arXiv subjects

Akshit Sharma

Publications and source records attributed to Akshit Sharma.

4 recordsLinked to original sources

arXiv2026

Small Cues, Big Consequences: Learning Pivotal Cues for Multimodal Meme Classification

Memes often derive their harmful, hateful, or sarcastic meaning from small but decisive visual, textual, or cross-modal cues. Existing multimodal classifiers can miss such evidence when relying mainly on global image-text representations. We introduce MemeCF, a cue-focused benchmark of 9,895 memes across harm, hate, and sarcasm, with annotations identifying the modality and rationale of the pivotal evidence. We also propose MemePIVOT, a local-global architecture for meme classification. MemePIVOT uses frozen CLIP features, unbalanced optimal transport to align words with image patches while allowing irrelevant evidence to remain unmatched, and an evidential fusion head to combine local grounding with global meme context under uncertainty. Experiments on HarMeme, PrideMM, and MemeCF show consistent gains over strong text-only, image-only, multimodal, and vision-language baselines. Cross-dataset and ablation results further show that explicit pivotal-evidence modeling improves robustness and contributes meaningfully beyond global multimodal representations. Our code and dataset are publicly available at https://github.com/AkshitSharma1/MemePIVOT

cs.MM↗

arXiv2026

MemeTAG: Keyword-Driven Meme Classification through Tag Embedding Reconstruction

The proliferation of harmful internet memes poses a significant societal threat, yet their automated classification remains a formidable algorithmic challenge due to the nuanced, multimodal nature of their content. To address this, we introduce MemeTAG, a novel dual-objective framework that pioneers a keyword-aware approach to meme classification. Our core innovation is a two-part semantic guidance mechanism: first, we leverage a pretrained Vision-Language Model to generate a set of descriptive keywords, that capture the high-level semantics. Second, we introduce the Aggregated Tag Inference Network (ATIN), an attention-based module that distills these keywords into a single, rich semantic embedding. This embedding serves as a target for a novel auxiliary reconstruction loss, which compels the model to learn deeply aligned visual and textual features. This approach, combined with an efficient three-stage training strategy, establishes a new state-of-the-art on the HarMeme, Hateful Memes Challenge (HMC), and PrideMM datasets, decisively outperforming existing state-of-the-art methods.

cs.CV↗

arXiv2026

Quadratic Gauge Transformation

Symmetries plays a significant role in understanding the conservation laws in Quantum field theories. Here, we attempted a quadratic type dimensionless gauge transformation to achieve the invariance in QFTs. We have shown the extensive study of invariance of complex scalar, Abelian and Non- Abelian theories and established the conservation laws. We included an explicit graphical analysis to invoke the invariance. This is studied in a physical context, where different field configurations correspond to the same physical state. The necessity of the covariant derivative is studied in detail, highlighting how it ensures consistent transformation under local symmetry operations. The meaning of covariance is clarified as the preservation of the form of physical laws under transformations.

hep-th↗

arXiv2024

FLEXIS: FLEXible Frequent Subgraph Mining using Maximal Independent Sets

Frequent Subgraph Mining (FSM) is the process of identifying common subgraph patterns that surpass a predefined frequency threshold. While FSM is widely applicable in fields like bioinformatics, chemical analysis, and social network anomaly detection, its execution remains time-consuming and complex. This complexity stems from the need to recognize high-frequency subgraphs and ascertain if they exceed the set threshold. Current approaches to identifying these patterns often rely on edge or vertex extension methods. However, these strategies can introduce redundancies and cause increased latency. To address these challenges, this paper introduces a novel approach for identifying potential k-vertex patterns by combining two frequently observed (k - 1)-vertex patterns. This method optimizes the breadth-]first search, which allows for quicker search termination based on vertices count and support value. Another challenge in FSM is the validation of the presumed pattern against a specific threshold. Existing metrics, such as Maximum Independent Set (MIS) and Minimum Node Image (MNI), either demand significant computational time or risk overestimating pattern counts. Our innovative approach aligns with the MIS and identifies independent subgraphs. Through the "Maximal Independent Set" metric, this paper offers an efficient solution that minimizes latency and provides users with control over pattern overlap. Through extensive experimentation, our proposed method achieves an average of 10.58x speedup when compared to GraMi and an average 3x speedup when compared to T-FSM

cs.DB↗