Multi-label text classification (MLTC) is an essential task in NLP applications.Traditional methods require extensive labeled data and are limited to fixed label sets.Extracting labels with large language models (LLMs) is more effective and universal, but incurs high computational costs.In this work, we introduce a distillation-based T5 generalist model for zero-shot MLTC and few-shot fine-tuning.Our model accommodates variable label sets with general domain-agnostic pretraining, while modeling dependency between labels.Experiments show that our approach outperforms baselines of similar size on three few-shot tasks.Our code is available at repository.

As pretrained language model emerge and consistently develop, prompt-based training has become a well-studied paradigm to improve the exploitation of models for many natural language processing tasks. Furthermore, prompting demonstrates great performance compared to conventional fine-tuning in scenarios with limited annotated data, such as zero-shot or few-shot situations. Verbalizers are crucial in this context, as they help interpret masked word distributions generated by language models into output predictions. This study introduces a benchmarking approach to assess three common baselines of verbalizers for topic classification in few-shot learning scenarios. Additionally, we find that increasing the number of label words for automatic label word searching enhances model performance. Moreover, we investigate the effectiveness of template assembling with various aggregation strategies to develop stronger classifiers that outperform models trained with individual templates. Our approach achieves comparable results to prior research while using significantly fewer resources. Our code is available at https://github.com/quang-anh-nguyen/verbalizer_benchmark.git.

In this paper, we present a probabilistic formulation of kernel-based tracking methods based upon maximum likelihood estimation. To this end, we view the coordinates for the pixels in both, the target model and its candidate as random variables and make use of a generative model so as to cast the tracking task into a maximum likelihood framework. This, in turn, permits the use of the EM-algorithm to estimate a set of latent variables that can be used to update the target-center position. Once the latent variables have been estimated, we use the Kullback-Leibler divergence so as to minimise the mutual information between the target model and candidate distributions in order to develop a target-center update rule and a kernel bandwidth adjustment scheme. The method is very general in nature. We illustrate the utility of our approach for purposes of tracking on real-world video sequences using two alternative kernel functions.

In this paper, we present an enhanced kernel-based tracker for monochromatic and thermographic video. The technique presented here employs the image intensity and the Local Binary Pattern (LBP) to construct a two dimensional histogram representative of the grayscale values and the texture of the target under study. With the histogram at hand, we proceed to compute its power density function. The new location of the object is then determined making use of a mean-shift optimisation approach. We illustrate the performance of our method in both, thermographic and monochromatic footages and compare our results to an alternative.