40 research outputs found
YYDS: Visible-Infrared Person Re-Identification with Coarse Descriptions
Visible-infrared person re-identification (VI-ReID) is challenging due to
considerable cross-modality discrepancies. Existing works mainly focus on
learning modality-invariant features while suppressing modality-specific ones.
However, retrieving visible images only depends on infrared samples is an
extreme problem because of the absence of color information. To this end, we
present the Refer-VI-ReID settings, which aims to match target visible images
from both infrared images and coarse language descriptions (e.g., "a man with
red top and black pants") to complement the missing color information. To
address this task, we design a Y-Y-shape decomposition structure, dubbed YYDS,
to decompose and aggregate texture and color features of targets. Specifically,
the text-IoU regularization strategy is firstly presented to facilitate the
decomposition training, and a joint relation module is then proposed to infer
the aggregation. Furthermore, the cross-modal version of k-reciprocal
re-ranking algorithm is investigated, named CMKR, in which three neighbor
search strategies and one local query expansion method are explored to
alleviate the modality bias problem of the near neighbors. We conduct
experiments on SYSU-MM01, RegDB and LLCM datasets with our manually annotated
descriptions. Both YYDS and CMKR achieve remarkable improvements over SOTA
methods on all three datasets. Codes are available at
https://github.com/dyhBUPT/YYDS.Comment: 14 pages, 6 figure
iKUN: Speak to Trackers without Retraining
Referring multi-object tracking (RMOT) aims to track multiple objects based
on input textual descriptions. Previous works realize it by simply integrating
an extra textual module into the multi-object tracker. However, they typically
need to retrain the entire framework and have difficulties in optimization. In
this work, we propose an insertable Knowledge Unification Network, termed iKUN,
to enable communication with off-the-shelf trackers in a plug-and-play manner.
Concretely, a knowledge unification module (KUM) is designed to adaptively
extract visual features based on textual guidance. Meanwhile, to improve the
localization accuracy, we present a neural version of Kalman filter (NKF) to
dynamically adjust process noise and observation noise based on the current
motion status. Moreover, to address the problem of open-set long-tail
distribution of textual descriptions, a test-time similarity calibration method
is proposed to refine the confidence score with pseudo frequency. Extensive
experiments on Refer-KITTI dataset verify the effectiveness of our framework.
Finally, to speed up the development of RMOT, we also contribute a more
challenging dataset, Refer-Dance, by extending public DanceTrack dataset with
motion and dressing descriptions. The codes and dataset are available at
https://github.com/dyhBUPT/iKUN.Comment: CVPR 2024 camera-read
Video-based Visible-Infrared Person Re-Identification with Auxiliary Samples
Visible-infrared person re-identification (VI-ReID) aims to match persons
captured by visible and infrared cameras, allowing person retrieval and
tracking in 24-hour surveillance systems. Previous methods focus on learning
from cross-modality person images in different cameras. However, temporal
information and single-camera samples tend to be neglected. To crack this nut,
in this paper, we first contribute a large-scale VI-ReID dataset named
BUPTCampus. Different from most existing VI-ReID datasets, it 1) collects
tracklets instead of images to introduce rich temporal information, 2) contains
pixel-aligned cross-modality sample pairs for better modality-invariant
learning, 3) provides one auxiliary set to help enhance the optimization, in
which each identity only appears in a single camera. Based on our constructed
dataset, we present a two-stream framework as baseline and apply Generative
Adversarial Network (GAN) to narrow the gap between the two modalities. To
exploit the advantages introduced by the auxiliary set, we propose a curriculum
learning based strategy to jointly learn from both primary and auxiliary sets.
