Your "Labrador" is My "Dog": Fine-Grained, or Not

11/18/2020
by   Dongliang Chang, et al.
4

Whether what you see in Figure 1 is a "labrador" or a "dog", is the question we ask in this paper. While fine-grained visual classification (FGVC) strives to arrive at the former, for the majority of us non-experts just "dog" would probably suffice. The real question is therefore – how can we tailor for different fine-grained definitions under divergent levels of expertise. For that, we re-envisage the traditional setting of FGVC, from single-label classification, to that of top-down traversal of a pre-defined coarse-to-fine label hierarchy – so that our answer becomes "dog"–>"gun dog"–>"retriever"–>"labrador". To approach this new problem, we first conduct a comprehensive human study where we confirm that most participants prefer multi-granularity labels, regardless whether they consider themselves experts. We then discover the key intuition that: coarse-level label prediction exacerbates fine-grained feature learning, yet fine-level feature betters the learning of coarse-level classifier. This discovery enables us to design a very simple albeit surprisingly effective solution to our new problem, where we (i) leverage level-specific classification heads to disentangle coarse-level features with fine-grained ones, and (ii) allow finer-grained features to participate in coarser-grained label predictions, which in turn helps with better disentanglement. Experiments show that our method achieves superior performance in the new FGVC setting, and performs better than state-of-the-art on traditional single-label FGVC problem as well. Thanks to its simplicity, our method can be easily implemented on top of any existing FGVC frameworks and is parameter-free.

READ FULL TEXT

page 1

page 2

page 3

page 4

page 5

page 6

page 7

page 9

01/10/2022

Label Relation Graphs Enhanced Hierarchical Residual Network for Hierarchical Multi-Granularity Classification

Hierarchical multi-granularity classification (HMC) assigns hierarchical...
09/12/2020

Exploring the Hierarchy in Relation Labels for Scene Graph Generation

By assigning each relationship a single label, current approaches formul...
09/22/2021

Coarse2Fine: Fine-grained Text Classification on Coarsely-grained Annotated Data

Existing text classification methods mainly focus on a fixed label set, ...
04/30/2018

Types for Information Flow Control: Labeling Granularity and Semantic Models

Language-based information flow control (IFC) tracks dependencies within...
03/12/2021

JITLine: A Simpler, Better, Faster, Finer-grained Just-In-Time Defect Prediction

A Just-In-Time (JIT) defect prediction model is a classifier to predict ...
12/19/2020

Towards Coarse and Fine-grained Multi-Graph Multi-Label Learning

Multi-graph multi-label learning (Mgml) is a supervised learning framewo...
08/22/2021

Efficient Algorithms for Learning from Coarse Labels

For many learning problems one may not have access to fine grained label...