447 research outputs found
Object Detection in 20 Years: A Survey
Object detection, as of one the most fundamental and challenging problems in
computer vision, has received great attention in recent years. Its development
in the past two decades can be regarded as an epitome of computer vision
history. If we think of today's object detection as a technical aesthetics
under the power of deep learning, then turning back the clock 20 years we would
witness the wisdom of cold weapon era. This paper extensively reviews 400+
papers of object detection in the light of its technical evolution, spanning
over a quarter-century's time (from the 1990s to 2019). A number of topics have
been covered in this paper, including the milestone detectors in history,
detection datasets, metrics, fundamental building blocks of the detection
system, speed up techniques, and the recent state of the art detection methods.
This paper also reviews some important detection applications, such as
pedestrian detection, face detection, text detection, etc, and makes an in-deep
analysis of their challenges as well as technical improvements in recent years.Comment: This work has been submitted to the IEEE TPAMI for possible
publicatio
Machine learning in the real world with multiple objectives
Machine learning (ML) is ubiquitous in many real-world applications. Existing ML systems are based on optimizing a single quality metric such as prediction accuracy. These metrics typically do not fully align with real-world design constraints such as computation, latency, fairness, and acquisition costs that we encounter in real-world applications. In this thesis, we develop ML methods for optimizing prediction accuracy while accounting for such real-world constraints. In particular, we introduce multi-objective learning in two different setups: resource-efficient prediction and algorithmic fairness in language models.
First, we focus on decreasing the test-time computational costs of prediction systems. Budget constraints arise in many machine learning problems. Computational costs limit the usage of many models on small devices such as IoT or mobile phones and increase the energy consumption in cloud computing. We design systems that allow on-the-fly modification of the prediction model for each input sample. These sample-adaptive systems allow us to leverage wide variability in sample complexity where we learn policies for selecting cheap models for low complexity instances and using descriptive models only for complex ones. We utilize multiple--objective approach where one minimizes the system cost while preserving predictive accuracy. We demonstrate significant speed-ups in the fields of computer vision, structured prediction, natural language processing, and deep learning.
In the context of fairness, we first demonstrate that a naive application of ML methods runs the risk of amplifying social biases present in data. This danger is particularly acute for methods based on word embeddings, which are increasingly gaining importance in many natural language processing applications of ML. We show that word embeddings trained on Google News articles exhibit female/male gender stereotypes. We demonstrate that geometrically, gender bias is captured by unique directions in the word embedding vector space. To remove bias we formulate a empirical risk objective with fairness constraints to remove stereotypes from embeddings while maintaining desired associations. Using crowd-worker evaluation as well as standard benchmarks, we empirically demonstrate that our algorithms significantly reduces gender bias in embeddings, while preserving its useful properties such as the ability to cluster related concepts
LF-ViT: Reducing Spatial Redundancy in Vision Transformer for Efficient Image Recognition
The Vision Transformer (ViT) excels in accuracy when handling high-resolution
images, yet it confronts the challenge of significant spatial redundancy,
leading to increased computational and memory requirements. To address this, we
present the Localization and Focus Vision Transformer (LF-ViT). This model
operates by strategically curtailing computational demands without impinging on
performance. In the Localization phase, a reduced-resolution image is
processed; if a definitive prediction remains elusive, our pioneering
Neighborhood Global Class Attention (NGCA) mechanism is triggered, effectively
identifying and spotlighting class-discriminative regions based on initial
findings. Subsequently, in the Focus phase, this designated region is used from
the original image to enhance recognition. Uniquely, LF-ViT employs consistent
parameters across both phases, ensuring seamless end-to-end optimization. Our
empirical tests affirm LF-ViT's prowess: it remarkably decreases Deit-S's FLOPs
by 63\% and concurrently amplifies throughput twofold. Code of this project is
at https://github.com/edgeai1/LF-ViT.git
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