A survey on deep learning-based fine-grained object classification and semantic segmentation
The deep learning technology has shown impressive performance in various vision tasks
such as image classification, object detection and semantic segmentation. In particular …
such as image classification, object detection and semantic segmentation. In particular …
Fine-grained image analysis with deep learning: A survey
Fine-grained image analysis (FGIA) is a longstanding and fundamental problem in computer
vision and pattern recognition, and underpins a diverse set of real-world applications. The …
vision and pattern recognition, and underpins a diverse set of real-world applications. The …
Concept bottleneck models
We seek to learn models that we can interact with using high-level concepts: if the model did
not think there was a bone spur in the x-ray, would it still predict severe arthritis? State-of-the …
not think there was a bone spur in the x-ray, would it still predict severe arthritis? State-of-the …
Hierarchical deep click feature prediction for fine-grained image recognition
The click feature of an image, defined as the user click frequency vector of the image on a
predefined word vocabulary, is known to effectively reduce the semantic gap for fine-grained …
predefined word vocabulary, is known to effectively reduce the semantic gap for fine-grained …
This looks like that: deep learning for interpretable image recognition
When we are faced with challenging image classification tasks, we often explain our
reasoning by dissecting the image, and pointing out prototypical aspects of one class or …
reasoning by dissecting the image, and pointing out prototypical aspects of one class or …
Fine-grained visual classification via progressive multi-granularity training of jigsaw patches
Fine-grained visual classification (FGVC) is much more challenging than traditional
classification tasks due to the inherently subtle intra-class object variations. Recent works …
classification tasks due to the inherently subtle intra-class object variations. Recent works …
Destruction and construction learning for fine-grained image recognition
Delicate feature representation about object parts plays a critical role in fine-grained
recognition. For example, experts can even distinguish fine-grained objects relying only on …
recognition. For example, experts can even distinguish fine-grained objects relying only on …
Look closer to see better: Recurrent attention convolutional neural network for fine-grained image recognition
Recognizing fine-grained categories (eg, bird species) is difficult due to the challenges of
discriminative region localization and fine-grained feature learning. Existing approaches …
discriminative region localization and fine-grained feature learning. Existing approaches …
Learning multi-attention convolutional neural network for fine-grained image recognition
Recognizing fine-grained categories (eg, bird species) highly relies on discriminative part
localization and part-based fine-grained feature learning. Existing approaches …
localization and part-based fine-grained feature learning. Existing approaches …
Feature fusion vision transformer for fine-grained visual categorization
The core for tackling the fine-grained visual categorization (FGVC) is to learn subtle yet
discriminative features. Most previous works achieve this by explicitly selecting the …
discriminative features. Most previous works achieve this by explicitly selecting the …