Search Results for author:
Found 25 papers, 7 papers with code
G3R: Generating Rich and Fine-grained mmWave Radar Data from 2D Videos for Generalized Gesture Recognition
Millimeter wave radar is gaining traction recently as a promising modality for enabling pervasive and privacy-preserving gesture recognition.
NTIRE 2024 Challenge on Short-form UGC Video Quality Assessment: Methods and Results
This paper reviews the NTIRE 2024 Challenge on Shortform UGC Video Quality Assessment (S-UGC VQA), where various excellent solutions are submitted and evaluated on the collected dataset KVQ from popular short-form video platform, i. e., Kuaishou/Kwai Platform.
Region-Aware Exposure Consistency Network for Mixed Exposure Correction
Exposure correction aims to enhance images suffering from improper exposure to achieve satisfactory visual effects.
Learning Exposure Correction in Dynamic Scenes
While image exposure correction is a popular topic, the video counterpart is less explored in the literature.
Mutual Distillation Learning For Person Re-Identification
With the rapid advancements in deep learning technologies, person re-identification (ReID) has witnessed remarkable performance improvements.
Uncovering the human motion pattern: Pattern Memory-based Diffusion Model for Trajectory Prediction
To uncover latent motion patterns in human behavior, we introduce a novel memory-based method, named Motion Pattern Priors Memory Network.
Multi-Stage Contrastive Regression for Action Quality Assessment
In recent years, there has been growing interest in the video-based action quality assessment (AQA).
Efficient Cloud-edge Collaborative Inference for Object Re-identification
Therefore, we pioneer a cloud-edge collaborative inference framework for ReID systems and particularly propose a distribution-aware correlation modeling network (DaCM) to make the desired image return to the cloud server as soon as possible via learning to model the spatial-temporal correlations among instances.
VIoTGPT: Learning to Schedule Vision Tools towards Intelligent Video Internet of Things
Video Internet of Things (VIoT) has shown full potential in collecting an unprecedented volume of video data.
Weakly-Supervised Temporal Action Localization by Inferring Salient Snippet-Feature
Weakly-supervised temporal action localization aims to locate action regions and identify action categories in untrimmed videos simultaneously by taking only video-level labels as the supervision.
Pseudo Label
Weakly-supervised Temporal Action Localization
+1
SGFormer: Semantic Graph Transformer for Point Cloud-based 3D Scene Graph Generation
In this paper, we propose a novel model called SGFormer, Semantic Graph TransFormer for point cloud-based 3D scene graph generation.
Unsupervised Self-Driving Attention Prediction via Uncertainty Mining and Knowledge Embedding
Predicting attention regions of interest is an important yet challenging task for self-driving systems.
Thinking Image Color Aesthetics Assessment: Models, Datasets and Benchmarks
We present a comprehensive study on a new task named image color aesthetics assessment (ICAA), which aims to assess color aesthetics based on human perception.
Dancing in the Dark: A Benchmark towards General Low-light Video Enhancement
To address this issue, we design a camera system and collect a high-quality low-light video dataset with multiple exposures and cameras.
You Do Not Need Additional Priors or Regularizers in Retinex-Based Low-Light Image Enhancement
The Retinex-based methods require decomposing the image into reflectance and illumination components, which is a highly ill-posed problem and there is no available ground truth.
Coarse-to-Fine Video Denoising with Dual-Stage Spatial-Channel Transformer
Meanwhile, we design a Multi-Scale Residual Structure to preserve multiple aspects of information at different stages, which contains a Temporal Features Aggregation Module to summarize the dynamic representation.
Learning to Help Emergency Vehicles Arrive Faster: A Cooperative Vehicle-Road Scheduling Approach
The ever-increasing heavy traffic congestion potentially impedes the accessibility of emergency vehicles (EVs), resulting in detrimental impacts on critical services and even safety of people's lives.
SPAP: Simultaneous Demand Prediction and Planning for Electric Vehicle Chargers in a New City
A direct idea is to leverage the urban transfer learning paradigm to learn the knowledge from a source city, then exploit it to predict charging demands, and meanwhile determine locations and amounts of slow/fast chargers for charging stations in the target city.
Language Guided Networks for Cross-modal Moment Retrieval
In this paper, we present Language Guided Networks (LGN), a new framework that leverages the sentence embedding to guide the whole process of moment retrieval.
A Real-time Action Representation with Temporal Encoding and Deep Compression
Our method achieves clear improvements on UCF101 action recognition benchmark against state-of-the-art real-time methods by 5. 4% in terms of accuracy and 2 times faster in terms of inference speed with a less than 5MB storage model.
MemNet: Memory-Efficiency Guided Neural Architecture Search with Augment-Trim learning
Recent studies on automatic neural architectures search have demonstrated significant performance, competitive to or even better than hand-crafted neural architectures.
PVSS: A Progressive Vehicle Search System for Video Surveillance Networks
In this paper, a Progressive Vehicle Search System, named as PVSS, is designed to solve the above problems.
Multi-Granularity Reasoning for Social Relation Recognition from Images
To bridge the domain gap, we propose a Multi-Granularity Reasoning framework for social relation recognition from images.
Ranked #3 on
Visual Social Relationship Recognition
on PISC
KTAN: Knowledge Transfer Adversarial Network
To transfer the knowledge of intermediate representations, we set high-level teacher feature maps as a target, toward which the student feature maps are trained.
Generalized Zero-Shot Learning for Action Recognition with Web-Scale Video Data
Motivated by this, we study the task of action recognition in surveillance video under a more realistic \emph{generalized zero-shot setting}, where testing data contains both seen and unseen classes.
