207 research outputs found
A2-RL: Aesthetics Aware Reinforcement Learning for Image Cropping
Image cropping aims at improving the aesthetic quality of images by adjusting
their composition. Most weakly supervised cropping methods (without bounding
box supervision) rely on the sliding window mechanism. The sliding window
mechanism requires fixed aspect ratios and limits the cropping region with
arbitrary size. Moreover, the sliding window method usually produces tens of
thousands of windows on the input image which is very time-consuming. Motivated
by these challenges, we firstly formulate the aesthetic image cropping as a
sequential decision-making process and propose a weakly supervised Aesthetics
Aware Reinforcement Learning (A2-RL) framework to address this problem.
Particularly, the proposed method develops an aesthetics aware reward function
which especially benefits image cropping. Similar to human's decision making,
we use a comprehensive state representation including both the current
observation and the historical experience. We train the agent using the
actor-critic architecture in an end-to-end manner. The agent is evaluated on
several popular unseen cropping datasets. Experiment results show that our
method achieves the state-of-the-art performance with much fewer candidate
windows and much less time compared with previous weakly supervised methods.Comment: Accepted by CVPR 201
Learning Deep Context-aware Features over Body and Latent Parts for Person Re-identification
Person Re-identification (ReID) is to identify the same person across
different cameras. It is a challenging task due to the large variations in
person pose, occlusion, background clutter, etc How to extract powerful
features is a fundamental problem in ReID and is still an open problem today.
In this paper, we design a Multi-Scale Context-Aware Network (MSCAN) to learn
powerful features over full body and body parts, which can well capture the
local context knowledge by stacking multi-scale convolutions in each layer.
Moreover, instead of using predefined rigid parts, we propose to learn and
localize deformable pedestrian parts using Spatial Transformer Networks (STN)
with novel spatial constraints. The learned body parts can release some
difficulties, eg pose variations and background clutters, in part-based
representation. Finally, we integrate the representation learning processes of
full body and body parts into a unified framework for person ReID through
multi-class person identification tasks. Extensive evaluations on current
challenging large-scale person ReID datasets, including the image-based
Market1501, CUHK03 and sequence-based MARS datasets, show that the proposed
method achieves the state-of-the-art results.Comment: Accepted by CVPR 201
How Does Adaptive Optimization Impact Local Neural Network Geometry?
Adaptive optimization methods are well known to achieve superior convergence
relative to vanilla gradient methods. The traditional viewpoint in
optimization, particularly in convex optimization, explains this improved
performance by arguing that, unlike vanilla gradient schemes, adaptive
algorithms mimic the behavior of a second-order method by adapting to the
global geometry of the loss function. We argue that in the context of neural
network optimization, this traditional viewpoint is insufficient. Instead, we
advocate for a local trajectory analysis. For iterate trajectories produced by
running a generic optimization algorithm OPT, we introduce
, a statistic that is analogous to the condition
number of the loss Hessian evaluated at the iterates. Through extensive
experiments, we show that adaptive methods such as Adam bias the trajectories
towards regions where is small, where one might
expect faster convergence. By contrast, vanilla gradient methods like SGD bias
the trajectories towards regions where is
comparatively large. We complement these empirical observations with a
theoretical result that provably demonstrates this phenomenon in the simplified
setting of a two-layer linear network. We view our findings as evidence for the
need of a new explanation of the success of adaptive methods, one that is
different than the conventional wisdom
Mixed Supervised Object Detection with Robust Objectness Transfer
In this paper, we consider the problem of leveraging existing fully labeled
categories to improve the weakly supervised detection (WSD) of new object
categories, which we refer to as mixed supervised detection (MSD). Different
from previous MSD methods that directly transfer the pre-trained object
detectors from existing categories to new categories, we propose a more
reasonable and robust objectness transfer approach for MSD. In our framework,
we first learn domain-invariant objectness knowledge from the existing fully
labeled categories. The knowledge is modeled based on invariant features that
are robust to the distribution discrepancy between the existing categories and
new categories; therefore the resulting knowledge would generalize well to new
categories and could assist detection models to reject distractors (e.g.,
object parts) in weakly labeled images of new categories. Under the guidance of
learned objectness knowledge, we utilize multiple instance learning (MIL) to
model the concepts of both objects and distractors and to further improve the
ability of rejecting distractors in weakly labeled images. Our robust
objectness transfer approach outperforms the existing MSD methods, and achieves
state-of-the-art results on the challenging ILSVRC2013 detection dataset and
the PASCAL VOC datasets.Comment: IEEE Transactions on Pattern Analysis and Machine Intelligence
(2019). Together with Supplementary Materials. Note: The author list in
Google Scholar is INCORRECT. The right author list is 1) Yan Li, 2) Junge
Zhang, 3) Kaiqi Huang and 4) Jianguo Zhang. The official published version
can be found in https://ieeexplore.ieee.org/abstract/document/830462
Discriminative Learning of Latent Features for Zero-Shot Recognition
Zero-shot learning (ZSL) aims to recognize unseen image categories by
learning an embedding space between image and semantic representations. For
years, among existing works, it has been the center task to learn the proper
mapping matrices aligning the visual and semantic space, whilst the importance
to learn discriminative representations for ZSL is ignored. In this work, we
retrospect existing methods and demonstrate the necessity to learn
discriminative representations for both visual and semantic instances of ZSL.
We propose an end-to-end network that is capable of 1) automatically
discovering discriminative regions by a zoom network; and 2) learning
discriminative semantic representations in an augmented space introduced for
both user-defined and latent attributes. Our proposed method is tested
extensively on two challenging ZSL datasets, and the experiment results show
that the proposed method significantly outperforms state-of-the-art methods.Comment: CVPR 2018 (Oral
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