Few-shot video object segmentation (FSVOS) aims to segment dynamic objec...
Weakly supervised semantic segmentation (WSSS) models relying on class
a...
Semi-supervised semantic segmentation aims to learn from a small amount ...
Zero-shot video object segmentation (ZS-VOS) aims to segment foreground
...
Due to its significant capability of modeling long-range dependencies, v...
Few-shot semantic segmentation is the task of learning to locate each pi...
Optical flow is an easily conceived and precious cue for advancing
unsup...
Weakly supervised semantic segmentation with only image-level labels aim...
Non-linear activation functions, e.g., Sigmoid, ReLU, and Tanh, have ach...
Learning from the web can ease the extreme dependence of deep learning o...
Semantic segmentation aims to classify every pixel of an input image.
Co...
Due to the memorization effect in Deep Neural Networks (DNNs), training ...
One-shot semantic image segmentation aims to segment the object regions ...
Labeling objects at a subordinate level typically requires expert knowle...
WebFG 2020 is an international challenge hosted by Nanjing University of...
We propose a new method for learning with multi-field categorical data.
...
Constructing fine-grained image datasets typically requires domain-speci...
Due to the existence of label noise in web images and the high memorizat...
Despite significant progress of applying deep learning methods to the fi...
In this paper, we present a novel Motion-Attentive Transition Network
(M...
Recent successes in visual recognition can be primarily attributed to fe...
Due to the high cost of manual annotation, learning directly from the we...
Robust road detection is a key challenge in safe autonomous driving.
Rec...
Robust road segmentation is a key challenge in self-driving research. Th...
The availability of labeled image datasets has been shown critical for
h...
Studies show that refining real-world categories into semantic subcatego...
Labelled image datasets have played a critical role in high-level image
...