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20242026
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cs.CV2026

The Gate Always Closes: On Injecting Auxiliary Signals into Frozen Vision-Language Models

Moshiur Farazi, Sameera Ramasinghe, Bekir Sait Ciftler +2

Auxiliary signal pathways in VLMs are routinely fitted with learnable gates so the optimiser can decide how much of the signal to admit. We find that the optimiser almost always de…

cs.CV2026

HyperVis: Continuous Latent Visual Relational Graphs on the Lorentz Hyperboloid for Compositional Reasoning

Moshiur Farazi, Sameera Ramasinghe, Mahbub Ahmed Turza +1

Vision-Language Models (VLMs) struggle with compositional reasoning that requires understanding inter-object relationships. A natural remedy is to inject explicit scene graph tripl…

cs.CV2026

From Activation to Initialization: Scaling Insights for Optimizing Neural Fields

Hemanth Saratchandran, Sameera Ramasinghe, Simon Lucey

In the realm of computer vision, Neural Fields have gained prominence as a contemporary tool harnessing neural networks for signal representation. Despite the remarkable progress i…

cs.CV2026

Trading Positional Complexity vs. Deepness in Coordinate Networks

Jianqiao Zheng, Sameera Ramasinghe, Xueqian Li +1

It is well noted that coordinate-based MLPs benefit -- in terms of preserving high-frequency information -- through the encoding of coordinate positions as an array of Fourier feat…

cs.CV2026

DARB-Splatting: Generalizing Splatting with Decaying Anisotropic Radial Basis Functions

Hashiru Pramuditha, Vinasirajan Viruthshaan, Vishagar Arunan +4

Splatting-based 3D reconstruction methods have gained popularity with the advent of 3D Gaussian Splatting, efficiently synthesizing high-quality novel views. These methods commonly…

cs.CV2026

Learning Visual Hierarchies in Hyperbolic Space for Image Retrieval

Ziwei Wang, Sameera Ramasinghe, Chenchen Xu +3

Structuring latent representations in a hierarchical manner enables models to learn patterns at multiple levels of abstraction. However, most prevalent image understanding models f…