Search Results for author:
Found 24 papers, 15 papers with code
Beyond Raw Videos: Understanding Edited Videos with Large Multimodal Model
However, a large portion of videos in real-world applications are edited videos, \textit{e. g.}, users usually cut and add effects/modifications to the raw video before publishing it on social media platforms.
CuMo: Scaling Multimodal LLM with Co-Upcycled Mixture-of-Experts
Recent advancements in Multimodal Large Language Models (LLMs) have focused primarily on scaling by increasing text-image pair data and enhancing LLMs to improve performance on multimodal tasks.
Ranked #1 on
visual instruction following
on LLaVA-Bench
Edit3K: Universal Representation Learning for Video Editing Components
Each video in our dataset is rendered by various image/video materials with a single editing component, which supports atomic visual understanding of different editing components.
TopNet: Transformer-based Object Placement Network for Image Compositing
Given a background image and a segmented object, the goal is to train a model to predict plausible placements (location and scale) of the object for compositing.
$R^{2}$Former: Unified $R$etrieval and $R$eranking Transformer for Place Recognition
Visual Place Recognition (VPR) estimates the location of query images by matching them with images in a reference database.
R2Former: Unified Retrieval and Reranking Transformer for Place Recognition
Visual Place Recognition (VPR) estimates the location of query images by matching them with images in a reference database.
GALA: Toward Geometry-and-Lighting-Aware Object Search for Compositing
To move a step further, this paper proposes GALA (Geometry-and-Lighting-Aware), a generic foreground object search method with discriminative modeling on geometry and lighting compatibility for open-world image compositing.
TransGeo: Transformer Is All You Need for Cross-view Image Geo-localization
It does not rely on polar transform and infers faster than CNN-based methods.
Ranked #3 on
Image-Based Localization
on VIGOR Cross Area
BDANet: Multiscale Convolutional Neural Network with Cross-directional Attention for Building Damage Assessment from Satellite Images
With a pair of pre- and post-disaster satellite images, building damage assessment aims at predicting the extent of damage to buildings.
Ranked #2 on
2D Semantic Segmentation
on xBD
MutualNet: Adaptive ConvNet via Mutual Learning from Different Model Configurations
MutualNet is a general training methodology that can be applied to various network structures (e. g., 2D networks: MobileNets, ResNet, 3D networks: SlowFast, X3D) and various tasks (e. g., image classification, object detection, segmentation, and action recognition), and is demonstrated to achieve consistent improvements on a variety of datasets.
Consistency-based Active Learning for Object Detection
Unlike most recent works that focused on applying active learning for image classification, we propose an effective Consistency-based Active Learning method for object Detection (CALD), which fully explores the consistency between original and augmented data.
3D Human Pose Estimation with Spatial and Temporal Transformers
Transformer architectures have become the model of choice in natural language processing and are now being introduced into computer vision tasks such as image classification, object detection, and semantic segmentation.
Ranked #13 on
3D Human Pose Estimation
on HumanEva-I
Deep Learning-Based Human Pose Estimation: A Survey
Furthermore, 2D and 3D human pose estimation datasets and evaluation metrics are included.
VIGOR: Cross-View Image Geo-localization beyond One-to-one Retrieval
In this paper, we redefine this problem with a more realistic assumption that the query image can be arbitrary in the area of interest and the reference images are captured before the queries emerge.
A3D: Adaptive 3D Networks for Video Action Recognition
Even under the same computational constraints, the performance of our adaptive networks can be significantly boosted over the baseline counterparts by the mutual training along three dimensions.
Cross-directional Feature Fusion Network for Building Damage Assessment from Satellite Imagery
Fast and effective responses are required when a natural disaster (e. g., earthquake, hurricane, etc.)
Ranked #3 on
2D Semantic Segmentation
on xBD
Efficient Deep Learning of Non-local Features for Hyperspectral Image Classification
Therefore, to incorporate the long-range contextual information, a deep fully convolutional network (FCN) with an efficient non-local module, named ENL-FCN, is proposed for HSI classification.
GradAug: A New Regularization Method for Deep Neural Networks
The key idea is utilizing randomly transformed training samples to regularize a set of sub-networks, which are originated by sampling the width of the original network, in the training process.
Revisiting Street-to-Aerial View Image Geo-localization and Orientation Estimation
Street-to-aerial image geo-localization, which matches a query street-view image to the GPS-tagged aerial images in a reference set, has attracted increasing attention recently.
Density Map Guided Object Detection in Aerial Images
Specifically, we propose a Density-Map guided object detection Network (DMNet), which is inspired from the observation that the object density map of an image presents how objects distribute in terms of the pixel intensity of the map.
Video Anomaly Detection for Smart Surveillance
Temporal localization (i. e. indicating the start and end frames of the anomaly event in a video) is referred to as frame-level detection.
MutualNet: Adaptive ConvNet via Mutual Learning from Network Width and Resolution
We propose the width-resolution mutual learning method (MutualNet) to train a network that is executable at dynamic resource constraints to achieve adaptive accuracy-efficiency trade-offs at runtime.
Visual Explanation for Deep Metric Learning
This work explores the visual explanation for deep metric learning and its applications.
A closer look at network resolution for efficient network design
We propose a framework to mutually learn from different input resolutions and network widths.
