Search Results for author:
Found 29 papers, 18 papers with code
ID-Aligner: Enhancing Identity-Preserving Text-to-Image Generation with Reward Feedback Learning
The rapid development of diffusion models has triggered diverse applications.
DiffusionGPT: LLM-Driven Text-to-Image Generation System
Diffusion models have opened up new avenues for the field of image generation, resulting in the proliferation of high-quality models shared on open-source platforms.
Dual-View Data Hallucination with Semantic Relation Guidance for Few-Shot Image Recognition
Specifically, an instance-view data hallucination module hallucinates each sample of a novel class to generate new data by employing local semantic correlated attention and global semantic feature fusion derived from base classes.
SQLNet: Scale-Modulated Query and Localization Network for Few-Shot Class-Agnostic Counting
In the localization stage, the Scale-aware Multi-head Localization (SAML) module utilizes the query tensor to predict the confidence, location, and size of each potential object.
Contrastive Transformer Learning with Proximity Data Generation for Text-Based Person Search
Moreover, we propose a proximity data generation (PDG) module to automatically produce more diverse data for cross-modal training.
SketchBodyNet: A Sketch-Driven Multi-faceted Decoder Network for 3D Human Reconstruction
Compared with natural images, freehand sketches are much more flexible to depict various shapes, providing a high potential and valuable way for 3D human reconstruction.
Spatial-Temporal Knowledge-Embedded Transformer for Video Scene Graph Generation
In this work, we propose a spatial-temporal knowledge-embedded transformer (STKET) that incorporates the prior spatial-temporal knowledge into the multi-head cross-attention mechanism to learn more representative relationship representations.
Control-A-Video: Controllable Text-to-Video Generation with Diffusion Models
Based on a pre-trained conditional text-to-image (T2I) diffusion model, our model aims to generate videos conditioned on a sequence of control signals, such as edge or depth maps.
Multi-object Video Generation from Single Frame Layouts
In this paper, we study video synthesis with emphasis on simplifying the generation conditions.
Category-Adaptive Label Discovery and Noise Rejection for Multi-label Image Recognition with Partial Positive Labels
To reject noisy labels, recent works regard large loss samples as noise but ignore the semantic correlation different multi-label images.
Dual-Perspective Semantic-Aware Representation Blending for Multi-Label Image Recognition with Partial Labels
Specifically, an instance-perspective representation blending (IPRB) module is designed to blend the representations of the known labels in an image with the representations of the corresponding unknown labels in another image to complement these unknown labels.
Image Classification
Multi-label Image Recognition with Partial Labels
Semantic Representation and Dependency Learning for Multi-Label Image Recognition
We also design an object erasing (OE) module to implicitly learn semantic dependency among categories by erasing semantic-aware regions to regularize the network training.
Semantic-Aware Representation Blending for Multi-Label Image Recognition with Partial Labels
However, these algorithms depend on sufficient multi-label annotations to train the models, leading to poor performance especially with low known label proportion.
Structured Semantic Transfer for Multi-Label Recognition with Partial Labels
To reduce the annotation cost, we propose a structured semantic transfer (SST) framework that enables training multi-label recognition models with partial labels, i. e., merely some labels are known while other labels are missing (also called unknown labels) per image.
AU-Expression Knowledge Constrained Representation Learning for Facial Expression Recognition
In this work, we explore the correlations among the action units and facial expressions, and devise an AU-Expression Knowledge Constrained Representation Learning (AUE-CRL) framework to learn the AU representations without AU annotations and adaptively use representations to facilitate facial expression recognition.
Facial Expression Recognition
Facial Expression Recognition (FER)
+1
Cross-Modal Collaborative Representation Learning and a Large-Scale RGBT Benchmark for Crowd Counting
Extensive experiments conducted on the RGBT-CC benchmark demonstrate the effectiveness of our framework for RGBT crowd counting.
Knowledge-Guided Multi-Label Few-Shot Learning for General Image Recognition
The framework exploits prior knowledge to guide adaptive information propagation among different categories to facilitate multi-label analysis and reduce the dependency of training samples.
Few-Shot Learning
Multi-label Image Recognition with Partial Labels
Adversarial Graph Representation Adaptation for Cross-Domain Facial Expression Recognition
However, most of these works focus on holistic feature adaptation, and they ignore local features that are more transferable across different datasets.
Cross-Domain Facial Expression Recognition
Facial Expression Recognition (FER)
Cross-Domain Facial Expression Recognition: A Unified Evaluation Benchmark and Adversarial Graph Learning
Although each declares to achieve superior performance, fair comparisons are lacking due to the inconsistent choices of the source/target datasets and feature extractors.
Ranked #1 on
Cross-Domain Facial Expression Recognition
on Source: AFE, Target: CK+, JAFFE, SFEW2.0, FER2013, ExpW
Cross-Domain Facial Expression Recognition
Domain Adaptation
+3
Fine-Grained Image Captioning with Global-Local Discriminative Objective
This is primarily due to (i) the conservative characteristic of traditional training objectives that drives the model to generate correct but hardly discriminative captions for similar images and (ii) the uneven word distribution of the ground-truth captions, which encourages generating highly frequent words/phrases while suppressing the less frequent but more concrete ones.
Efficient Crowd Counting via Structured Knowledge Transfer
Crowd counting is an application-oriented task and its inference efficiency is crucial for real-world applications.
Physical-Virtual Collaboration Modeling for Intra-and Inter-Station Metro Ridership Prediction
To address this problem, we model a metro system as graphs with various topologies and propose a unified Physical-Virtual Collaboration Graph Network (PVCGN), which can effectively learn the complex ridership patterns from the tailor-designed graphs.
Knowledge Graph Transfer Network for Few-Shot Recognition
In this work, we represent the semantic correlations in the form of structured knowledge graph and integrate this graph into deep neural networks to promote few-shot learning by a novel Knowledge Graph Transfer Network (KGTN).
Learning Semantic-Specific Graph Representation for Multi-Label Image Recognition
Recognizing multiple labels of images is a practical and challenging task, and significant progress has been made by searching semantic-aware regions and modeling label dependency.
Ranked #8 on
Multi-Label Classification
on PASCAL VOC 2007
Instance-Aware Representation Learning and Association for Online Multi-Person Tracking
Multi-Person Tracking (MPT) is often addressed within the detection-to-association paradigm.
Multi-column Point-CNN for Sketch Segmentation
Traditional sketch segmentation methods mainly rely on handcrafted features and complicate models, and their performance is far from satisfactory due to the abstract representation of sketches.
ADCrowdNet: An Attention-injective Deformable Convolutional Network for Crowd Understanding
We propose an attention-injective deformable convolutional network called ADCrowdNet for crowd understanding that can address the accuracy degradation problem of highly congested noisy scenes.
Ranked #2 on
Crowd Counting
on TRANCOS
Structured Inhomogeneous Density Map Learning for Crowd Counting
In this paper, we aim at tackling the problem of crowd counting in extremely high-density scenes, which contain hundreds, or even thousands of people.
Learning Deep Similarity Models with Focus Ranking for Fabric Image Retrieval
Fabric image retrieval is beneficial to many applications including clothing searching, online shopping and cloth modeling.
