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A. N. Rajagopalan

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3 papers

1 author row

AAAI Conference 2023 Short Paper

Exploring the Effectiveness of Mask-Guided Feature Modulation as a Mechanism for Localized Style Editing of Real Images (Student Abstract)

Snehal Singh Tomar
Maitreya Suin
A. N. Rajagopalan

The success of Deep Generative Models at high-resolution image generation has led to their extensive utilization for style editing of real images. Most existing methods work on the principle of inverting real images onto their latent space, followed by determining controllable directions. Both inversion of real images and determination of controllable latent directions are computationally expensive operations. Moreover, the determination of controllable latent directions requires additional human supervision. This work aims to explore the efficacy of mask-guided feature modulation in the latent space of a Deep Generative Model as a solution to these bottlenecks. To this end, we present the SemanticStyle Autoencoder (SSAE), a deep Generative Autoencoder model that leverages semantic mask-guided latent space manipulation for highly localized photorealistic style editing of real images. We present qualitative and quantitative results for the same and their analysis. This work shall serve as a guiding primer for future work.

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AAAI Conference 2020 Conference Paper

An Efficient Framework for Dense Video Captioning

Maitreya Suin
A. N. Rajagopalan

Dense video captioning is an extremely challenging task since an accurate and faithful description of events in a video requires a holistic knowledge of the video contents as well as contextual reasoning of individual events. Most existing approaches handle this problem by ﬁrst proposing event boundaries from a video and then captioning on a subset of the proposals. Generation of dense temporal annotations and corresponding captions from long videos can be dramatically source consuming. In this paper, we focus on the task of generating a dense description of temporally untrimmed videos and aim to signiﬁcantly reduce the computational cost by processing fewer frames while maintaining accuracy. Existing video captioning methods sample frames with a prede- ﬁned frequency over the entire video or use all the frames. Instead, we propose a deep reinforcement-based approach which enables an agent to describe multiple events in a video by watching a portion of the frames. The agent needs to watch more frames when it is processing an informative part of the video, and skip frames when there is redundancy. The agent is trained using actor-critic algorithm, where the actor determines the frames to be watched from a video and the critic assesses the optimality of the decisions taken by the actor. Such an efﬁcient frame selection simpliﬁes the event proposal task considerably. This has the added effect of reducing the occurrence of unwanted proposals. The encoded state representation of the frame selection agent is further utilized for guiding event proposal and caption generation tasks. We also leverage the idea of knowledge distillation to improve the accuracy. We conduct extensive evaluations on ActivityNet captions dataset to validate our method.

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AAAI Conference 2020 Conference Paper

Region-Adaptive Dense Network for Efficient Motion Deblurring

Kuldeep Purohit
A. N. Rajagopalan

In this paper, we address the problem of dynamic scene deblurring in the presence of motion blur. Restoration of images affected by severe blur necessitates a network design with a large receptive ﬁeld, which existing networks attempt to achieve through simple increment in the number of generic convolution layers, kernel-size, or the scales at which the image is processed. However, these techniques ignore the nonuniform nature of blur, and they come at the expense of an increase in model size and inference time. We present a new architecture composed of region adaptive dense deformable modules that implicitly discover the spatially varying shifts responsible for non-uniform blur in the input image and learn to modulate the ﬁlters. This capability is complemented by a self-attentive module which captures non-local spatial relationships among the intermediate features and enhances the spatially varying processing capability. We incorporate these modules into a densely connected encoder-decoder design which utilizes pre-trained Densenet ﬁlters to further improve the performance. Our network facilitates interpretable modeling of the spatially-varying deblurring process while dispensing with multi-scale processing and large ﬁlters entirely. Extensive comparisons with prior art on benchmark dynamic scene deblurring datasets clearly demonstrate the superiority of the proposed networks via signiﬁcant improvements in accuracy and speed, enabling almost real-time deblurring.

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