Search Results for author:
Found 11 papers, 3 papers with code
OVFoodSeg: Elevating Open-Vocabulary Food Image Segmentation via Image-Informed Textual Representation
The pre-training phase equips FoodLearner with the capability to align visual information with corresponding textual representations that are specifically related to food, while the second phase adapts both the FoodLearner and the Image-Informed Text Encoder for the segmentation task.
A Map-matching Algorithm with Extraction of Multi-group Information for Low-frequency Data
In addition, there are a large amount of other data, e. g., other vehicles' state and past prediction results, but it is hard to extract useful information for matching maps and inferring paths.
Collaborative Intelligent Reflecting Surface Networks with Multi-Agent Reinforcement Learning
Intelligent reflecting surface (IRS) is envisioned to be widely applied in future wireless networks.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Class Re-Activation Maps for Weakly-Supervised Semantic Segmentation
Specifically, due to the sum-over-class pooling nature of BCE, each pixel in CAM may be responsive to multiple classes co-occurring in the same receptive field.
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
Attention-based Feature Aggregation
To implement this module, we define two variants of attention: self-attention on the summed-up feature map, and cross-attention between two feature maps before summed up.
A Large-Scale Benchmark for Food Image Segmentation
Existing food image segmentation models are underperforming due to two reasons: (1) there is a lack of high quality food image datasets with fine-grained ingredient labels and pixel-wise location masks -- the existing datasets either carry coarse ingredient labels or are small in size; and (2) the complex appearance of food makes it difficult to localize and recognize ingredients in food images, e. g., the ingredients may overlap one another in the same image, and the identical ingredient may appear distinctly in different food images.
Ranked #3 on
Semantic Segmentation
on FoodSeg103
(using extra training data)
Meta-RCNN: Meta Learning for Few-Shot Object Detection
Specifically, Meta-RCNN learns an object detector in an episodic learning paradigm on the (meta) training data.
Recent Advances in Deep Learning for Object Detection
Object detection is a fundamental visual recognition problem in computer vision and has been widely studied in the past decades.
Single-Shot Bidirectional Pyramid Networks for High-Quality Object Detection
Recent years have witnessed many exciting achievements for object detection using deep learning techniques.
Feature Agglomeration Networks for Single Stage Face Detection
In this paper, we propose a novel simple yet effective framework of "Feature Agglomeration Networks" (FANet) to build a new single stage face detector, which not only achieves state-of-the-art performance but also runs efficiently.
LOGO-Net: Large-scale Deep Logo Detection and Brand Recognition with Deep Region-based Convolutional Networks
In this paper, we introduce "LOGO-Net", a large-scale logo image database for logo detection and brand recognition from real-world product images.
