60 research outputs found
UPAR: A Kantian-Inspired Prompting Framework for Enhancing Large Language Model Capabilities
Large Language Models (LLMs) have demonstrated impressive inferential
capabilities, with numerous research endeavors devoted to enhancing this
capacity through prompting. Despite these efforts, a unified epistemological
foundation is still conspicuously absent. Drawing inspiration from Kant's a
priori philosophy, we propose the UPAR prompting framework, designed to emulate
the structure of human cognition within LLMs. The UPAR framework is delineated
into four phases: "Understand", "Plan", "Act", and "Reflect", enabling the
extraction of structured information from complex contexts, prior planning of
solutions, execution according to plan, and self-reflection. This structure
significantly augments the explainability and accuracy of LLM inference,
producing a human-understandable and inspectable inferential trajectory.
Furthermore, our work offers an epistemological foundation for existing
prompting techniques, allowing for a possible systematic integration of these
methods. With GPT-4, our approach elevates the accuracy from COT baseline of
22.92% to 58.33% in a challenging subset of GSM8K, and from 67.91% to 75.40% in
the causal judgment task. Without using few-shot examples or external tools,
UPAR significantly outperforms existing prompting methods on SCIBENCH, a
challenging dataset containing collegiate-level mathematics, chemistry, and
physics scientific problems
Tracking Objects as Pixel-wise Distributions
Multi-object tracking (MOT) requires detecting and associating objects
through frames. Unlike tracking via detected bounding boxes or tracking objects
as points, we propose tracking objects as pixel-wise distributions. We
instantiate this idea on a transformer-based architecture, P3AFormer, with
pixel-wise propagation, prediction, and association. P3AFormer propagates
pixel-wise features guided by flow information to pass messages between frames.
Furthermore, P3AFormer adopts a meta-architecture to produce multi-scale object
feature maps. During inference, a pixel-wise association procedure is proposed
to recover object connections through frames based on the pixel-wise
prediction. P3AFormer yields 81.2\% in terms of MOTA on the MOT17 benchmark --
the first among all transformer networks to reach 80\% MOTA in literature.
P3AFormer also outperforms state-of-the-arts on the MOT20 and KITTI benchmarks.Comment: Accepted in ECCV22 as an oral presentation paper. The code&project
page is at
https://github.com/dvlab-research/ECCV22-P3AFormer-Tracking-Objects-as-Pixel-wise-Distribution
GaitFormer: Revisiting Intrinsic Periodicity for Gait Recognition
Gait recognition aims to distinguish different walking patterns by analyzing
video-level human silhouettes, rather than relying on appearance information.
Previous research on gait recognition has primarily focused on extracting local
or global spatial-temporal representations, while overlooking the intrinsic
periodic features of gait sequences, which, when fully utilized, can
significantly enhance performance. In this work, we propose a plug-and-play
strategy, called Temporal Periodic Alignment (TPA), which leverages the
periodic nature and fine-grained temporal dependencies of gait patterns. The
TPA strategy comprises two key components. The first component is Adaptive
Fourier-transform Position Encoding (AFPE), which adaptively converts features
and discrete-time signals into embeddings that are sensitive to periodic
walking patterns. The second component is the Temporal Aggregation Module
(TAM), which separates embeddings into trend and seasonal components, and
extracts meaningful temporal correlations to identify primary components, while
filtering out random noise. We present a simple and effective baseline method
for gait recognition, based on the TPA strategy. Extensive experiments
conducted on three popular public datasets (CASIA-B, OU-MVLP, and GREW)
demonstrate that our proposed method achieves state-of-the-art performance on
multiple benchmark tests
Foodborne Pathogens of Enterobacteriaceae, Their Detection and Control
Foodborne pathogens of Enterobacteriaceae including Escherichia coli, Salmonella, Shigella, Yersinia, etc., causes a great number of diseases and has a significant impact on human health. Here, we reviewed the prevalence, virulence, and antimicrobial susceptibility of Enterobacteriaceae belonging to 4 genera: E. coli, Salmonella, Shigella, and Yersinia. The routes of the pathogens’ transmission in the food chain; the antimicrobial resistance, genetic diversity, and molecular epidemiology of the Enterobacteriaceae strains; novel technologies for detection of the bacterial communities (such as the molecular marker-based methods, Immunoaffinity based detection, etc.); and the controlling of the foodborne pathogens using chemical/natural compounds or physical methods (such as UV-C and pulsed-light treatment, etc.), is also summarized
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