Recent advancements in Multi-Modal Large Language models (MLLMs) have shown promising potential in enhancing the explainability as a driving agent by producing control predictions along with natural language explanations.
Mask labels are labor-intensive, which limits the number of categories in segmentation datasets.
Synthetic training data has gained prominence in numerous learning tasks and scenarios, offering advantages such as dataset augmentation, generalization evaluation, and privacy preservation.
Hence, humour generation and understanding can serve as a new task for evaluating the ability of deep-learning methods to process abstract and subjective information.
This paper presents a new mechanism to facilitate the training of mask transformers for efficient panoptic segmentation, democratizing its deployment.
LUMix is simple as it can be implemented in just a few lines of code and can be universally applied to any deep networks \eg CNNs and Vision Transformers, with minimal computational cost.
Recent text-to-image generation models have shown promising results in generating high-fidelity photo-realistic images.
Large-scale pre-training has been proven to be crucial for various computer vision tasks.
We encode all panoptic entities in a video, including both foreground instances and background semantics, with a unified representation called panoptic slots.
The confidence of the label will be larger if the corresponding input image is weighted higher by the attention map.
As a typical example, the Vision Transformer (ViT) directly applies a pure transformer architecture on image classification, by simply splitting images into tokens with a fixed length, and employing transformers to learn relations between these tokens.
To model the representations of the two levels, we first encode the information from the whole into part vectors through an attention mechanism, then decode the global information within the part vectors back into the whole representation.
Ranked #308 on Image Classification on ImageNet
Aggregating features from different depths of a network is widely adopted to improve the network capability.
By assigning each relationship a single label, current approaches formulate the relationship detection as a classification problem.
Multi-sensor perception is crucial to ensure the reliability and accuracy in autonomous driving system, while multi-object tracking (MOT) improves that by tracing sequential movement of dynamic objects.
Ranked #10 on Multiple Object Tracking on KITTI Tracking test
144 code implementations • 17 Jun 2019 • Kai Chen, Jiaqi Wang, Jiangmiao Pang, Yuhang Cao, Yu Xiong, Xiaoxiao Li, Shuyang Sun, Wansen Feng, Ziwei Liu, Jiarui Xu, Zheng Zhang, Dazhi Cheng, Chenchen Zhu, Tianheng Cheng, Qijie Zhao, Buyu Li, Xin Lu, Rui Zhu, Yue Wu, Jifeng Dai, Jingdong Wang, Jianping Shi, Wanli Ouyang, Chen Change Loy, Dahua Lin
In this paper, we introduce the various features of this toolbox.
In exploring a more effective approach, we find that the key to a successful instance segmentation cascade is to fully leverage the reciprocal relationship between detection and segmentation.
Ranked #32 on Object Detection on COCO-O
The basic principles in designing convolutional neural network (CNN) structures for predicting objects on different levels, e. g., image-level, region-level, and pixel-level are diverging.
In this study, we introduce a novel compact motion representation for video action recognition, named Optical Flow guided Feature (OFF), which enables the network to distill temporal information through a fast and robust approach.
Ranked #35 on Action Recognition on UCF101
Person re-identification (ReID) is an important task in video surveillance and has various applications.