research
∙
09/11/2023
HiLM-D: Towards High-Resolution Understanding in Multimodal Large Language Models for Autonomous Driving
Autonomous driving systems generally employ separate models for differen...
share
research
∙
08/31/2023
Any-Size-Diffusion: Toward Efficient Text-Driven Synthesis for Any-Size HD Images
Stable diffusion, a generative model used in text-to-image synthesis, fr...
research
∙
08/22/2023
GrowCLIP: Data-aware Automatic Model Growing for Large-scale Contrastive Language-Image Pre-training
Cross-modal pre-training has shown impressive performance on a wide rang...
research
∙
08/18/2023
DiffDis: Empowering Generative Diffusion Model with Cross-Modal Discrimination Capability
Recently, large-scale diffusion models, e.g., Stable diffusion and DallE...
research
∙
06/17/2023
MO-VLN: A Multi-Task Benchmark for Open-set Zero-Shot Vision-and-Language Navigation
Given a natural language, a general robot has to comprehend the instruct...
research
∙
05/31/2023
Boosting Text-to-Image Diffusion Models with Fine-Grained Semantic Rewards
Recent advances in text-to-image diffusion models have achieved remarkab...
research
∙
05/23/2023
DetGPT: Detect What You Need via Reasoning
In recent years, the field of computer vision has seen significant advan...
research
∙
04/10/2023
DetCLIPv2: Scalable Open-Vocabulary Object Detection Pre-training via Word-Region Alignment
This paper presents DetCLIPv2, an efficient and scalable training framew...
research
∙
03/22/2023
CLIP^2: Contrastive Language-Image-Point Pretraining from Real-World Point Cloud Data
Contrastive Language-Image Pre-training, benefiting from large-scale unl...
research
∙
03/12/2023
Towards Universal Vision-language Omni-supervised Segmentation
Existing open-world universal segmentation approaches usually leverage C...
research
∙
03/04/2023
CapDet: Unifying Dense Captioning and Open-World Detection Pretraining
Benefiting from large-scale vision-language pre-training on image-text p...
research
∙
03/03/2023
Visual Exemplar Driven Task-Prompting for Unified Perception in Autonomous Driving
Multi-task learning has emerged as a powerful paradigm to solve a range ...
research
∙
12/14/2022
NLIP: Noise-robust Language-Image Pre-training
Large-scale cross-modal pre-training paradigms have recently shown ubiqu...
research
∙
11/02/2022
P^3OVD: 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 c...
research
∙
10/31/2022
Generative Negative Text Replay for Continual Vision-Language Pretraining
Vision-language pre-training (VLP) has attracted increasing attention re...
research
∙
09/20/2022
DetCLIP: Dictionary-Enriched Visual-Concept Paralleled Pre-training for Open-world Detection
Open-world object detection, as a more general and challenging goal, aim...
research
∙
09/19/2022
Effective Adaptation in Multi-Task Co-Training for Unified Autonomous Driving
Aiming towards a holistic understanding of multiple downstream tasks sim...
research
∙
07/18/2022
Open-world Semantic Segmentation via Contrasting and Clustering Vision-Language Embedding
To bridge the gap between supervised semantic segmentation and real-worl...
research
∙
05/26/2022
Task-Customized Self-Supervised Pre-training with Scalable Dynamic Routing
Self-supervised learning (SSL), especially contrastive methods, has rais...
research
∙
04/30/2022
ONCE-3DLanes: Building Monocular 3D Lane Detection
We present ONCE-3DLanes, a real-world autonomous driving dataset with la...
research
∙
03/18/2022
Laneformer: Object-aware Row-Column Transformers for Lane Detection
We present Laneformer, a conceptually simple yet powerful transformer-ba...
research
∙
03/15/2022
CODA: A Real-World Road Corner Case Dataset for Object Detection in Autonomous Driving
Contemporary deep-learning object detection methods for autonomous drivi...
research
∙
06/21/2021
