arXiv subjects

Aditya Kumar Singh

Publications and source records attributed to Aditya Kumar Singh.

7 recordsLinked to original sources

arXiv2026

DUET-VLM: Dual stage Unified Efficient Token reduction for VLM Training and Inference

Vision-language models (VLMs) have achieved remarkable multimodal understanding and reasoning capabilities, yet remain computationally expensive due to dense visual tokenization. Existing efficiency approaches either merge redundant visual tokens or drop them progressively in language backbone, often trading accuracy for speed. In this work, we propose DUET-VLM, a versatile plug-and-play dual compression framework that consists of (a) vision-only redundancy aware compression of vision encoder's output into information-preserving tokens, followed by (b) layer-wise, salient text-guided dropping of visual tokens within the language backbone to progressively prune less informative tokens. This coordinated token management enables aggressive compression while retaining critical semantics. On LLaVA-1.5-7B, our approach maintains over 99% of baseline accuracy with 67% fewer tokens, and still retains >97% even at 89% reduction. With this dual-stage compression during training, it achieves 99.7% accuracy at 67% and 97.6% at 89%, surpassing prior SoTA visual token reduction methods across multiple benchmarks. When integrated into Video-LLaVA-7B, it even surpasses the baseline -- achieving >100% accuracy with a substantial 53.1% token reduction and retaining 97.6% accuracy under an extreme 93.4% setting. These results highlight end-to-end training with DUET-VLM, enabling robust adaptation to reduced visual (image/video) input without sacrificing accuracy, producing compact yet semantically rich representations within the same computational budget. Our code is available at https://github.com/AMD-AGI/DUET-VLM.

cs.CV↗

arXiv2026

Transport of $D^0$ meson in hadronic matter in the domain of Non-Extensive statistics

In this work, we investigate the drag and diffusion coefficients of $D^0$ meson propagating through a hadronic thermal bath by employing the Fokker Planck equation within the framework of Tsallis non extensive statistics. The non extensive parameter, $q$ accounts for the deviation from equilibrium and provides a more realistic description of the medium that is not perfectly thermalized. The hadronic bath, consisting of various mesonic and baryonic species, is characterized by different mass cutoffs that control the spectral composition of the medium. Our analysis shows that both the drag, $F$ and momentum diffusion coefficients, $\Gamma$ increases with temperature and also increases with increasing $q$ and mass cutoff. The spatial diffusion coefficient, $D_x$ exhibits a decreasing trend with temperature $T$, $q$ and mass cutoff which highlights the significant influence of non-equilibrium effects and hadronic composition on the transport behaviour of $D^0$ meson, offering valuable insights into the thermal and dynamical properties of the hadronic phase in heavy ion collisions. In this study while calculating the spatial diffusion coefficient, $D_x$ we observe that beyond $q$ = 1.24, $D_x$ goes below to the lower limit proposed in Ads/CFT theory. This suggests that 1.24 is the upper limit of non extensive parameter, $q$.

hep-ph↗

arXiv2025

Machine Learning Algorithms in Statistical Modelling Bridging Theory and Application

It involves the completely novel ways of integrating ML algorithms with traditional statistical modelling that has changed the way we analyze data, do predictive analytics or make decisions in the fields of the data. In this paper, we study some ML and statistical model connections to understand ways in which some modern ML algorithms help 'enrich' conventional models; we demonstrate how new algorithms improve performance, scale, flexibility and robustness of the traditional models. It shows that the hybrid models are of great improvement in predictive accuracy, robustness, and interpretability

cs.LG↗

arXiv2024

"Previously on ..." From Recaps to Story Summarization

We introduce multimodal story summarization by leveraging TV episode recaps - short video sequences interweaving key story moments from previous episodes to bring viewers up to speed. We propose PlotSnap, a dataset featuring two crime thriller TV shows with rich recaps and long episodes of 40 minutes. Story summarization labels are unlocked by matching recap shots to corresponding sub-stories in the episode. We propose a hierarchical model TaleSumm that processes entire episodes by creating compact shot and dialog representations, and predicts importance scores for each video shot and dialog utterance by enabling interactions between local story groups. Unlike traditional summarization, our method extracts multiple plot points from long videos. We present a thorough evaluation on story summarization, including promising cross-series generalization. TaleSumm also shows good results on classic video summarization benchmarks.

