914 research outputs found
Learning Intra and Inter-Camera Invariance for Isolated Camera Supervised Person Re-identification
Supervised person re-identification assumes that a person has images captured
under multiple cameras. However when cameras are placed in distance, a person
rarely appears in more than one camera. This paper thus studies person re-ID
under such isolated camera supervised (ISCS) setting. Instead of trying to
generate fake cross-camera features like previous methods, we explore a novel
perspective by making efficient use of the variation in training data. Under
ISCS setting, a person only has limited images from a single camera, so the
camera bias becomes a critical issue confounding ID discrimination.
Cross-camera images are prone to being recognized as different IDs simply by
camera style. To eliminate the confounding effect of camera bias, we propose to
learn both intra- and inter-camera invariance under a unified framework. First,
we construct style-consistent environments via clustering, and perform
prototypical contrastive learning within each environment. Meanwhile, strongly
augmented images are contrasted with original prototypes to enforce
intra-camera augmentation invariance. For inter-camera invariance, we further
design a much improved variant of multi-camera negative loss that optimizes the
distance of multi-level negatives. The resulting model learns to be invariant
to both subtle and severe style variation within and cross-camera. On multiple
benchmarks, we conduct extensive experiments and validate the effectiveness and
superiority of the proposed method. Code will be available at
https://github.com/Terminator8758/IICI.Comment: ACM MultiMedia 202
Transformer Based Multi-Grained Features for Unsupervised Person Re-Identification
Multi-grained features extracted from convolutional neural networks (CNNs)
have demonstrated their strong discrimination ability in supervised person
re-identification (Re-ID) tasks. Inspired by them, this work investigates the
way of extracting multi-grained features from a pure transformer network to
address the unsupervised Re-ID problem that is label-free but much more
challenging. To this end, we build a dual-branch network architecture based
upon a modified Vision Transformer (ViT). The local tokens output in each
branch are reshaped and then uniformly partitioned into multiple stripes to
generate part-level features, while the global tokens of two branches are
averaged to produce a global feature. Further, based upon offline-online
associated camera-aware proxies (O2CAP) that is a top-performing unsupervised
Re-ID method, we define offline and online contrastive learning losses with
respect to both global and part-level features to conduct unsupervised
learning. Extensive experiments on three person Re-ID datasets show that the
proposed method outperforms state-of-the-art unsupervised methods by a
considerable margin, greatly mitigating the gap to supervised counterparts.
Code will be available soon at https://github.com/RikoLi/WACV23-workshop-TMGF.Comment: Accepted by WACVW 2023, 3rd Workshop on Real-World Surveillance:
Applications and Challenge
Investigating Spatial Interdependence in E-Bike Choice Using Spatially Autoregressive Model
Increased attention has been given to promoting e-bike usage in recent years. However, the research gap still exists in understanding the effects of spatial interdependence on e-bike choice. This study investigated how spatial interdependence affected the e-bike choice. The Moran’s I statistic test showed that spatial interdependence exists in e-bike choice at aggregated level. Bayesian spatial autoregressive logistic analyses were then used to investigate the spatial interdependence at individual level. Separate models were developed for commuting and non-commuting trips. The factors affecting e-bike choice are different between commuting and non-commuting trips. Spatial interdependence exists at both origin and destination sides of commuting and non-commuting trips. Travellers are more likely to choose e-bikes if their neighbours at the trip origin and destination also travel by e-bikes. And the magnitude of this spatial interdependence is different across various traffic analysis zones. The results suggest that, without considering spatial interdependence, the traditional methods may have biased estimation results and make systematic forecasting errors.</p
Emergent Correspondence from Image Diffusion
Finding correspondences between images is a fundamental problem in computer
vision. In this paper, we show that correspondence emerges in image diffusion
models without any explicit supervision. We propose a simple strategy to
extract this implicit knowledge out of diffusion networks as image features,
namely DIffusion FeaTures (DIFT), and use them to establish correspondences
between real images. Without any additional fine-tuning or supervision on the
task-specific data or annotations, DIFT is able to outperform both
weakly-supervised methods and competitive off-the-shelf features in identifying
semantic, geometric, and temporal correspondences. Particularly for semantic
correspondence, DIFT from Stable Diffusion is able to outperform DINO and
OpenCLIP by 19 and 14 accuracy points respectively on the challenging SPair-71k
benchmark. It even outperforms the state-of-the-art supervised methods on 9 out
of 18 categories while remaining on par for the overall performance. Project
page: https://diffusionfeatures.github.ioComment: Project page: https://diffusionfeatures.github.i
Offline-Online Associated Camera-Aware Proxies for Unsupervised Person Re-identification
Recently, unsupervised person re-identification (Re-ID) has received
increasing research attention due to its potential for label-free applications.
