Muhammad Haris Khan works at Mohamed Bin Zayed University of Artificial Intelligence.

Posts

Matching Confidences and Softened Target Occurrences for Calibration

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The problem of calibrating deep neural networks (DNNs) is gaining attention, as these networks are becoming central to many real-world applications. Different attempts have been made to counter the poor calibration of DNNs. Amongst others, train-time calibration methods have unfolded as an effective class for improving model calibration. Motivated by this, we propose a novel train-time calibration method that is built on a new auxiliary loss formulation, namely multiclass alignment of confidences with the gradually softened ground truth occurrences (MACSO). It is developed on the intuition that, for a class, the gradually softened ground truth occurrences distribution is a suitable non-zero entropy signal whose better alignment withthe predicted confidences distribution is positively correlated with reducing the model calibration error. In our train-time approach, besides simply aligning the two distributions, e.g., via their means or KL divergence, we propose to quantify the linear correlation between the two distributions, which preserves the relations among them, thereby further improving the calibration performance. Finally, we also reveal that MACSO posses desirable theoretical properties. Extensive results on several challenging datasets, featuring in and out-of-domain scenarios, class imbalanced problem, and a medical image classification task, validate the efficacy of our method against state-of-the-art train-time calibration methods.

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Why Not Use Your Textbook? Knowledge-Enhanced Procedure Planning of Instructional Videos

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In this paper we explore the capability of an agent to construct a logical sequence of action steps thereby assembling a strategic procedural plan. This plan is crucial for navigating from an initial visual observation to a target visual outcome as depicted in real-life instructional videos. Existing works have attained partial success by extensively leveraging various sources of information available in the datasets such as heavy intermediate visual observations procedural names or natural language step-by-step instructions for features or supervision signals. However the task remains formidable due to the implicit causal constraints in the sequencing of steps and the variability inherent in multiple feasible plans. To tackle these intricacies that previous efforts have overlooked we propose to enhance the agent’s capabilities by infusing it with procedural knowledge. This knowledge sourced from training procedure plans and structured as a directed weighted graph equips the agent to better navigate the complexities of step sequencing and its potential variations. We coin our approach KEPP a novel Knowledge-Enhanced Procedure Planning system which harnesses a probabilistic procedural knowledge graph extracted from training data effectively acting as a comprehensive textbook for the training domain. Experimental evaluations across three widely-used datasets under settings of varying complexity reveal that KEPP attains superior state-of-the-art results while requiring only minimal supervision. Code and trained model are available at https://github.com/Ravindu-Yasas-Nagasinghe/KEPP

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MSI: Maximize Support-Set Information for Few-Shot Segmentation

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Few-Shot Segmentation FSS (Few-shot segmentation) aims to segment a target class using a small number of labeled images (support set). To extract information relevant to the target class, a dominant approach in best performing FSS methods removes background features using a support mask. We observe that this feature excision through a limiting support mask introduces an information bottleneck in several challenging FSS cases, e.g., for small targets and/or inaccurate target boundaries. To this end, we present a novel method (MSI), which maximizes the support-set information by exploiting two complementary sources of features to generate super correlation maps. We validate the effectiveness of our approach by instantiating it into three recent and strong FSS methods. Experimental results on several publicly available FSS benchmarks show that our proposed method consistently improves performance by visible margins and leads to faster convergence.

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