Search Results for author:
Found 36 papers, 10 papers with code
LLM-AD: Large Language Model based Audio Description System
The development of Audio Description (AD) has been a pivotal step forward in making video content more accessible and inclusive.
Asynchronous Microphone Array Calibration using Hybrid TDOA Information
Asynchronous microphone array calibration is a prerequisite for many audition robot applications.
Training Diffusion Models Towards Diverse Image Generation with Reinforcement Learning
We also show the effectiveness of our RL fine-tuning framework on enhancing the diversity of image generation with different types of diffusion models including class-conditional models and text-conditional models e. g. StableDiffusion.
RefineVIS: Video Instance Segmentation with Temporal Attention Refinement
We introduce a novel framework called RefineVIS for Video Instance Segmentation (VIS) that achieves good object association between frames and accurate segmentation masks by iteratively refining the representations using sequence context.
Ranked #5 on
Video Instance Segmentation
on YouTube-VIS 2021
(using extra training data)
Adaptive Human Matting for Dynamic Videos
The most recent efforts in video matting have focused on eliminating trimap dependency since trimap annotations are expensive and trimap-based methods are less adaptable for real-time applications.
Binary Latent Diffusion
In this paper, we show that a binary latent space can be explored for compact yet expressive image representations.
Energy-Inspired Self-Supervised Pretraining for Vision Models
In the proposed framework, we model energy estimation and data restoration as the forward and backward passes of a single network without any auxiliary components, e. g., an extra decoder.
Consistent Video Instance Segmentation with Inter-Frame Recurrent Attention
We propose a consistent end-to-end video instance segmentation framework with Inter-Frame Recurrent Attention to model both the temporal instance consistency for adjacent frames and the global temporal context.
Augmenting Knowledge Graphs for Better Link Prediction
Experiments on legacy benchmarks and a new large benchmark, DWD, show that augmenting the knowledge graph with quantities and years is beneficial for predicting both entities and numbers, as KGA outperforms the vanilla models and other relevant baselines.
Deep Frequency Filtering for Domain Generalization
Improving the generalization ability of Deep Neural Networks (DNNs) is critical for their practical uses, which has been a longstanding challenge.
Parameter Identification of a PN-Guided Incoming Missile Using an Improved Multiple-Model Mechanism
An active defense against an incoming missile requires information of it, including a guidance law parameter and a first-order lateral time constant.
Lifelong Unsupervised Domain Adaptive Person Re-identification with Coordinated Anti-forgetting and Adaptation
In this paper, to address more practical scenarios, we propose a new task, Lifelong Unsupervised Domain Adaptive (LUDA) person ReID.
Domain Adaptive Person Re-Identification
Knowledge Distillation
+4
MMPTRACK: Large-scale Densely Annotated Multi-camera Multiple People Tracking Benchmark
This dataset provides a more reliable benchmark of multi-camera, multi-object tracking systems in cluttered and crowded environments.
Ranked #2 on
Object Tracking
on MMPTRACK
TransMOT: Spatial-Temporal Graph Transformer for Multiple Object Tracking
TransMOT effectively models the interactions of a large number of objects by arranging the trajectories of the tracked objects as a set of sparse weighted graphs, and constructing a spatial graph transformer encoder layer, a temporal transformer encoder layer, and a spatial graph transformer decoder layer based on the graphs.
Ranked #2 on
Multi-Object Tracking
on 2DMOT15
(using extra training data)
A Learning-Based Computational Impact Time Guidance
This paper investigates the problem of impact-time-control and proposes a learning-based computational guidance algorithm to solve this problem.
Coarse Graining Molecular Dynamics with Graph Neural Networks
5, 755 (2019)] demonstrated that the existence of such a variational limit enables the use of a supervised machine learning framework to generate a coarse-grained force field, which can then be used for simulation in the coarse-grained space.
Ensemble Learning of Coarse-Grained Molecular Dynamics Force Fields with a Kernel Approach
Using ensemble learning and stratified sampling, we propose a 2-layer training scheme that enables GDML to learn an effective coarse-grained model.
