Search Results for author:
Found 27 papers, 19 papers with code
UCDR-Adapter: Exploring Adaptation of Pre-Trained Vision-Language Models for Universal Cross-Domain Retrieval
Second, Target Prompt Generation creates dynamic prompts by attending to masked source prompts, enabling seamless adaptation to unseen domains and classes.
GLDesigner: Leveraging Multi-Modal LLMs as Designer for Enhanced Aesthetic Text Glyph Layouts
Text logo design heavily relies on the creativity and expertise of professional designers, in which arranging element layouts is one of the most important procedures.
POPoS: Improving Efficient and Robust Facial Landmark Detection with Parallel Optimal Position Search
(2) To enhance the pseudo-range accuracy of selected anchor points, a new loss function, named multilateration anchor loss, is proposed.
MetaDesigner: Advancing Artistic Typography Through AI-Driven, User-Centric, and Multilingual WordArt Synthesis
MetaDesigner introduces a transformative framework for artistic typography synthesis, powered by Large Language Models (LLMs) and grounded in a user-centric design paradigm.
Human-Aware Vision-and-Language Navigation: Bridging Simulation to Reality with Dynamic Human Interactions
Vision-and-Language Navigation (VLN) aims to develop embodied agents that navigate based on human instructions.
Emotion-LLaMA: Multimodal Emotion Recognition and Reasoning with Instruction Tuning
Accurate emotion perception is crucial for various applications, including human-computer interaction, education, and counseling.
UMETTS: A Unified Framework for Emotional Text-to-Speech Synthesis with Multimodal Prompts
Emotional Text-to-Speech (E-TTS) synthesis has garnered significant attention in recent years due to its potential to revolutionize human-computer interaction.
Exploring Dynamic Transformer for Efficient Object Tracking
For instance, DyTrack obtains 64. 9% AUC on LaSOT with a speed of 256 fps.
DyRoNet: Dynamic Routing and Low-Rank Adapters for Autonomous Driving Streaming Perception
The advancement of autonomous driving systems hinges on the ability to achieve low-latency and high-accuracy perception.
Multi-modal Instruction Tuned LLMs with Fine-grained Visual Perception
Multimodal Large Language Model (MLLMs) leverages Large Language Models as a cognitive framework for diverse visual-language tasks.
WordArt Designer API: User-Driven Artistic Typography Synthesis with Large Language Models on ModelScope
This paper introduces the WordArt Designer API, a novel framework for user-driven artistic typography synthesis utilizing Large Language Models (LLMs) on ModelScope.
Tracking with Human-Intent Reasoning
The perception component then generates the tracking results based on the embeddings.
Ranked #5 on
Referring Video Object Segmentation
on ReVOS
AnyText: Multilingual Visual Text Generation And Editing
Based on AnyWord-3M dataset, we propose AnyText-benchmark for the evaluation of visual text generation accuracy and quality.
Ranked #6 on
Image Generation
on TextAtlasEval
WordArt Designer: User-Driven Artistic Typography Synthesis using Large Language Models
This paper introduces WordArt Designer, a user-driven framework for artistic typography synthesis, relying on the Large Language Model (LLM).
DCPT: Darkness Clue-Prompted Tracking in Nighttime UAVs
To address this, we propose a novel architecture called Darkness Clue-Prompted Tracking (DCPT) that achieves robust UAV tracking at night by efficiently learning to generate darkness clue prompts.
Refined Temporal Pyramidal Compression-and-Amplification Transformer for 3D Human Pose Estimation
Accurately estimating the 3D pose of humans in video sequences requires both accuracy and a well-structured architecture.
Ranked #10 on
3D Human Pose Estimation
on HumanEva-I
PoSynDA: Multi-Hypothesis Pose Synthesis Domain Adaptation for Robust 3D Human Pose Estimation
Typically, PoSynDA uses a diffusion-inspired structure to simulate 3D pose distribution in the target domain.
Towards Deeply Unified Depth-aware Panoptic Segmentation with Bi-directional Guidance Learning
Our method sets the new state of the art for depth-aware panoptic segmentation on both Cityscapes-DVPS and SemKITTI-DVPS datasets.
Tracking Anything in High Quality
To further improve the quality of tracking masks, a pretrained MR model is employed to refine the tracking results.
Ranked #5 on
Semi-Supervised Video Object Segmentation
on YouTube-VOS 2019
(using extra training data)
KeyPosS: Plug-and-Play Facial Landmark Detection through GPS-Inspired True-Range Multilateration
By spearheading the integration of Multilateration with facial analysis, KeyPosS marks a paradigm shift in facial landmark detection.
Overcoming Topology Agnosticism: Enhancing Skeleton-Based Action Recognition through Redefined Skeletal Topology Awareness
As a remedy, we propose a threefold strategy: (1) We forge an innovative pathway that encodes bone connectivity by harnessing the power of graph distances.
DAMO-StreamNet: Optimizing Streaming Perception in Autonomous Driving
Real-time perception, or streaming perception, is a crucial aspect of autonomous driving that has yet to be thoroughly explored in existing research.
HDFormer: High-order Directed Transformer for 3D Human Pose Estimation
Human pose estimation is a challenging task due to its structured data sequence nature.
Ranked #89 on
3D Human Pose Estimation
on Human3.6M
Optimal Proposal Learning for Deployable End-to-End Pedestrian Detection
End-to-end pedestrian detection focuses on training a pedestrian detection model via discarding the Non-Maximum Suppression (NMS) post-processing.
ProContEXT: Exploring Progressive Context Transformer for Tracking
Existing Visual Object Tracking (VOT) only takes the target area in the first frame as a template.
Ranked #1 on
Video Object Tracking
on NT-VOT211
LongShortNet: Exploring Temporal and Semantic Features Fusion in Streaming Perception
Streaming perception is a critical task in autonomous driving that requires balancing the latency and accuracy of the autopilot system.
Improving the Learning of Multi-column Convolutional Neural Network for Crowd Counting
By minimizing the mutual information, each column is guided to learn features with different image scales.
