Search Results for author:
Found 16 papers, 5 papers with code
Unbiased Video Scene Graph Generation via Visual and Semantic Dual Debiasing
Video Scene Graph Generation (VidSGG) aims to capture dynamic relationships among entities by sequentially analyzing video frames and integrating visual and semantic information.
KPIs 2024 Challenge: Advancing Glomerular Segmentation from Patch- to Slide-Level
Chronic kidney disease (CKD) is a major global health issue, affecting over 10% of the population and causing significant mortality.
When Should We Prefer State-to-Visual DAgger Over Visual Reinforcement Learning?
Visual reinforcement learning is a promising approach that directly trains policies from visual observations, although it faces challenges in sample efficiency and computational costs.
Evaluating and Advancing Multimodal Large Language Models in Ability Lens
To address this, we introduce \textbf{AbilityLens}, a unified benchmark designed to evaluate MLLMs across six key perception abilities, focusing on both accuracy and stability, with each ability encompassing diverse question types, domains, and metrics.
Effectively Enhancing Vision Language Large Models by Prompt Augmentation and Caption Utilization
may change model output and make the output hallucinate again.
Localization and Expansion: A Decoupled Framework for Point Cloud Few-shot Semantic Segmentation
Point cloud few-shot semantic segmentation (PC-FSS) aims to segment targets of novel categories in a given query point cloud with only a few annotated support samples.
Machine learning-based input-augmented Koopman modeling and predictive control of nonlinear processes
In this work, we propose an input-augmented Koopman modeling and model predictive control approach.
The Multi-modality Cell Segmentation Challenge: Towards Universal Solutions
This benchmark and the improved algorithm offer promising avenues for more accurate and versatile cell analysis in microscopy imaging.
Recognize Anything: A Strong Image Tagging Model
We are releasing the RAM at \url{https://recognize-anything. github. io/} to foster the advancements of large models in computer vision.
Extracting knowledge from features with multilevel abstraction
Knowledge distillation aims at transferring the knowledge from a large teacher model to a small student model with great improvements of the performance of the student model.
FaceInpainter: High Fidelity Face Adaptation to Heterogeneous Domains
In this work, we propose a novel two-stage framework named FaceInpainter to implement controllable Identity-Guided Face Inpainting (IGFI) under heterogeneous domains.
Information Bottleneck Disentanglement for Identity Swapping
In this work, we propose a novel information disentangling and swapping network, called InfoSwap, to extract the most expressive information for identity representation from a pre-trained face recognition model.
Box Re-Ranking: Unsupervised False Positive Suppression for Domain Adaptive Pedestrian Detection
Therefore, it yields our attention to suppress false positive in each target domain in an unsupervised way.
A Neural Network for Detailed Human Depth Estimation from a Single Image
To achieve this goal, we separate the depth map into a smooth base shape and a residual detail shape and design a network with two branches to regress them respectively.
Action Recognition Based on Joint Trajectory Maps Using Convolutional Neural Networks
Recently, Convolutional Neural Networks (ConvNets) have shown promising performances in many computer vision tasks, especially image-based recognition.
Combining ConvNets with Hand-Crafted Features for Action Recognition Based on an HMM-SVM Classifier
This paper proposes a new framework for RGB-D-based action recognition that takes advantages of hand-designed features from skeleton data and deeply learned features from depth maps, and exploits effectively both the local and global temporal information.
