top-down step, the refined object regions are used as supervision to train the segmentation network and to predict object masks.
JTAV: Jointly Learning Social Media Content Representation by Fusing Textual, Acoustic, and Visual Features
Learning social media content is the basis of many real-world applications, including information retrieval and recommendation systems, among others.
Cross-connected Networks for Multi-task Learning of Detection and Segmentation
We evaluated our proposed architecture on a combination of detection and segmentation using two datasets.
Semantic Single-Image Dehazing
In experiments, we validate our ap- proach upon synthetic and real hazy images, where our method showed superior performance over state-of-the-art approaches, suggesting semantic information facilitates the haze removal task.
A Frequency Domain Neural Network for Fast Image Super-resolution
Moreover, the non-linearity in deep nets, often achieved by a rectifier unit, is here cast as a convolution in the frequency domain.
Deep Texture and Structure Aware Filtering Network for Image Smoothing
Image smoothing is a fundamental task in computer vision, that aims to retain salient structures and remove insignificant textures.
Differentiating Objects by Motion: Joint Detection and Tracking of Small Flying Objects
While generic object detection has achieved large improvements with rich feature hierarchies from deep nets, detecting small objects with poor visual cues remains challenging.
Learning RGB-D Salient Object Detection using background enclosure, depth contrast, and top-down features
Here we propose a novel deep CNN architecture for RGB-D salient object detection that exploits high-level, mid-level, and low level features.
Automatic Generation of Grounded Visual Questions
In this paper, we propose the first model to be able to generate visually grounded questions with diverse types for a single image.
Single Image Action Recognition using Semantic Body Part Actions
Second, for each body parts, a Part Action Res-Net is used to predict semantic body part actions.
Edge Preserving and Multi-Scale Contextual Neural Network for Salient Object Detection
In this paper, we propose a novel edge preserving and multi-scale contextual neural network for salient object detection.
Haze Visibility Enhancement: A Survey and Quantitative Benchmarking
This paper provides a comprehensive survey of methods dealing with visibility enhancement of images taken in hazy or foggy scenes.
Local Background Enclosure for RGB-D Salient Object Detection
Recent work in salient object detection has considered the incorporation of depth cues from RGB-D images.
Ranked #25 on
RGB-D Salient Object Detection
on NJU2K
Multiview Rectification of Folded Documents
Digitally unwrapping images of paper sheets is crucial for accurate document scanning and text recognition.
Perceptually Consistent Color-to-Gray Image Conversion
In this paper, we propose a color to grayscale image conversion algorithm (C2G) that aims to preserve the perceptual properties of the color image as much as possible.
Waterdrop Stereo
(2) The imagery inside a water-drop is determined by the water-drop 3D shape and total reflection at the boundary.
Adherent Raindrop Detection and Removal in Video
First, it detects raindrops based on the motion and the intensity temporal derivatives of the input video.
