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Rishav Pramanik

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3 papers

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NeurIPS Conference 2025 Conference Paper

Rendering-Aware Reinforcement Learning for Vector Graphics Generation

Juan Rodriguez
Haotian Zhang
Abhay Puri
Rishav Pramanik
Aarash Feizi
Pascal Wichmann
Arnab Mondal
Mohammad R. Samsami

Scalable Vector Graphics (SVG) offer a powerful format for representing visual designs as interpretable code. Recent advances in vision-language models (VLMs) have enabled high-quality SVG generation by framing the problem as a code generation task and leveraging large-scale pretraining. VLMs are particularly suitable for this task as they capture both global semantics and fine-grained visual patterns, while transferring knowledge across vision, natural language, and code domains. However, existing VLM approaches often struggle to produce faithful and efficient SVGs because they never observe the rendered images during training. Although differentiable rendering for autoregressive SVG code generation remains unavailable, rendered outputs can still be compared to original inputs, enabling evaluative feedback suitable for reinforcement learning (RL). We introduce Reinforcement Learning from Rendering Feedback, an RL method that enhances SVG generation in autoregressive VLMs by leveraging feedback from rendered SVG outputs. Given an input image, the model generates SVG roll-outs that are rendered and compared to the original image to compute a reward. This visual fidelity feedback guides the model toward producing more accurate, efficient, and semantically coherent SVGs. \method significantly outperforms supervised fine-tuning, addressing common failure modes and enabling precise, high-quality SVG generation with strong structural understanding and generalization.

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EAAI Journal 2023 Journal Article

MSENet: Mean and standard deviation based ensemble network for cervical cancer detection

Rishav Pramanik
Bihan Banerjee
Ram Sarkar

Cervical cancer is one of the most concerning carcinogenic diseases among women worldwide. The condition is especially bad in low- or middle-income countries due to the lack of medical facilities. In such situations, computer-aided diagnosis (CAD) systems can alleviate the need to a large extent. However, sometimes a single learning model may not be effective enough to capture relevant information for accurate prediction of diseases from complex data. To this end, we propose an ensemble of deep learning models, called Mean and Standard Deviation-based Ensemble Network (MSENet), for detecting cervical cancer from Pap smear images. Our ensemble model consists of three standard base classifiers, namely Xception, Inception V3, and VGG-16. We further improve the classification abilities of these models by implementing a novel probability enhancement scheme that takes into account the mean and standard deviation of the confidence scores. This technique enables the overall framework to capture complementary information offered by the base classifiers and is tailored to the characteristics of deep learners. Finally, we use the product rule to aggregate the obtained outcomes and get final predictions. The proposed MSENet has been evaluated on a standard public benchmark dataset, called SIPaKMeD. With a classification accuracy of 97. 21% using a 5-fold cross-validation scheme, the MSENet outperforms many state-of-the-art methods. The source codes are made public in the following Github repository https: //github. com/rishavpramanik/msenet.

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EAAI Journal 2023 Journal Article

Transformer-based deep reverse attention network for multi-sensory human activity recognition

Rishav Pramanik
Ritodeep Sikdar
Ram Sarkar

In today’s era, one of the important applications of Artificial Intelligence (AI) is Human Activity Recognition (HAR). It has a wide range of applicability in health monitoring for patients with chronic diseases, gaming consoles for gesture recognition, etc. Sensor-based HAR systems use signals collected over a period of time to label an activity. When we design an efficient sensor-based HAR system, a model requires learning an optimal association of spatial and temporal features. In this article, we propose a sensor-based HAR technique using the deep learning approach. We present a deep reverse transformer-based attention mechanism to guide the side residual features Unlike the conventional bottom-up approaches for feature fusion, we exploit a top-down feature fusion approach. The reverse attention is self-calibrated throughout the course of learning, which regularizes the attention modules and dynamically adjusts the learning rate. The overall framework outperforms several state-of-the-art methods and is shown to be statistically significant against these methods on five publicly available sensor-based HAR datasets, namely, MHEALTH, USC-HAD, WHARF, UTD-MHAD1, and UTD-MHAD2. Further, we conduct an ablation study to showcase the importance of each of the components of the proposed framework. Source code of this work is available at https: //github. com/rishavpramanik/RevTransformerAttentionHAR.

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