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Aniket Thomas

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TMLR Journal 2026 Journal Article

GENIE: A Visual-Only Diffusion Framework for Task- Agnostic Image Transformation

Uddeshya Singh
Aniket Thomas
Aishwarya Agarwal
Srikrishna Karanam
Biplab Banerjee

Designing a unified vision model capable of handling diverse visual transformation tasks without task-specific modifications remains a significant challenge, particularly in scaling and generalizing beyond narrowly defined objectives. We propose GENIE, a novel ControlNet-Diffusion framework that performs task-based image generation solely through visual exemplars, eliminating dependence on textual prompts or auxiliary metadata. Unlike conventional prompt-driven diffusion models, GENIE employs a dual visual conditioning mechanism—combining implicit guidance via ControlNet and explicit task encoding through CLIP-based visual arithmetic—to infer task intent directly from reference input-output pairs. To improve semantic alignment between visual exemplars and generated outputs, we introduce a lightweight task consistency loss, which encourages representational coherence in the embedding space across transformed pairs. While not a multitask learner in the classical sense, GENIE enables task switching across multiple tasks without any task-specific modifications in architecture or task-specific loss functions. Evaluations across seven vision tasks—inpainting, colorization, edge detection, deblurring, denoising, semantic segmentation, and depth estimation—and two out-of-distribution (OOD) tasks—deraining and contrast enhancement—demonstrate that GENIE achieves an average performance gain of 10% over visual-conditioned baselines, showcasing its effectiveness for scalable and text-free visual generation.

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TMLR Journal 2025 Journal Article

ASTRA: A Scene-aware Transformer-based Model for Trajectory Prediction

Izzeddin Teeti
Aniket Thomas
Munish Monga
Sachin Kumar Giroh
Uddeshya Singh
Andrew Bradley
Biplab Banerjee
Fabio Cuzzolin

We present ASTRA (A Scene-aware Transformer-based model for trajectory prediction), a light-weight pedestrian trajectory forecasting model that integrates the scene context, spatial dynamics, social inter-agent interactions and temporal progressions for precise forecasting. We utilised a U-Net-based feature extractor, via its latent vector representation, to capture scene representations and a graph-aware transformer encoder for capturing social interactions. These components are integrated to learn an agent-scene aware embedding, enabling the model to learn spatial dynamics and forecast the future trajectory of pedestrians. The model is designed to produce both deterministic and stochastic outcomes, with the stochastic predictions being generated by incorporating a Conditional Variational Auto-Encoder (CVAE). ASTRA also proposes a simple yet effective weighted penalty loss function, which helps to yield predictions that outperform a wide array of state-of-the-art deterministic and generative models. ASTRA demonstrates an average improvement of 27%/10% in deterministic/stochastic settings on the ETH-UCY dataset, and 26% improvement on the PIE dataset, respectively, along with seven times fewer parameters than the existing state-of-the-art model (see Figure 1). Additionally, the model's versatility allows it to generalize across different perspectives, such as Bird's Eye View (BEV) and Ego-Vehicle View (EVV).

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