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Anshul Shah

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5 papers

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TMLR Journal 2025 Journal Article

DiffNat: Exploiting the Kurtosis Concentration Property for Image quality improvement

Aniket Roy
Maitreya Suin
Anshul Shah
Ketul Shah
Jiang Liu
Rama Chellappa

Diffusion models have significantly advanced generative AI in terms of creating and editing natural images. However, improving the image quality of generated images is still of paramount interest. In this context, we propose a generic kurtosis concentration (KC) loss that can be readily applied to any standard diffusion model pipeline to improve image quality. Our motivation stems from the projected kurtosis concentration property of natural images, which states that natural images have nearly constant kurtosis values across different band-pass filtered versions of the image. To improve the image quality of generated images, we reduce the gap between the highest and lowest kurtosis values across the band-pass filtered versions (e.g., Discrete Wavelet Transform (DWT)) of images. In addition, we also propose a novel condition-agnostic perceptual guidance strategy during inference to further improve the quality. We validate the proposed approach on four diverse tasks, viz., (1) personalized few-shot finetuning using text guidance, (2) unconditional image generation, (3) image super-resolution, and (4) blind face-restoration. Integrating the proposed KC loss and perceptual guidance has improved the perceptual quality in all these tasks in terms of FID, MUSIQ score, and user evaluation. Code: https://github.com/aniket004/DiffNat.git

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TMLR Journal 2025 Journal Article

Learning to Prompt Your Domain for Federated Vision-Language Models

Guoyizhe Wei
Feng Wang
Anshul Shah
Rama Chellappa

The prompt tuning paradigm, with its great advantages of low parameter count and stable training, has recently inspired numerous applications of CLIP-like vision-language models in federated learning. However, in this work, we posit that under significant domain gaps across federated participants, prompt-based CLIP may easily collapse to non-optimal solutions due to the neglect of domain-aware knowledge. We present a novel prompt tuning method, termed ADAPT, to address this issue by learning both intra- and inter-domain prompts. Specifically, we assign each federated participant a domain-specific prompt and use the image's visual features as a condition to guide the generation of language features, with the underlying idea that the prompted CLIP should detect the input image's domain correspondence before making the prediction of its category. Extensive experiments demonstrate ADAPT's significant efficiency and effectiveness in federated learning. For example, by learning and sharing only 2.1M parameters, ADAPT attains a 69.8% average accuracy over the six domains of DomainNet, which improves the original CLIP accuracy by 16.2%.

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NeurIPS Conference 2022 Conference Paper

FeLMi : Few shot Learning with hard Mixup

Aniket Roy
Anshul Shah
Ketul Shah
Prithviraj Dhar
Anoop Cherian
Rama Chellappa

Learning from a few examples is a challenging computer vision task. Traditionally, meta-learning-based methods have shown promise towards solving this problem. Recent approaches show benefits by learning a feature extractor on the abundantbase examples and transferring these to the fewer novel examples. However, thefinetuning stage is often prone to overfitting due to the small size of the noveldataset. To this end, we propose Few shot Learning with hard Mixup (FeLMi)using manifold mixup to synthetically generate samples that helps in mitigatingthe data scarcity issue. Different from a naïve mixup, our approach selects the hardmixup samples using an uncertainty-based criteria. To the best of our knowledge, we are the first to use hard-mixup for the few-shot learning problem. Our approachallows better use of the pseudo-labeled base examples through base-novel mixupand entropy-based filtering. We evaluate our approach on several common few-shotbenchmarks - FC-100, CIFAR-FS, miniImageNet and tieredImageNet and obtainimprovements in both 1-shot and 5-shot settings. Additionally, we experimented onthe cross-domain few-shot setting (miniImageNet → CUB) and obtain significantimprovements.

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AAAI Conference 2022 Conference Paper

Max-Margin Contrastive Learning

Anshul Shah
Suvrit Sra
Rama Chellappa
Anoop Cherian

Standard contrastive learning approaches usually require a large number of negatives for effective unsupervised learning and often exhibit slow convergence. We suspect this behavior is due to the suboptimal selection of negatives used for offering contrast to the positives. We counter this difficulty by taking inspiration from support vector machines (SVMs) to present max-margin contrastive learning (MMCL). Our approach selects negatives as the sparse support vectors obtained via a quadratic optimization problem, and contrastiveness is enforced by maximizing the decision margin. As SVM optimization can be computationally demanding, especially in an end-to-end setting, we present simplifications that alleviate the computational burden. We validate our approach on standard vision benchmark datasets, demonstrating better performance in unsupervised representation learning over state-of-the-art, while having better empirical convergence properties.

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TMLR Journal 2022 Journal Article

Object-aware Cropping for Self-Supervised Learning

Shlok Kumar Mishra
Anshul Shah
Ankan Bansal
Janit K Anjaria
Abhyuday Narayan Jagannatha
Abhishek Sharma
David Jacobs
Dilip Krishnan

A core component of the recent success of self-supervised learning is cropping data augmentation, which selects sub-regions of an image to be used as positive views in the self-supervised loss. The underlying assumption is that randomly cropped and resized regions of a given image share information about the objects of interest, which is captured by the learned representation. This assumption is mostly satisfied in datasets such as ImageNet where there is a large, centered object, which is highly likely to be present in random crops of the full image. However, in other datasets such as OpenImages or COCO, which are more representative of real world uncurated data, there are typically multiple small objects in an image. In this work, we show that self-supervised learning based on the usual random cropping performs poorly on such datasets (measured by the difference from fully-supervised learning). Instead of using pairs of random crops, we propose to leverage an unsupervised object proposal technique; the first view is a crop obtained from this algorithm, and the second view is a dilated version of the first view. This encourages the self-supervised model to learn both object and scene level semantic representations. Using this approach, which we call object-aware cropping, results in significant improvements over random scene cropping on classification and object detection benchmarks. For example, for pre-training on OpenImages, our approach achieves an improvement of 8.8% mAP over random scene cropping (both meth- ods using MoCo-v2). We also show significant improvements on COCO and PASCAL-VOC object detection and segmentation tasks over the state-of-the-art self-supervised learning approaches. Our approach is efficient, simple and general, and can be used in most existing contrastive and non-contrastive self-supervised learning frameworks.

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