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Found 22 papers, 14 papers with code
YOLO-World: Real-Time Open-Vocabulary Object Detection
The You Only Look Once (YOLO) series of detectors have established themselves as efficient and practical tools.
UniRepLKNet: A Universal Perception Large-Kernel ConvNet for Audio, Video, Point Cloud, Time-Series and Image Recognition
1) We propose four architectural guidelines for designing large-kernel ConvNets, the core of which is to exploit the essential characteristics of large kernels that distinguish them from small kernels - they can see wide without going deep.
Ranked #1 on
Object Detection
on COCO 2017
(mAP metric)
Meta-Adapter: An Online Few-shot Learner for Vision-Language Model
Without bells and whistles, our approach outperforms the state-of-the-art online few-shot learning method by an average of 3. 6\% on eight image classification datasets with higher inference speed.
InstructDET: Diversifying Referring Object Detection with Generalized Instructions
In order to encompass common detection expressions, we involve emerging vision-language model (VLM) and large language model (LLM) to generate instructions guided by text prompts and object bbxs, as the generalizations of foundation models are effective to produce human-like expressions (e. g., describing object property, category, and relationship).
GMM: Delving into Gradient Aware and Model Perceive Depth Mining for Monocular 3D Detection
In light of the success of sample mining techniques in 2D object detection, we propose a simple yet effective mining strategy for improving depth perception in 3D object detection.
Sticker820K: Empowering Interactive Retrieval with Stickers
Therefore, we propose StickerCLIP as a benchmark model on the Sticker820K dataset.
GPT4Tools: Teaching Large Language Model to Use Tools via Self-instruction
This paper aims to efficiently enable Large Language Models (LLMs) to use multimodal tools.
BoxSnake: Polygonal Instance Segmentation with Box Supervision
Box-supervised instance segmentation has gained much attention as it requires only simple box annotations instead of costly mask or polygon annotations.
Dynamic Grained Encoder for Vision Transformers
Specifically, we propose a Dynamic Grained Encoder for vision transformers, which can adaptively assign a suitable number of queries to each spatial region.
Safety Embedded Stochastic Optimal Control of Networked Multi-Agent Systems via Barrier States
This paper presents a novel approach for achieving safe stochastic optimal control in networked multi-agent systems (MASs).
DBQ-SSD: Dynamic Ball Query for Efficient 3D Object Detection
Many point-based 3D detectors adopt point-feature sampling strategies to drop some points for efficient inference.
Simplified Analysis on Filtering Sensitivity Trade-offs in Continuous- and Discrete-Time Systems
A simplified analysis is performed on the Bode-type filtering sensitivity trade-off integrals, which capture the sensitivity characteristics of the estimate and estimation error with respect to the process input and estimated signal in continuous- and discrete-time linear time-invariant filtering systems.
Generalization of Safe Optimal Control Actions on Networked Multi-Agent Systems
The control action composition is achieved by taking a weighted mixture of the existing controllers according to the contribution of each component task.
Workshop on Autonomous Driving at CVPR 2021: Technical Report for Streaming Perception Challenge
In this report, we introduce our real-time 2D object detection system for the realistic autonomous driving scenario.
End-to-End Object Detection with Fully Convolutional Network
Mainstream object detectors based on the fully convolutional network has achieved impressive performance.
Fine-Grained Dynamic Head for Object Detection
To this end, we propose a fine-grained dynamic head to conditionally select a pixel-level combination of FPN features from different scales for each instance, which further releases the ability of multi-scale feature representation.
Rethinking Learnable Tree Filter for Generic Feature Transform
The Learnable Tree Filter presents a remarkable approach to model structure-preserving relations for semantic segmentation.
Compositionality of Linearly Solvable Optimal Control in Networked Multi-Agent Systems
The proposed approach achieves both the compositionality and optimality of control actions simultaneously within the cooperative MAS framework in both discrete- and continuous-time in a sample-efficient manner, which reduces the burden of re-computation of the optimal control solutions for the new task on the MASs.
Contraction $\mathcal{L}_1$-Adaptive Control using Gaussian Processes
We present $\mathcal{CL}_1$-$\mathcal{GP}$, a control framework that enables safe simultaneous learning and control for systems subject to uncertainties.
Learning Dynamic Routing for Semantic Segmentation
To demonstrate the superiority of the dynamic property, we compare with several static architectures, which can be modeled as special cases in the routing space.
Learnable Tree Filter for Structure-preserving Feature Transform
To this end, tree filtering modules are embedded to formulate a unified framework for semantic segmentation.
TACNet: Transition-Aware Context Network for Spatio-Temporal Action Detection
In this paper, we define these ambiguous samples as "transitional states", and propose a Transition-Aware Context Network (TACNet) to distinguish transitional states.
Ranked #7 on
Action Detection
on J-HMDB
