1,188 research outputs found
Transitivity Recovering Decompositions: Interpretable and Robust Fine-Grained Relationships
Recent advances in fine-grained representation learning leverage
local-to-global (emergent) relationships for achieving state-of-the-art
results. The relational representations relied upon by such methods, however,
are abstract. We aim to deconstruct this abstraction by expressing them as
interpretable graphs over image views. We begin by theoretically showing that
abstract relational representations are nothing but a way of recovering
transitive relationships among local views. Based on this, we design
Transitivity Recovering Decompositions (TRD), a graph-space search algorithm
that identifies interpretable equivalents of abstract emergent relationships at
both instance and class levels, and with no post-hoc computations. We
additionally show that TRD is provably robust to noisy views, with empirical
evidence also supporting this finding. The latter allows TRD to perform at par
or even better than the state-of-the-art, while being fully interpretable.
Implementation is available at https://github.com/abhrac/trd.Comment: Neural Information Processing Systems (NeurIPS) 202
Deep Adaptive Feature Embedding with Local Sample Distributions for Person Re-identification
Person re-identification (re-id) aims to match pedestrians observed by
disjoint camera views. It attracts increasing attention in computer vision due
to its importance to surveillance system. To combat the major challenge of
cross-view visual variations, deep embedding approaches are proposed by
learning a compact feature space from images such that the Euclidean distances
correspond to their cross-view similarity metric. However, the global Euclidean
distance cannot faithfully characterize the ideal similarity in a complex
visual feature space because features of pedestrian images exhibit unknown
distributions due to large variations in poses, illumination and occlusion.
Moreover, intra-personal training samples within a local range are robust to
guide deep embedding against uncontrolled variations, which however, cannot be
captured by a global Euclidean distance. In this paper, we study the problem of
person re-id by proposing a novel sampling to mine suitable \textit{positives}
(i.e. intra-class) within a local range to improve the deep embedding in the
context of large intra-class variations. Our method is capable of learning a
deep similarity metric adaptive to local sample structure by minimizing each
sample's local distances while propagating through the relationship between
samples to attain the whole intra-class minimization. To this end, a novel
objective function is proposed to jointly optimize similarity metric learning,
local positive mining and robust deep embedding. This yields local
discriminations by selecting local-ranged positive samples, and the learned
features are robust to dramatic intra-class variations. Experiments on
benchmarks show state-of-the-art results achieved by our method.Comment: Published on Pattern Recognitio
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