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Found 21 papers, 3 papers with code
TPTU-v2: Boosting Task Planning and Tool Usage of Large Language Model-based Agents in Real-world Systems
Large Language Models (LLMs) have demonstrated proficiency in addressing tasks that necessitate a combination of task planning and the usage of external tools that require a blend of task planning and the utilization of external tools, such as APIs.
To be or not to be? an exploration of continuously controllable prompt engineering
In response, we introduce ControlPE (Continuously Controllable Prompt Engineering).
MeanAP-Guided Reinforced Active Learning for Object Detection
Active learning presents a promising avenue for training high-performance models with minimal labeled data, achieved by judiciously selecting the most informative instances to label and incorporating them into the task learner.
TPTU: Large Language Model-based AI Agents for Task Planning and Tool Usage
With recent advancements in natural language processing, Large Language Models (LLMs) have emerged as powerful tools for various real-world applications.
Explore the Power of Synthetic Data on Few-shot Object Detection
To construct a representative synthetic training dataset, we maximize the diversity of the selected images via a sample-based and cluster-based method.
SeqCo-DETR: Sequence Consistency Training for Self-Supervised Object Detection with Transformers
To this end, we propose SeqCo-DETR, a novel Sequence Consistency-based self-supervised method for object DEtection with TRansformers.
An Effective Crop-Paste Pipeline for Few-shot Object Detection
Specifically, we first discover the base images which contain the FP of novel categories and select a certain amount of samples from them for the base and novel categories balance.
Explore the Power of Dropout on Few-shot Learning
The generalization power of the pre-trained model is the key for few-shot deep learning.
A Unified Framework with Meta-dropout for Few-shot Learning
Conventional training of deep neural networks usually requires a substantial amount of data with expensive human annotations.
MDFL: A UNIFIED FRAMEWORK WITH META-DROPOUT FOR FEW-SHOT LEARNING
Conventional training of deep neural networks usually requires a substantial amount of data with expensive human annotations.
Rethinking Pseudo-LiDAR Representation
Based on this observation, we design an image based CNN detector named Patch-Net, which is more generalized and can be instantiated as pseudo-LiDAR based 3D detectors.
Adapting Object Detectors with Conditional Domain Normalization
Then this domain-vector is used to encode the features from another domain through a conditional normalization, resulting in different domains' features carrying the same domain attribute.
Ranked #1 on
Unsupervised Domain Adaptation
on SIM10K to BDD100K
Monocular 3D Object Detection with Decoupled Structured Polygon Estimation and Height-Guided Depth Estimation
Monocular 3D object detection task aims to predict the 3D bounding boxes of objects based on monocular RGB images.
GS3D: An Efficient 3D Object Detection Framework for Autonomous Driving
We present an efficient 3D object detection framework based on a single RGB image in the scenario of autonomous driving.
Ranked #18 on
Vehicle Pose Estimation
on KITTI Cars Hard
Crafting GBD-Net for Object Detection
The effectiveness of GBD-Net is shown through experiments on three object detection datasets, ImageNet, Pascal VOC2007 and Microsoft COCO.
T-CNN: Tubelets with Convolutional Neural Networks for Object Detection from Videos
Temporal and contextual information of videos are not fully investigated and utilized.
Window-Object Relationship Guided Representation Learning for Generic Object Detections
We propose a representation learning pipeline to use the relationship as supervision for improving the learned representation in object detection.
Learning Deep Representation With Large-Scale Attributes
Experimental results show that the attributes are helpful in learning better features and improving the object detection accuracy by 2. 6% in mAP on the ILSVRC 2014 object detection dataset and 2. 4% in mAP on PASCAL VOC 2007 object detection dataset.
DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection
In this paper, we propose deformable deep convolutional neural networks for generic object detection.
DeepID-Net: multi-stage and deformable deep convolutional neural networks for object detection
In the proposed new deep architecture, a new deformation constrained pooling (def-pooling) layer models the deformation of object parts with geometric constraint and penalty.
Modeling Mutual Visibility Relationship in Pedestrian Detection
In this paper, we propose a mutual visibility deep model that jointly estimates the visibility statuses of overlapping pedestrians.
