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Found 18 papers, 1 papers with code
DetGPT: Detect What You Need via Reasoning
Overall, our proposed paradigm and DetGPT demonstrate the potential for more sophisticated and intuitive interactions between humans and machines.
DetCLIPv2: Scalable Open-Vocabulary Object Detection Pre-training via Word-Region Alignment
This paper presents DetCLIPv2, an efficient and scalable training framework that incorporates large-scale image-text pairs to achieve open-vocabulary object detection (OVD).
CLIP$^2$: Contrastive Language-Image-Point Pretraining from Real-World Point Cloud Data
Contrastive Language-Image Pre-training, benefiting from large-scale unlabeled text-image pairs, has demonstrated great performance in open-world vision understanding tasks.
Towards Universal Vision-language Omni-supervised Segmentation
To improve the open-world segmentation ability, we leverage omni-supervised data (i. e., panoptic segmentation data, object detection data, and image-text pairs data) into training, thus enriching the open-world segmentation ability and achieving better segmentation accuracy.
CapDet: Unifying Dense Captioning and Open-World Detection Pretraining
Besides, our CapDet also achieves state-of-the-art performance on dense captioning tasks, e. g., 15. 44% mAP on VG V1. 2 and 13. 98% on the VG-COCO dataset.
Visual Exemplar Driven Task-Prompting for Unified Perception in Autonomous Driving
Multi-task learning has emerged as a powerful paradigm to solve a range of tasks simultaneously with good efficiency in both computation resources and inference time.
CLIP2: Contrastive Language-Image-Point Pretraining From Real-World Point Cloud Data
Contrastive Language-Image Pre-training, benefiting from large-scale unlabeled text-image pairs, has demonstrated great performance in open-world vision understanding tasks.
NLIP: Noise-robust Language-Image Pre-training
Large-scale cross-modal pre-training paradigms have recently shown ubiquitous success on a wide range of downstream tasks, e. g., zero-shot classification, retrieval and image captioning.
P$^3$OVD: Fine-grained Visual-Text Prompt-Driven Self-Training for Open-Vocabulary Object Detection
Inspired by the success of visual-language methods (VLMs) in zero-shot classification, recent works attempt to extend this line of work into object detection by leveraging the localization ability of pre-trained VLMs and generating pseudo labels for unseen classes in a self-training manner.
Generative Negative Text Replay for Continual Vision-Language Pretraining
In this work, we focus on learning a VLP model with sequential chunks of image-text pair data.
DetCLIP: Dictionary-Enriched Visual-Concept Paralleled Pre-training for Open-world Detection
We further design a concept dictionary~(with descriptions) from various online sources and detection datasets to provide prior knowledge for each concept.
Effective Adaptation in Multi-Task Co-Training for Unified Autonomous Driving
Aiming towards a holistic understanding of multiple downstream tasks simultaneously, there is a need for extracting features with better transferability.
Open-world Semantic Segmentation via Contrasting and Clustering Vision-Language Embedding
To bridge the gap between supervised semantic segmentation and real-world applications that acquires one model to recognize arbitrary new concepts, recent zero-shot segmentation attracts a lot of attention by exploring the relationships between unseen and seen object categories, yet requiring large amounts of densely-annotated data with diverse base classes.
Task-Customized Self-Supervised Pre-training with Scalable Dynamic Routing
On the other hand, for existing SSL methods, it is burdensome and infeasible to use different downstream-task-customized datasets in pre-training for different tasks.
ONCE-3DLanes: Building Monocular 3D Lane Detection
We present ONCE-3DLanes, a real-world autonomous driving dataset with lane layout annotation in 3D space.
Laneformer: Object-aware Row-Column Transformers for Lane Detection
We present Laneformer, a conceptually simple yet powerful transformer-based architecture tailored for lane detection that is a long-standing research topic for visual perception in autonomous driving.
CODA: A Real-World Road Corner Case Dataset for Object Detection in Autonomous Driving
One main reason that impedes the development of truly reliably self-driving systems is the lack of public datasets for evaluating the performance of object detectors on corner cases.
SODA10M: A Large-Scale 2D Self/Semi-Supervised Object Detection Dataset for Autonomous Driving
Experiments show that SODA10M can serve as a promising pre-training dataset for different self-supervised learning methods, which gives superior performance when fine-tuning with different downstream tasks (i. e., detection, semantic/instance segmentation) in autonomous driving domain.
