Utsav Kumar Nareti

Utsav Kumar Nareti, Chandranath Adak, Soumi Chattopadhyay

In the film industry, movie posters have been an essential part of advertising and marketing for many decades, and continue to play a vital role even today in the form of digital posters through online, social media and OTT (over-the-top) platforms. Typically, movie posters can effectively promote and communicate the essence of a film, such as its genre, visual style/tone, vibe and storyline cue/theme, which are essential to attract potential viewers. Identifying the genres of a movie often has significant practical applications in recommending the film to target audiences. Previous studies on genre identification have primarily focused on sources such as plot synopses, subtitles, metadata, movie scenes, and trailer videos; however, posters precede the availability of these sources, and provide pre-release implicit information to generate mass interest. In this paper, we work for automated multi-label movie genre identification only from poster images, without any aid of additional textual/metadata/video information about movies, which is one of the earliest attempts of its kind. Here, we present a deep transformer network with a probabilistic module to identify the movie genres exclusively from the poster. For experiments, we procured 13882 number of posters of 13 genres from the Internet Movie Database (IMDb), where our model performances were encouraging and even outperformed some major contemporary architectures.

Suraj Kumar, Utsav Kumar Nareti, Soumi Chattopadhyay et al.

Accurate book genre classification is fundamental to digital library organization, content discovery, and personalized recommendation. Existing approaches typically model genre prediction as a flat, single-label task, ignoring hierarchical genre structure and relying heavily on noisy, subjective user reviews, which often degrade classification reliability. We propose HiGeMine, a two-phase hierarchical genre mining framework that robustly integrates user reviews with authoritative book blurbs. In the first phase, HiGeMine employs a zero-shot semantic alignment strategy to filter reviews, retaining only those semantically consistent with the corresponding blurb, thereby mitigating noise, bias, and irrelevance. In the second phase, we introduce a dual-path, two-level graph-based classification architecture: a coarse-grained Level-1 binary classifier distinguishes fiction from non-fiction, followed by Level-2 multi-label classifiers for fine-grained genre prediction. Inter-genre dependencies are explicitly modeled using a label co-occurrence graph, while contextual representations are derived from pretrained language models applied to the filtered textual content. To facilitate systematic evaluation, we curate a new hierarchical book genre dataset. Extensive experiments demonstrate that HiGeMine consistently outperformed strong baselines across hierarchical genre classification tasks. The proposed framework offers a principled and effective solution for leveraging both structured and unstructured textual data in hierarchical book genre analysis.

Utsav Kumar Nareti, Soumi Chattopadhyay, Prolay Mallick et al.

Identifying fine-grained book genres is essential for enhancing user experience through efficient discovery, personalized recommendations, and improved reader engagement. At the same time, it provides publishers and marketers with valuable insights into consumer preferences and emerging market trends. While traditional genre classification methods predominantly rely on textual reviews or content analysis, the integration of additional modalities, such as book covers, blurbs, and metadata, offers richer contextual cues. However, the effectiveness of such multi-modal systems is often hindered by incomplete, noisy, or missing data across modalities. To address this, we propose IMAGINE (Intelligent Multi-modal Adaptive Genre Identification NEtwork), a framework designed to leverage multi-modal data while remaining robust to missing or unreliable information. IMAGINE learns modality-specific feature representations and adaptively prioritizes the most informative sources available at inference time. It further employs a hierarchical classification strategy, grounded in a curated taxonomy of book genres, to capture inter-genre relationships and support multi-label assignments reflective of real-world literary diversity. A key strength of IMAGINE is its adaptability: it maintains high predictive performance even when one modality, such as text or image, is unavailable. We also curated a large-scale hierarchical dataset that structures book genres into multiple levels of granularity, allowing for a more comprehensive evaluation. Experimental results demonstrate that IMAGINE outperformed strong baselines in various settings, with significant gains in scenarios involving incomplete modality-specific data.

Utsav Kumar Nareti, Suraj Kumar, Soumya Pandey et al.

The surge in user-generated reviews has amplified the need for interpretable models that can provide fine-grained insights. Existing prototype-based models offer intuitive explanations but typically operate at coarse granularity (sentence or document level) and fail to address the multi-label nature of real-world text classification. We propose ProtoSiTex, a semi-interpretable framework designed for fine-grained multi-label text classification. ProtoSiTex employs a dual-phase alternating training strategy: an unsupervised prototype discovery phase that learns semantically coherent and diverse prototypes, and a supervised classification phase that maps these prototypes to class labels. A hierarchical loss function enforces consistency across sub-sentence, sentence, and document levels, enhancing interpretability and alignment. Unlike prior approaches, ProtoSiTex captures overlapping and conflicting semantics using adaptive prototypes and multi-head attention. We also introduce a benchmark dataset of hotel reviews annotated at the sub-sentence level with multiple labels. Experiments on this dataset and two public benchmarks (binary and multi-class) show that ProtoSiTex achieves state-of-the-art performance while delivering faithful, human-aligned explanations, establishing it as a robust solution for semi-interpretable multi-label text classification.

Utsav Kumar Nareti, Chandranath Adak, Soumi Chattopadhyay et al.

Movie posters are not just decorative; they are meticulously designed to capture the essence of a movie, such as its genre, storyline, and tone/vibe. For decades, movie posters have graced cinema walls, billboards, and now our digital screens as a form of digital posters. Movie genre classification plays a pivotal role in film marketing, audience engagement, and recommendation systems. Previous explorations into movie genre classification have been mostly examined in plot summaries, subtitles, trailers and movie scenes. Movie posters provide a pre-release tantalizing glimpse into a film's key aspects, which can ignite public interest. In this paper, we presented the framework that exploits movie posters from a visual and textual perspective to address the multilabel movie genre classification problem. Firstly, we extracted text from movie posters using an OCR and retrieved the relevant embedding. Next, we introduce a cross-attention-based fusion module to allocate attention weights to visual and textual embedding. In validating our framework, we utilized 13882 posters sourced from the Internet Movie Database (IMDb). The outcomes of the experiments indicate that our model exhibited promising performance and outperformed even some prominent contemporary architectures.