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Found 20 papers, 13 papers with code
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.
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.
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.
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 #74 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.
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.
