Search Results for author:
Found 27 papers, 8 papers with code
EmoVIT: Revolutionizing Emotion Insights with Visual Instruction Tuning
Visual Instruction Tuning represents a novel learning paradigm involving the fine-tuning of pre-trained language models using task-specific instructions.
Lightweight Deep Learning for Resource-Constrained Environments: A Survey
Over the past decade, the dominance of deep learning has prevailed across various domains of artificial intelligence, including natural language processing, computer vision, and biomedical signal processing.
MonoTAKD: Teaching Assistant Knowledge Distillation for Monocular 3D Object Detection
Subsequently, we introduce the cross-modal residual distillation to transfer the 3D spatial cues.
DQ-DETR: DETR with Dynamic Query for Tiny Object Detection
Despite previous DETR-like methods having performed successfully in generic object detection, tiny object detection is still a challenging task for them since the positional information of object queries is not customized for detecting tiny objects, whose scale is extraordinarily smaller than general objects.
MobileVidFactory: Automatic Diffusion-Based Social Media Video Generation for Mobile Devices from Text
In the basic generation, we take advantage of the pretrained image diffusion model, and adapt it to a high-quality open-domain vertical video generator for mobile devices.
MovieFactory: Automatic Movie Creation from Text using Large Generative Models for Language and Images
To generate videos, we extend the capabilities of a pretrained text-to-image diffusion model through a two-stage process.
Size Does Matter: Size-aware Virtual Try-on via Clothing-oriented Transformation Try-on Network
COTTON leverages clothing structure with landmarks and segmentation to design a novel landmark-guided transformation for precisely deforming clothes, allowing for size adjustment during try-on.
Most Important Person-guided Dual-branch Cross-Patch Attention for Group Affect Recognition
Then, the Cross-Patch Attention module is proposed to fuse the features of MIP and global context together to complement each other.
Power Efficient Video Super-Resolution on Mobile NPUs with Deep Learning, Mobile AI & AIM 2022 challenge: Report
While numerous solutions have been proposed for this problem, they are usually quite computationally demanding, demonstrating low FPS rates and power efficiency on mobile devices.
Vision Transformers: State of the Art and Research Challenges
Transformers have achieved great success in natural language processing.
Fast Vehicle Detection and Tracking on Fisheye Traffic Monitoring Video using CNN and Bounding Box Propagation
We design a fast car detection and tracking algorithm for traffic monitoring fisheye video mounted on crossroads.
Mask or Non-Mask? Robust Face Mask Detector via Triplet-Consistency Representation Learning
To automate the monitoring process, one of the promising solutions is to leverage existing object detection models to detect the faces with or without masks.
Technical Report for Valence-Arousal Estimation in ABAW2 Challenge
In this work, we describe our method for tackling the valence-arousal estimation challenge from ABAW2 ICCV-2021 Competition.
Multimodal Deep Learning Framework for Image Popularity Prediction on Social Media
In this study, motivated by multimodal learning, which uses information from various modalities, and the current success of convolutional neural networks (CNNs) in various fields, we propose a deep learning model, called visual-social convolutional neural network (VSCNN), which predicts the popularity of a posted image by incorporating various types of visual and social features into a unified network model.
ZYELL-NCTU NetTraffic-1.0: A Large-Scale Dataset for Real-World Network Anomaly Detection
Network security has been an active research topic for long.
Template-Free Try-on Image Synthesis via Semantic-guided Optimization
Afterward, given an in-shop clothing image, a user image, and a synthesized pose, we propose a novel model for synthesizing a human try-on image with the target clothing in the best fitting pose.
Spatiotemporal Dilated Convolution with Uncertain Matching for Video-based Crowd Estimation
In this paper, we propose a novel SpatioTemporal convolutional Dense Network (STDNet) to address the video-based crowd counting problem, which contains the decomposition of 3D convolution and the 3D spatiotemporal dilated dense convolution to alleviate the rapid growth of the model size caused by the Conv3D layer.
DAF:re: A Challenging, Crowd-Sourced, Large-Scale, Long-Tailed Dataset For Anime Character Recognition
In this work we tackle the challenging problem of anime character recognition.
FashionMirror: Co-Attention Feature-Remapping Virtual Try-On With Sequential Template Poses
In the second stage, we first remove the clothes on the source human via the removed mask and warp the clothing features conditioning on the try-on clothing mask to fit the next frame human.
Naturalistic Physical Adversarial Patch for Object Detectors
Most prior works on physical adversarial attacks mainly focus on the attack performance but seldom enforce any restrictions over the appearance of the generated adversarial patches.
An Overview of Facial Micro-Expression Analysis: Data, Methodology and Challenge
Facial micro-expressions indicate brief and subtle facial movements that appear during emotional communication.
Micro Expression Recognition
Micro-Expression Recognition
+2
MER-GCN: Micro Expression Recognition Based on Relation Modeling with Graph Convolutional Network
Micro-Expression (ME) is the spontaneous, involuntary movement of a face that can reveal the true feeling.
Fashion Meets Computer Vision: A Survey
Fashion is the way we present ourselves to the world and has become one of the world's largest industries.
SMP Challenge: An Overview of Social Media Prediction Challenge 2019
In the SMP Challenge at ACM Multimedia 2019, we introduce a novel prediction task Temporal Popularity Prediction, which focuses on predicting future interaction or attractiveness (in terms of clicks, views or likes etc.)
Joint Enhancement and Denoising Method via Sequential Decomposition
Many low-light enhancement methods ignore intensive noise in original images.
Sequential Prediction of Social Media Popularity with Deep Temporal Context Networks
With a joint embedding network, we obtain a unified deep representation of multi-modal user-post data in a common embedding space.
Time Matters: Multi-scale Temporalization of Social Media Popularity
We evaluate our approach on two large-scale Flickr image datasets with over 1. 8 million photos in total, for the task of popularity prediction.
