Useful resource-constrained picture era and visible understanding: an interview with Aniket Roy

Within the newest in our collection of interviews assembly the AAAI/SIGAI Doctoral Consortium individuals, we caught up with Aniket Roy to seek out out extra about his analysis on generative fashions for laptop imaginative and prescient duties.

Inform us a bit about your PhD – the place did you examine, and what was the subject of your analysis?

I lately accomplished my PhD in Pc Science at Johns Hopkins College, the place I labored underneath the supervision of Bloomberg Distinguished Professor Rama Chellappa. My analysis primarily centered on creating strategies for resource-constrained picture era and visible understanding. Specifically, I explored how trendy generative fashions might be tailored to function effectively whereas sustaining sturdy efficiency.

Throughout my PhD, I labored broadly on the intersection of generative AI, multimodal studying, and few-shot studying. A lot of my work concerned designing strategies that allow fashions to be taught new ideas or carry out advanced visible duties with restricted knowledge or computational sources. This included analysis on diffusion fashions, personalised picture era, and multimodal illustration studying. Total, my work goals to make superior imaginative and prescient and generative AI techniques extra adaptable, environment friendly, and sensible for real-world functions.

Might you give us an outline of the analysis you carried out throughout your PhD?

Throughout my PhD, my analysis broadly centered on bettering the adaptability, effectivity, and high quality of recent generative fashions for laptop imaginative and prescient duties. The speedy progress in generative AI–notably diffusion fashions and imaginative and prescient–language fashions–has created new alternatives to deal with long-standing challenges equivalent to knowledge shortage, controllable era, and personalised picture synthesis. My work aimed to develop strategies that enable these massive fashions to adapt successfully with restricted knowledge and computational sources whereas sustaining excessive visible constancy.

One line of my analysis addressed studying in data-constrained settings. For instance, I proposed FeLMi, a few-shot studying framework that leverages uncertainty-guided arduous mixup methods to enhance robustness and generalization when solely a small variety of labeled samples can be found. Constructing on this concept of bettering coaching knowledge high quality, I additionally developed Cap2Aug, which introduces caption-guided multimodal augmentation. This method makes use of textual descriptions to information artificial picture era, bettering visible range whereas decreasing the area hole between actual and generated knowledge.

Overview of Cap2Aug.

One other facet of my analysis centered on bettering the perceptual high quality of photos generated by diffusion fashions. On this course, I proposed DiffNat, a plug-and-play regularization methodology primarily based on the kurtosis-concentration property noticed in pure photos. By incorporating this precept into diffusion fashions by way of a KC loss, the generated photos exhibit extra pure texture statistics and improved perceptual realism, which additionally advantages downstream imaginative and prescient duties.

A serious a part of my work explored personalization and environment friendly adaptation of enormous generative fashions. I launched DuoLoRA, a parameter-efficient framework for composing low-rank adapters that permits fine-grained management over content material and elegance with out requiring full retraining of the bottom mannequin. I additional prolonged personalization to zero-shot settings utilizing a training-free textual inversion method that enables arbitrary objects to be personalized immediately throughout era. Lastly, I proposed MultiLFG, a frequency-guided multi-LoRA composition framework that makes use of wavelet-domain representations and timestep-aware weighting to allow correct and training-free fusion of a number of ideas in diffusion fashions.

Overview of DuoLoRA.

Total, my analysis contributes towards constructing generative techniques which are extra environment friendly, adaptable, and controllable, enabling high-quality picture era and understanding even in data-limited or resource-constrained situations.

Was there a particular undertaking or a facet of your analysis that was notably fascinating?

One undertaking that I discovered notably fascinating throughout my PhD is DiffNat, which was printed in TMLR 2025. Diffusion fashions have develop into the spine of many trendy generative AI techniques and have achieved spectacular ends in producing and modifying sensible photos. Nevertheless, bettering the perceptual high quality and naturalness of generated photos stays an essential problem.

Overview of DiffNat.

On this work, we launched a easy however efficient regularization method referred to as the kurtosis focus (KC) loss, which might be built-in into customary diffusion mannequin pipelines as a plug-and-play element. The thought was impressed by a statistical property of pure photos: when a picture is decomposed into totally different band-pass filtered variations–for instance utilizing the Discrete Wavelet Rework–the kurtosis values throughout these frequency bands are usually comparatively constant. In distinction, generated photos typically present massive discrepancies throughout these bands. Our methodology reduces the hole between the best and lowest kurtosis values throughout the frequency elements, encouraging the generated photos to observe extra pure picture statistics.