RC-DARTS: Resource Constrained Differentiable Architecture Search
In this paper, we propose the resource constrained differentiable architecture search (RC-DARTS) method to learn architectures that are significantly smaller and faster while achieving comparable accuracy.
Adversarial Examples Improve Image Recognition
We show that AdvProp improves a wide range of models on various image recognition tasks and performs better when the models are bigger.
Ranked #4 on
Domain Generalization
on VizWiz-Classification
Machine Learning of coarse-grained Molecular Dynamics Force Fields
We show that CGnets can capture all-atom explicit-solvent free energy surfaces with models using only a few coarse-grained beads and no solvent, while classical coarse-graining methods fail to capture crucial features of the free energy surface.
NOTE-RCNN: NOise Tolerant Ensemble RCNN for Semi-Supervised Object Detection
Comparing to standard Faster RCNN, it contains three highlights: an ensemble of two classification heads and a distillation head to avoid overfitting on noisy labels and improve the mining precision, masking the negative sample loss in box predictor to avoid the harm of false negative labels, and training box regression head only on seed annotations to eliminate the harm from inaccurate boundaries of mined bounding boxes.
Training Generative Adversarial Networks via Primal-Dual Subgradient Methods: A Lagrangian Perspective on GAN
This formulation shows the connection between the standard GAN training process and the primal-dual subgradient methods for convex optimization.
Kernel Pooling for Convolutional Neural Networks
We demonstrate how to approximate kernels such as Gaussian RBF up to a given order using compact explicit feature maps in a parameter-free manner.
Localizing by Describing: Attribute-Guided Attention Localization for Fine-Grained Recognition
By designing a novel reward strategy, we are able to learn to locate regions that are spatially and semantically distinctive with reinforcement learning algorithm.
CNN-RNN: A Unified Framework for Multi-label Image Classification
While deep convolutional neural networks (CNNs) have shown a great success in single-label image classification, it is important to note that real world images generally contain multiple labels, which could correspond to different objects, scenes, actions and attributes in an image.
Fully Convolutional Attention Networks for Fine-Grained Recognition
Fine-grained recognition is challenging due to its subtle local inter-class differences versus large intra-class variations such as poses.
Look and Think Twice: Capturing Top-Down Visual Attention With Feedback Convolutional Neural Networks
While feedforward deep convolutional neural networks (CNNs) have been a great success in computer vision, it is important to remember that the human visual contex contains generally more feedback connections than foward connections.
ABC-CNN: An Attention Based Convolutional Neural Network for Visual Question Answering
ABC-CNN determines an attention map for an image-question pair by convolving the image feature map with configurable convolutional kernels derived from the question's semantics.
Attention to Scale: Scale-aware Semantic Image Segmentation
We adapt a state-of-the-art semantic image segmentation model, which we jointly train with multi-scale input images and the attention model.
Video Paragraph Captioning Using Hierarchical Recurrent Neural Networks
The sentence generator produces one simple short sentence that describes a specific short video interval.
Learning like a Child: Fast Novel Visual Concept Learning from Sentence Descriptions of Images
In particular, we propose a transposed weight sharing scheme, which not only improves performance on image captioning, but also makes the model more suitable for the novel concept learning task.
Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN)
In this paper, we present a multimodal Recurrent Neural Network (m-RNN) model for generating novel image captions.
Cross-view Action Modeling, Learning and Recognition
We present a novel multiview spatio-temporal AND-OR graph (MST-AOG) representation for cross-view action recognition, i. e., the recognition is performed on the video from an unknown and unseen view.
Learning Fine-grained Image Similarity with Deep Ranking
This paper proposes a deep ranking model that employs deep learning techniques to learn similarity metric directly from images. It has higher learning capability than models based on hand-crafted features.
Visual Tracking via Locality Sensitive Histograms
A robust tracking framework based on the locality sensitive histograms is proposed, which consists of two main components: a new feature for tracking that is robust to illumination changes and a novel multi-region tracking algorithm that runs in realtime even with hundreds of regions.
