Tejaswi Kasarla — accepted AI-conference papers

2026

OrthoRF: Exploring Orthogonality in Object-Centric Representations

Despoina Touska, Bastiaan Onne Fagginger Auer, Alexandru Onose, Tejaswi Kasarla, Luis Armando Pérez Rey, Maximilian Lipp +3

ICLR 2026poster

Neural synchrony is hypothesized to help the brain organize visual scenes into structured multi-object representations. In machine learning, synchrony-based models analogously learn object-centric representations by storing binding in the phase of complex-valued features. Rotating Features (RF) inst…

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2026

VL-JEPA: Joint Embedding Predictive Architecture for Vision-language

Delong Chen, Mustafa Shukor, Théo Moutakanni, Willy Chung, Lei Yu, Tejaswi Kasarla +3

ICLR 2026poster

We introduce VL-JEPA, a vision-language model built on a Joint Embedding Predictive Architecture (JEPA). Instead of autoregressively generating tokens as in classical VLMs, VL-JEPA predicts continuous embeddings of the target texts. By learning in an abstract representation space, the model can focu…

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2025

Hyperbolic Safety-Aware Vision-Language Models

Tobia Poppi, Tejaswi Kasarla, Pascal Mettes, Lorenzo Baraldi, Rita Cucchiara

CVPR 2025highlight

Addressing the retrieval of unsafe content from vision-language models such as CLIP is an important step towards real-world integration. Current efforts have relied on unlearning techniques that try to erase the model's knowledge of unsafe concepts. While effective in reducing unwanted outputs, unle…

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2022

Maximum Class Separation as Inductive Bias in One Matrix

Tejaswi Kasarla, Gertjan J. Burghouts, Max van Spengler, Elise van der Pol, Rita Cucchiara, Pascal Mettes

NeurIPS 2022accept

Maximizing the separation between classes constitutes a well-known inductive bias in machine learning and a pillar of many traditional algorithms. By default, deep networks are not equipped with this inductive bias and therefore many alternative solutions have been proposed through differential opti…

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