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Found 20 papers, 13 papers with code
RecDiffusion: Rectangling for Image Stitching with Diffusion Models
Image stitching from different captures often results in non-rectangular boundaries, which is often considered unappealing.
Better Explain Transformers by Illuminating Important Information
Transformer-based models excel in various natural language processing (NLP) tasks, attracting countless efforts to explain their inner workings.
GAFlow: Incorporating Gaussian Attention into Optical Flow
Moreover, for reliable motion analysis, we provide a new Gaussian-Guided Attention Module (GGAM) which not only inherits properties from Gaussian distribution to instinctively revolve around the neighbor fields of each point but also is empowered to put the emphasis on contextually related regions during matching.
SCP: Spherical-Coordinate-based Learned Point Cloud Compression
In recent years, the task of learned point cloud compression has gained prominence.
Low-Light Image Enhancement with Wavelet-based Diffusion Models
Diffusion models have achieved promising results in image restoration tasks, yet suffer from time-consuming, excessive computational resource consumption, and unstable restoration.
Ranked #1 on
Low-Light Image Enhancement
on LOLv2
Learning Optical Flow from Event Camera with Rendered Dataset
Previous datasets are created by either capturing real scenes by event cameras or synthesizing from images with pasted foreground objects.
Explicit Motion Disentangling for Efficient Optical Flow Estimation
In this paper, we propose a novel framework for optical flow estimation that achieves a good balance between performance and efficiency.
RealFlow: EM-based Realistic Optical Flow Dataset Generation from Videos
Specifically, we first estimate optical flow between a pair of video frames, and then synthesize a new image from this pair based on the predicted flow.
Memory-Efficient Learned Image Compression with Pruned Hyperprior Module
Learned Image Compression (LIC) gradually became more and more famous in these years.
Cross-View Language Modeling: Towards Unified Cross-Lingual Cross-Modal Pre-training
To this end, the cross-view language modeling framework considers both multi-modal data (i. e., image-caption pairs) and multi-lingual data (i. e., parallel sentence pairs) as two different views of the same object, and trains the model to align the two views by maximizing the mutual information between them with conditional masked language modeling and contrastive learning.
Learning Optical Flow with Adaptive Graph Reasoning
Our key idea is to decouple the context reasoning from the matching procedure, and exploit scene information to effectively assist motion estimation by learning to reason over the adaptive graph.
Learning Optical Flow With Kernel Patch Attention
Optical flow is a fundamental method used for quantitative motion estimation on the image plane.
Probabilistic Model Distillation for Semantic Correspondence
We address this problem with the use of a novel Probabilistic Model Distillation (PMD) approach which transfers knowledge learned by a probabilistic teacher model on synthetic data to a static student model with the use of unlabeled real image pairs.
ASFlow: Unsupervised Optical Flow Learning with Adaptive Pyramid Sampling
Equipped with these two modules, our method achieves the best performance for unsupervised optical flow estimation on multiple leading benchmarks, including MPI-SIntel, KITTI 2012 and KITTI 2015.
WebSRC: A Dataset for Web-Based Structural Reading Comprehension
In this paper, we introduce the task of structural reading comprehension (SRC) on web.
Uncertainty-Guided Transformer Reasoning for Camouflaged Object Detection
Spotting objects that are visually adapted to their surroundings is challenging for both humans and AI.
Cascade Graph Neural Networks for RGB-D Salient Object Detection
Current works either simply distill prior knowledge from the corresponding depth map for handling the RGB-image or blindly fuse color and geometric information to generate the coarse depth-aware representations, hindering the performance of RGB-D saliency detectors. In this work, we introduceCascade Graph Neural Networks(Cas-Gnn), a unified framework which is capable of comprehensively distilling and reasoning the mutual benefits between these two data sources through a set of cascade graphs, to learn powerful representations for RGB-D salient object detection.
Ranked #5 on
RGB-D Salient Object Detection
on NJU2K
Deep-VFX: Deep Action Recognition Driven VFX for Short Video
In the application, short-form mobile video is so popular all over the world such as Tik Tok.
Hybrid Graph Neural Networks for Crowd Counting
In this paper, we present a novel network structure called Hybrid Graph Neural Network (HyGnn) which targets to relieve the problem by interweaving the multi-scale features for crowd density as well as its auxiliary task (localization) together and performing joint reasoning over a graph.
Fast Portrait Segmentation with Highly Light-weight Network
In this paper, we describe a fast and light-weight portrait segmentation method based on a new highly light-weight backbone (HLB) architecture.
