13 citations · 40 across the 21 of their papers we have counts for
8 papers · 1 filter
Adaptive Poincaré Point to Set Distance for Few-Shot Classification
Rongkai Ma, Pengfei Fang, Tom Drummond +1
Learning and generalizing from limited examples, i,e, few-shot learning, is of core importance to many real-world vision applications. A principal way of achieving few-shot learnin…
Learning Instance and Task-Aware Dynamic Kernels for Few Shot Learning
Rongkai Ma, Pengfei Fang, Gil Avraham +4
Learning and generalizing to novel concepts with few samples (Few-Shot Learning) is still an essential challenge to real-world applications. A principle way of achieving few-shot l…
TSGB: Target-Selective Gradient Backprop for Probing CNN Visual Saliency
Lin Cheng, Pengfei Fang, Yanjie Liang +3
The explanation for deep neural networks has drawn extensive attention in the deep learning community over the past few years. In this work, we study the visual saliency, a.k.a. vi…
Feature Correlation Aggregation: on the Path to Better Graph Neural Networks
Jieming Zhou, Tong Zhang, Pengfei Fang +2
Prior to the introduction of Graph Neural Networks (GNNs), modeling and analyzing irregular data, particularly graphs, was thought to be the Achilles' heel of deep learning. The co…
Blind Image Decomposition
Junlin Han, Weihao Li, Pengfei Fang +5
We propose and study a novel task named Blind Image Decomposition (BID), which requires separating a superimposed image into constituent underlying images in a blind setting, that…
Uni-Encoder: A Fast and Accurate Response Selection Paradigm for Generation-Based Dialogue Systems
Chiyu Song, Hongliang He, Haofei Yu +3
Sample-and-rank is a key decoding strategy for modern generation-based dialogue systems. It helps achieve diverse and high-quality responses by selecting an answer from a small poo…
Reinforced Attention for Few-Shot Learning and Beyond
Jie Hong, Pengfei Fang, Weihao Li +4
Few-shot learning aims to correctly recognize query samples from unseen classes given a limited number of support samples, often by relying on global embeddings of images. In this…
Semantic-aware Knowledge Distillation for Few-Shot Class-Incremental Learning
Ali Cheraghian, Shafin Rahman, Pengfei Fang +3
Few-shot class incremental learning (FSCIL) portrays the problem of learning new concepts gradually, where only a few examples per concept are available to the learner. Due to the…