12,198 research outputs found
Prompt-guided DETR with RoI-pruned masked attention for open-vocabulary object detection
No full textPrompt-OVD is an efficient and effective DETR-based framework for open -vocabulary object detection that utilizes class embeddings from CLIP as prompts, guiding the Transformer decoder to detect objects in base and novel classes. Additionally, our RoI-pruned masked attention helps leverage the zero -shot classification ability of the Vision Transformer -based CLIP, resulting in improved detection performance at a minimal computational cost. Our experiments on the OV-COCO and OV-LVIS datasets demonstrate that Prompt-OVD achieves an impressive 21.2 times faster inference speed than the first end -to -end open -vocabulary detection method (OVDETR), while also achieving higher APs than four two -stage methods operating within similar inference time ranges. We release the code at https://github.com/DISL-Lab/Prompt-OVD.
Re-thinking Federated Active Learning based on Inter-class Diversity
No full textAlthough federated learning has made awe-inspiring advances, most studies have assumed that the client's data are fully labeled. However, in a real-world scenario, every client may have a significant amount of unlabeled instances. Among the various approaches to utilizing unlabeled data, a federated active learning framework has emerged as a promising solution. In the decentralized setting, there are two types of available query selector models, namely 'global' and 'local-only' models, but little literature discusses their performance dominance and its causes. In this work, we first demonstrate that the superiority of two selector models depends on the global and local interclass diversity. Furthermore, we observe that the global and local-only models are the keys to resolving the imbalance of each side. Based on our findings, we propose LoGo, a FAL sampling strategy robust to varying local heterogeneity levels and global imbalance ratio, that integrates both models by two steps of active selection scheme. LoGo consistently outperforms six active learning strategies in the total number of 38 experimental settings. The code is available at: https://github.com/raymin0223/LoGo
Machine Learning Robustness, Fairness, and their Convergence
No full textResponsible AI becomes critical where robustness and fairness must be satisfied together. Traditionally, the two topics have been studied by different communities for different applications. Robust training is designed for noisy or poisoned data where image data is typically considered. In comparison, fair training primarily deals with biased data where structured data is typically considered. Nevertheless, robust training and fair training are fundamentally similar in considering that both of them aim at fixing the inherent flaws of real-world data. In this tutorial, we first cover state-of-the-art robust training techniques where most of the research is on combating various label noises. In particular, we cover label noise modeling, robust training approaches, and real-world noisy data sets. Then, proceeding to the related fairness literature, we discuss pre-processing, in-processing, and post-processing unfairness mitigation techniques, depending on whether the mitigation occurs before, during, or after the model training. Finally, we cover the recent trend emerged to combine robust and fair training in two flavors: the former is to make the fair training more robust (i.e., robust fair training), and the latter is to consider robustness and fairness as two equals to incorporate them into a holistic framework. This tutorial is indeed timely and novel because the convergence of the two topics is increasingly common, but yet to be addressed in tutorials. The tutors have extensive experience publishing papers in top-tier machine learning and data mining venues and developing machine learning platforms. © 2021 Owner/Author
Robust Learning by Self-Transition for Handling Noisy Labels
No full textReal-world data inevitably contains noisy labels, which induce the poor generalization of deep neural networks. It is known that the network typically begins to rapidly memorize false-labeled samples after a certain point of training. Thus, to counter the label noise challenge, we propose a novel self-transitional learning method called MORPH, which automatically switches its learning phase at the transition point from seeding to evolution. In the seeding phase, the network is updated using all the samples to collect a seed of clean samples. Then, in the evolution phase, the network is updated using only the set of arguably clean samples, which precisely keeps expanding by the updated network. Thus, MORPH effectively avoids the overfitting to false-labeled samples throughout the entire training period. Extensive experiments using five real-world or synthetic benchmark datasets demonstrate substantial improvements over state-of-the-art methods in terms of robustness and efficiency. © 2021 ACM
Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspective
No full textData-centric AI is at the center of a fundamental shift in software engineering where machine learning becomes the new software, powered by big data and computing infrastructure. Here, software engineering needs to be re-thought where data become a first-class citizen on par with code. One striking observation is that a significant portion of the machine learning process is spent on data preparation. Without good data, even the best machine learning algorithms cannot perform well. As a result, data-centric AI practices are now becoming mainstream. Unfortunately, many datasets in the real world are small, dirty, biased, and even poisoned. In this survey, we study the research landscape for data collection and data quality primarily for deep learning applications. Data collection is important because there is lesser need for feature engineering for recent deep learning approaches, but instead more need for large amounts of data. For data quality, we study data validation, cleaning, and integration techniques. Even if the data cannot be fully cleaned, we can still cope with imperfect data during model training using robust model training techniques. In addition, while bias and fairness have been less studied in traditional data management research, these issues become essential topics in modern machine learning applications. We thus study fairness measures and unfairness mitigation techniques that can be applied before, during, or after model training. We believe that the data management community is well poised to solve these problems.
RA-TTA: Retrieval-Augmented Test-Time Adaptation for Vision-Language Models
No full textVision-language models (VLMs) are known to be susceptible to distribution shifts between pre-training data and test data, and test-time adaptation (TTA) methods for VLMs have been proposed to mitigate the detrimental impact of the distribution shifts. However, the existing methods solely rely on the internal knowledge encoded within the model parameters, which are constrained to pre-training data. To complement the limitation of the internal knowledge, we propose Retrieval-Augmented-TTA (RA-TTA) for adapting VLMs to test distribution using external knowledge obtained from a web-scale image database. By fully exploiting the bi-modality of VLMs, RA-TTA adaptively retrieves proper external images for each test image to refine VLMs' predictions using the retrieved external images, where fine-grained text descriptions are leveraged to extend the granularity of external knowledge. Extensive experiments on 17 datasets demonstrate that the proposed RA-TTA outperforms the state-of-the-art methods by 3.01-9.63% on average
ReFeed: Multi-dimensional Summarization Refinement with Reflective Reasoning on Feedback
No full textMobility Networked Time-Series Forecasting Benchmark Datasets
No full textHuman mobility is crucial for urban planning (e.g., public transportation) and epidemic response strategies. However, existing research often neglects integrating comprehensive perspectives on spatial dynamics, temporal trends, and other contextual views due to the limitations of existing mobility datasets. To bridge this gap, we introduce MOBINS (MOBIlity Networked time Series), a novel dataset collection designed for networked time-series forecasting of dynamic human movements. MOBINS features diverse and explainable datasets that capture various mobility patterns across different transportation modes in four cities and two countries and cover both transportation and epidemic domains at the administrative area level. Our experiments with nine baseline methods reveal the significant impact of different model backbones on the proposed six datasets. We provide a valuable resource for advancing urban mobility research
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