As well as, we launched a condition-agnostic perceptual steerage technique throughout inference that additional improves picture constancy with out requiring further coaching alerts. We evaluated the method throughout a number of various duties, together with personalised few-shot finetuning with textual content steerage, unconditional picture era, picture super-resolution, and blind face restoration. Throughout these duties, incorporating the KC loss and perceptual steerage constantly improved perceptual high quality, measured by way of metrics equivalent to FID and MUSIQ, in addition to by way of human analysis.

What I notably appreciated about this undertaking is that it connects classical picture statistics with trendy diffusion fashions. It reveals that comparatively easy statistical insights about pure photos can nonetheless play a strong function in bettering massive generative fashions.

What are your plans for constructing on the PhD – the place are you working now and what’s going to you be investigating subsequent?

Throughout my PhD, I found that I genuinely benefit from the strategy of analysis–particularly the second when an instinct or thought seems to work in observe. That strategy of exploring new concepts and pushing the boundaries of what we all know is one thing I discover very motivating.

To proceed pursuing this, I can be becoming a member of NEC Laboratories America as a Analysis Scientist. On this function, I hope to construct on my PhD work by creating new strategies for generative fashions and exploring how these fashions can work together with broader multimodal techniques. Specifically, I’m keen on advancing analysis on the intersection of generative fashions, imaginative and prescient–language–motion fashions, and embodied AI. Extra broadly, my purpose is to contribute to the event of clever techniques that may perceive, generate, and work together with the visible world extra successfully, whereas additionally persevering with to push ahead the scientific understanding of those fashions.

I’m keen on how you bought into the sphere. What impressed you to review laptop imaginative and prescient and machine studying?

My curiosity in laptop imaginative and prescient and machine studying began throughout my undergraduate research, after I took programs in sign processing and picture processing. I discovered these topics notably fascinating as a result of they allowed you to experiment with algorithms and instantly see their results on photos. That visible and intuitive facet made the sphere very partaking, and it helped me respect how mathematical ideas can immediately translate into significant visible outcomes.

On the similar time, I used to be additionally interested by how the human mind processes visible info—how we’re capable of acknowledge objects, perceive scenes, and interpret advanced visible alerts so effortlessly. That curiosity led me to wonder if we may design computational fashions that mimic facets of human notion and allow machines to know visible knowledge in an analogous means.

A serious affect throughout this time was my professor, Dr. Kuntal Ghosh, who inspired me to assume extra deeply about these issues and method them with a scientific mindset. His mentorship performed an essential function in shaping my curiosity in analysis. Since then, that curiosity about visible notion and clever techniques has continued to drive my work in laptop imaginative and prescient and machine studying.

What was your expertise of the Doctoral Consortium at AAAI?

Sadly, I used to be not capable of attend the AAAI Doctoral Consortium in particular person as a result of visa-related points. Nevertheless, a colleague kindly helped current my poster on my behalf throughout the occasion. Although I couldn’t be there bodily, I used to be very inspired by the response my work obtained. A number of researchers reached out to me after seeing the poster, and we had some very insightful discussions concerning the concepts and potential future instructions of the analysis. In that sense, I nonetheless discovered the expertise fairly rewarding. The Doctoral Consortium is a good platform for sharing early-stage concepts, receiving suggestions from the group, and connecting with different researchers engaged on associated issues. I appreciated the chance to interact with individuals who had been within the work, and people interactions helped spark new views and collaborations.

Might you inform us an fascinating (non-AI associated) truth about you?

Outdoors of analysis, I’m an enormous fan of music and stand-up comedy, and I actually take pleasure in touring at any time when I get the possibility. Exploring new locations, cultures, and views is one thing I discover refreshing—it’s an effective way to recharge and keep curious concerning the world past work. I additionally take pleasure in writing poetic satire occasionally, and I often carry out it. It’s a enjoyable artistic outlet that enables me to combine humor and storytelling, which is kind of totally different from the analytical nature of the analysis work I often do.

About Aniket Roy

Aniket is at the moment a Analysis Scientist at NEC Labs America. He obtained his PhD from the Pc Science dept at Johns Hopkins College underneath the steerage of Bloomberg Distinguished Professor Prof. Rama Chellappa. Previous to that, he did a Grasp’s from Indian Institute of Know-how Kharagpur. He was acknowledged with the Greatest Paper Award at IWDW 2016 and the Markose Thomas Memorial Award for the most effective analysis paper on the Grasp’s degree. Throughout PhD, he explored domains of few-shot studying, multimodal studying, diffusion fashions, LLMs, LoRA merging with publications in main venues equivalent to NeurIPS, ICCV, TMLR, WACV, CVPR and in addition 3 US patents filed. Throughout his PhD, he additionally gained industrial expertise by way of a number of internships in Amazon, Qualcomm, MERL, and SRI Worldwide. He was awarded as an Amazon Fellow (2023-24) at JHU and chosen to take part in ICCV’25 and AAAI’26 doctoral consortium.