89 research outputs found
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
Semantically Tied Paired Cycle Consistency for Zero-Shot Sketch-based Image Retrieval
This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordZero-shot sketch-based image retrieval (SBIR) is an emerging task in computer vision, allowing to retrieve natural images relevant to sketch queries that might not been seen in the training phase. Existing works either require aligned sketch-image pairs or inefficient memory fusion layer for mapping the visual information to a semantic space. In this work, we propose a semantically aligned paired cycle-consistent generative (SEM-PCYC) model for zero-shot SBIR, where each branch maps the visual information to a common semantic space via an adversarial training. Each of these branches maintains a cycle consistency that only requires supervision at category levels, and avoids the need of highly-priced aligned sketch-image pairs. A classification criteria on the generators' outputs ensures the visual to semantic space mapping to be discriminating. Furthermore, we propose to combine textual and hierarchical side information via a feature selection auto-encoder that selects discriminating side information within a same end-to-end model. Our results demonstrate a significant boost in zero-shot SBIR performance over the state-of-the-art on the challenging Sketchy and TU-Berlin datasets.European Union Horizon 202
DISC: Deep Image Saliency Computing via Progressive Representation Learning
Salient object detection increasingly receives attention as an important
component or step in several pattern recognition and image processing tasks.
Although a variety of powerful saliency models have been intensively proposed,
they usually involve heavy feature (or model) engineering based on priors (or
assumptions) about the properties of objects and backgrounds. Inspired by the
effectiveness of recently developed feature learning, we provide a novel Deep
Image Saliency Computing (DISC) framework for fine-grained image saliency
computing. In particular, we model the image saliency from both the coarse- and
fine-level observations, and utilize the deep convolutional neural network
(CNN) to learn the saliency representation in a progressive manner.
Specifically, our saliency model is built upon two stacked CNNs. The first CNN
generates a coarse-level saliency map by taking the overall image as the input,
roughly identifying saliency regions in the global context. Furthermore, we
integrate superpixel-based local context information in the first CNN to refine
the coarse-level saliency map. Guided by the coarse saliency map, the second
CNN focuses on the local context to produce fine-grained and accurate saliency
map while preserving object details. For a testing image, the two CNNs
collaboratively conduct the saliency computing in one shot. Our DISC framework
is capable of uniformly highlighting the objects-of-interest from complex
background while preserving well object details. Extensive experiments on
several standard benchmarks suggest that DISC outperforms other
state-of-the-art methods and it also generalizes well across datasets without
additional training. The executable version of DISC is available online:
http://vision.sysu.edu.cn/projects/DISC.Comment: This manuscript is the accepted version for IEEE Transactions on
Neural Networks and Learning Systems (T-NNLS), 201
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