Moreover, we design a novel temporal k-reciprocal re-ranking method to refine
the ranking list with fine-grained temporal correlation cues. Experimental
results demonstrate the effectiveness of the proposed methods. We also
reproduce 9 state-of-the-art image-based and video-based VI-ReID methods on
BUPTCampus and our methods show substantial superiority to them. The codes and
dataset are available at: https://github.com/dyhBUPT/BUPTCampus.Comment: Accepted by Transactions on Information Forensics & Security 202
Ad-UDDI: An Active and Distributed Service Registry
Abstract. In SOA (Service Oriented Architecture), web service providers use service registries to publish services and requestors use registries to find them. The major current service registry specifications, UDDI (Universal Description, Discovery and Integration), has the following drawbacks. First, it replicates all public service publications in all UBR (Universal Business Registry) nodes, which is not scalable and efficient, and second, it collects service information in a passive manner, which means it waits for service publication, updating or discovery request passively and thus cannot guarantee the real-time validity of the services information. In this paper, we propose an active and distributed UDDI architecture called Ad-UDDI, which extends and organizes the private or semi-private UDDIs based on industry classifications. Further, Ad-UDDI adopts an active monitoring mechanism, so that service information can be updated automatically and the service requestors may find the latest service information conveniently. We evaluate Ad-UDDI by comprehensive simulations and experimental results show that it outperforms existing approaches significantly.
Pushing the Limits of Machine Design: Automated CPU Design with AI
Design activity -- constructing an artifact description satisfying given
goals and constraints -- distinguishes humanity from other animals and
traditional machines, and endowing machines with design abilities at the human
level or beyond has been a long-term pursuit. Though machines have already
demonstrated their abilities in designing new materials, proteins, and computer
programs with advanced artificial intelligence (AI) techniques, the search
space for designing such objects is relatively small, and thus, "Can machines
design like humans?" remains an open question. To explore the boundary of
machine design, here we present a new AI approach to automatically design a
central processing unit (CPU), the brain of a computer, and one of the world's
most intricate devices humanity have ever designed. This approach generates the
circuit logic, which is represented by a graph structure called Binary
Speculation Diagram (BSD), of the CPU design from only external input-output
observations instead of formal program code. During the generation of BSD,
Monte Carlo-based expansion and the distance of Boolean functions are used to
guarantee accuracy and efficiency, respectively. By efficiently exploring a
search space of unprecedented size 10^{10^{540}}, which is the largest one of
all machine-designed objects to our best knowledge, and thus pushing the limits
of machine design, our approach generates an industrial-scale RISC-V CPU within
only 5 hours. The taped-out CPU successfully runs the Linux operating system
and performs comparably against the human-designed Intel 80486SX CPU. In
addition to learning the world's first CPU only from input-output observations,
which may reform the semiconductor industry by significantly reducing the
design cycle, our approach even autonomously discovers human knowledge of the
von Neumann architecture.Comment: 28 page
Surface-initiated Cu(0) mediated controlled radical polymerization (SI-CuCRP) using a copper plate
Surface engineering with polymer brushes has become one of the most versatile techniques to tailor surface properties of substrates for a broad variety of (bio-) technological applications. We report on a new facile approach to prepare defined and dense polymer brushes on planar substrates by surface-initiated Cu(0) mediated controlled radical polymerization (SI-CuCRP) of numerous vinyl monomers using a copper plate at room temperature. The fabrication of a variety of homo-, block, gradient and patterned polymer brushes as well as polymer brush arrays is demonstrated. The SI-CuCRP was found to be strictly surface-confined, of highly living character, proceeds remarkably fast and results in polymer brushes of very high grafting densities. The brush layer thickness can be modulated by the polymerization time or by the distance of the copper plate to the modified substrate. As the copper plate can be reused multiple times, no additional copper salts are added and only minimal amount of chemicals is needed, the simple and low-cost experimental conditions allows researchers from various fields to prepare tailored polymer brush surfaces for their needs
PAMI-AD: An Activity Detector Exploiting Part-attention and Motion Information in Surveillance Videos
Activity detection in surveillance videos is a challenging task caused by
small objects, complex activity categories, its untrimmed nature, etc. Existing
methods are generally limited in performance due to inaccurate proposals, poor
classifiers or inadequate post-processing method. In this work, we propose a
comprehensive and effective activity detection system in untrimmed surveillance
videos for person-centered and vehicle-centered activities. It consists of four
modules, i.e., object localizer, proposal filter, activity classifier and
activity refiner. For person-centered activities, a novel part-attention
mechanism is proposed to explore detailed features in different body parts. As
for vehicle-centered activities, we propose a localization masking method to
jointly encode motion and foreground attention features. We conduct experiments
on the large-scale activity detection datasets VIRAT, and achieve the best
results for both groups of activities. Furthermore, our team won the 1st place
in the TRECVID 2021 ActEV challenge.Comment: ICME 2022 Worksho