A promising way to address unsupervised Re-ID is clustering-based, which
generates pseudo labels by clustering and uses the pseudo labels to train a
Re-ID model iteratively. However, most clustering-based methods take each
cluster as a pseudo identity class, neglecting the intra-cluster variance
mainly caused by the change of cameras. To address this issue, we propose to
split each single cluster into multiple proxies according to camera views. The
camera-aware proxies explicitly capture local structures within clusters, by
which the intra-ID variance and inter-ID similarity can be better tackled.
Assisted with the camera-aware proxies, we design two proxy-level contrastive
learning losses that are, respectively, based on offline and online association
results. The offline association directly associates proxies according to the
clustering and splitting results, while the online strategy dynamically
associates proxies in terms of up-to-date features to reduce the noise caused
by the delayed update of pseudo labels. The combination of two losses enables
us to train a desirable Re-ID model. Extensive experiments on three person
Re-ID datasets and one vehicle Re-ID dataset show that our proposed approach
demonstrates competitive performance with state-of-the-art methods. Code will
be available at: https://github.com/Terminator8758/O2CAP.Comment: Accepted to TI
Camera-aware Proxies for Unsupervised Person Re-Identification
This paper tackles the purely unsupervised person re-identification (Re-ID)
problem that requires no annotations. Some previous methods adopt clustering
techniques to generate pseudo labels and use the produced labels to train Re-ID
models progressively. These methods are relatively simple but effective.
However, most clustering-based methods take each cluster as a pseudo identity
class, neglecting the large intra-ID variance caused mainly by the change of
camera views. To address this issue, we propose to split each single cluster
into multiple proxies and each proxy represents the instances coming from the
same camera. These camera-aware proxies enable us to deal with large intra-ID
variance and generate more reliable pseudo labels for learning. Based on the
camera-aware proxies, we design both intra- and inter-camera contrastive
learning components for our Re-ID model to effectively learn the ID
discrimination ability within and across cameras. Meanwhile, a proxy-balanced
sampling strategy is also designed, which facilitates our learning further.
Extensive experiments on three large-scale Re-ID datasets show that our
proposed approach outperforms most unsupervised methods by a significant
margin. Especially, on the challenging MSMT17 dataset, we gain Rank-1
and mAP improvements when compared to the second place. Code is
available at: \texttt{https://github.com/Terminator8758/CAP-master}.Comment: Accepted to AAAI 2021. Code is available at:
https://github.com/Terminator8758/CAP-maste
Systematic review: Factors influencing creativity in the design discipline and assessment criteria
Using psychological instrument to measure creativity is getting popular in design research. However, unlike quantifying general creativity using divergent thinking, the complexity and interdisciplinarity of the design discipline have made it difficult to explore research on design creativity. Therefore, to better quantify and measure design creativity, 31 relevant studies were retrieved by Google Scholar and the University of London Common Research in this article. This study summarizes the factors that influence design creativity in different design disciplines, the rules for setting the internal dimensions, and the valid instruments for measuring design creativity. The factors affecting design creativity can be divided into internal factors (aesthetic, spatial ability, and ambiguity tolerance) and external factors (environment and visual stimulation). Among these factors, different instruments and evaluation criteria considerably impact the result, while the measurement of design creativity is still not mature enough. A single scale evaluation or creative task evaluation cannot comprehensively evaluate the design creativity, which consists of aesthetic, functional, and technical aspects. In addition, the reference value of ordinary creativity remains to be further discussed in design. Under some professional design fields, the effect of widely recognized factors closely related to creativity, such as divergent thinking, imagination, and personality, is insignificant
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