Search Results for author:
Found 23 papers, 13 papers with code
Self-similarity-based super-resolution of photoacoustic angiography from hand-drawn doodles
To address this challenge, we propose a novel approach that employs a super-resolution PAA method trained with forged PAA images.
Efficient Meshy Neural Fields for Animatable Human Avatars
Moreover, only minutes of optimization is enough for plausible reconstruction results.
Global Knowledge Calibration for Fast Open-Vocabulary Segmentation
Recent advancements in pre-trained vision-language models, such as CLIP, have enabled the segmentation of arbitrary concepts solely from textual inputs, a process commonly referred to as open-vocabulary semantic segmentation (OVS).
Knowledge Distillation
Open Vocabulary Semantic Segmentation
+3
Semantics-Aware Dynamic Localization and Refinement for Referring Image Segmentation
Referring image segmentation segments an image from a language expression.
LOGO: A Long-Form Video Dataset for Group Action Quality Assessment
Action quality assessment (AQA) has become an emerging topic since it can be extensively applied in numerous scenarios.
FLAG3D: A 3D Fitness Activity Dataset with Language Instruction
With the continuously thriving popularity around the world, fitness activity analytic has become an emerging research topic in computer vision.
Global Spectral Filter Memory Network for Video Object Segmentation
Besides, we empirically find low frequency feature should be enhanced in encoder (backbone) while high frequency for decoder (segmentation head).
Semantic Segmentation
Semi-Supervised Video Object Segmentation
+1
HorNet: Efficient High-Order Spatial Interactions with Recursive Gated Convolutions
In this paper, we show that the key ingredients behind the vision Transformers, namely input-adaptive, long-range and high-order spatial interactions, can also be efficiently implemented with a convolution-based framework.
Ranked #19 on
Semantic Segmentation
on ADE20K
Learning from Temporal Spatial Cubism for Cross-Dataset Skeleton-based Action Recognition
Different from the conventional adversarial learning-based approaches for UDA, we utilize a self-supervision scheme to reduce the domain shift between two skeleton-based action datasets.
OrdinalCLIP: Learning Rank Prompts for Language-Guided Ordinal Regression
In this paper, we propose to learn the rank concepts from the rich semantic CLIP latent space.
BNV-Fusion: Dense 3D Reconstruction using Bi-level Neural Volume Fusion
Dense 3D reconstruction from a stream of depth images is the key to many mixed reality and robotic applications.
ScalableViT: Rethinking the Context-oriented Generalization of Vision Transformer
The vanilla self-attention mechanism inherently relies on pre-defined and steadfast computational dimensions.
YouMVOS: An Actor-Centric Multi-Shot Video Object Segmentation Dataset
Many video understanding tasks require analyzing multi-shot videos, but existing datasets for video object segmentation (VOS) only consider single-shot videos.
Semantic-Aware Auto-Encoders for Self-Supervised Representation Learning
Inspired by perceptual learning that could use cross-view learning to perceive concepts and semantics, we propose a novel AE that could learn semantic-aware representation via cross-view image reconstruction.
LAVT: Language-Aware Vision Transformer for Referring Image Segmentation
Referring image segmentation is a fundamental vision-language task that aims to segment out an object referred to by a natural language expression from an image.
Ranked #3 on
Referring Expression Segmentation
on RefCOCOg-test
DenseCLIP: Language-Guided Dense Prediction with Context-Aware Prompting
In this work, we present a new framework for dense prediction by implicitly and explicitly leveraging the pre-trained knowledge from CLIP.
Hierarchical interaction network for video object segmentation from referring expressions
In this paper, we investigate the problem of video object segmentation from referring expressions (VOSRE).
Ranked #1 on
Referring Expression Segmentation
on J-HMDB
(Precision@0.9 metric)
Optical Flow Estimation
Referring Expression Segmentation
+3
Unsupervised Embedding Learning from Uncertainty Momentum Modeling
We justify that the performance limitation is caused by the gradient vanishing on these sample outliers.
Breaking Shortcut: Exploring Fully Convolutional Cycle-Consistency for Video Correspondence Learning
Previous cycle-consistency correspondence learning methods usually leverage image patches for training.
Uncertainty-aware Score Distribution Learning for Action Quality Assessment
Assessing action quality from videos has attracted growing attention in recent years.
Ranked #4 on
Action Quality Assessment
on AQA-7
Comprehensive Instructional Video Analysis: The COIN Dataset and Performance Evaluation
We believe the introduction of the COIN dataset will promote the future in-depth research on instructional video analysis for the community.
COIN: A Large-scale Dataset for Comprehensive Instructional Video Analysis
There are substantial instructional videos on the Internet, which enables us to acquire knowledge for completing various tasks.
Deep Progressive Reinforcement Learning for Skeleton-Based Action Recognition
In this paper, we propose a deep progressive reinforcement learning (DPRL) method for action recognition in skeleton-based videos, which aims to distil the most informative frames and discard ambiguous frames in sequences for recognizing actions.
Ranked #3 on
Skeleton Based Action Recognition
on UT-Kinect