cs.CV↗

arXiv2023

Nuclear Modification Factor in Pb-Pb and p-Pb collisions at $\sqrt{s_{NN}}$=5.02 TeV at LHC energies using Boltzmann Transport Equation with Tsallis Blast Wave Description

In this article, we have studied the nuclear modification factor measured in Pb-Pb collisions ($R_{PbPb}$) for $π^{\pm}$, $K^{\pm}$, $p+\bar{p}$, $K^{*0} + \bar{K^{*0}}$, $ϕ$ and in p-Pb collisions ($R_{pPb}$) for $π^{\pm}$, $K^{\pm}$, $p+\bar{p}$ at Large hadron collider (LHC) energy of $\sqrt{s_{NN}}$ = 5.02 TeV for the most central and peripheral collisions. We have also analysed the experimental data of transverse momentum ($p_T$) spectra for these identified hadrons at LHC for Pb-Pb as well as for p-Pb collisions. We have used Boltzmann transport equation (BTE) in relaxation time approximation (RTA) for this analysis. The Tsallis statistics is used as an initial distribution function and The Tsallis blast wave (TBW) model is employed as an equilibrium distribution in BTE. The present model fits the measured transverse momentum spectra, $R_{PbPb}$, and $R_{pPb}$ successfully upto $p_T$ = 8 GeV with a reasonable $χ^2/ndf$ for all the considered hadrons at various centralities. The experimental data for $R_{pPb}$ are generated using the particle yields at pPb and pp collisions where number of binary collisions are taken from Glauber model calculations. We find that the average transverse flow velocity ($<β_r>$) follows the mass and centrality ordering and decreases with the mass as well as when one move from the central collisions to peripheral collisions. These findings are inline with the results of the hydrodynamical calculations.

nucl-th↗

arXiv2023

How you feelin'? Learning Emotions and Mental States in Movie Scenes

Movie story analysis requires understanding characters' emotions and mental states. Towards this goal, we formulate emotion understanding as predicting a diverse and multi-label set of emotions at the level of a movie scene and for each character. We propose EmoTx, a multimodal Transformer-based architecture that ingests videos, multiple characters, and dialog utterances to make joint predictions. By leveraging annotations from the MovieGraphs dataset, we aim to predict classic emotions (e.g. happy, angry) and other mental states (e.g. honest, helpful). We conduct experiments on the most frequently occurring 10 and 25 labels, and a mapping that clusters 181 labels to 26. Ablation studies and comparison against adapted state-of-the-art emotion recognition approaches shows the effectiveness of EmoTx. Analyzing EmoTx's self-attention scores reveals that expressive emotions often look at character tokens while other mental states rely on video and dialog cues.

cs.CV↗

arXiv2022

Multi-Label Classification on Remote-Sensing Images

Acquiring information on large areas on the earth's surface through satellite cameras allows us to see much more than we can see while standing on the ground. This assists us in detecting and monitoring the physical characteristics of an area like land-use patterns, atmospheric conditions, forest cover, and many unlisted aspects. The obtained images not only keep track of continuous natural phenomena but are also crucial in tackling the global challenge of severe deforestation. Among which Amazon basin accounts for the largest share every year. Proper data analysis would help limit detrimental effects on the ecosystem and biodiversity with a sustainable healthy atmosphere. This report aims to label the satellite image chips of the Amazon rainforest with atmospheric and various classes of land cover or land use through different machine learning and superior deep learning models. Evaluation is done based on the F2 metric, while for loss function, we have both sigmoid cross-entropy as well as softmax cross-entropy. Images are fed indirectly to the machine learning classifiers after only features are extracted using pre-trained ImageNet architectures. Whereas for deep learning models, ensembles of fine-tuned ImageNet pre-trained models are used via transfer learning. Our best score was achieved so far with the F2 metric is 0.927.

cs.CV↗