Latest Research
3D Face Modeling via Weakly-supervised Disentanglement Network joint Identity-consistency Prior
Generative 3D face models featuring disentangled controlling factors hold immense potential for diverse applications in computer vision and computer graphics.
List Items One by One: A New Data Source and Learning Paradigm for Multimodal LLMs
Set-of-Mark (SoM) Prompting unleashes the visual grounding capability of GPT-4V, by enabling the model to associate visual objects with tags inserted on the image.
Multimodal Information Interaction for Medical Image Segmentation
To address this issue, we introduce an innovative Multimodal Information Cross Transformer (MicFormer), which employs a dual-stream architecture to simultaneously extract features from each modality.
Lost in Recursion: Mining Rich Event Semantics in Knowledge Graphs
In this paper, we show how narratives concerning complex events can be constructed and utilized.
Commonsense Prototype for Outdoor Unsupervised 3D Object Detection
The prevalent approaches of unsupervised 3D object detection follow cluster-based pseudo-label generation and iterative self-training processes.
PAD: Patch-Agnostic Defense against Adversarial Patch Attacks
Adversarial patch attacks present a significant threat to real-world object detectors due to their practical feasibility.
A Multi-objective Optimization Benchmark Test Suite for Real-time Semantic Segmentation
To bridge the gap, we introduce a tailored streamline to transform the task of HW-NAS for real-time semantic segmentation into standard MOPs.
ConsistentID: Portrait Generation with Multimodal Fine-Grained Identity Preserving
ConsistentID comprises two key components: a multimodal facial prompt generator that combines facial features, corresponding facial descriptions and the overall facial context to enhance precision in facial details, and an ID-preservation network optimized through the facial attention localization strategy, aimed at preserving ID consistency in facial regions.
ReZero: Boosting MCTS-based Algorithms by Just-in-Time and Speedy Reanalyze
MCTS-based algorithms, such as MuZero and its derivatives, have achieved widespread success in various decision-making domains.
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.
