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cs.CV2026

Flash-BoN: Instant Drafts for Inference-Time Scaling in Diffusion Models

Ruchit Rawal, Reza Shirkavand, Sayak Paul +5

Inference-time scaling for text-to-image generation has progressed from simple Best-of- (BoN) sampling to guided search methods that verify and steer candidate trajectories at i…

cs.CV2026

ChannelTok: Efficient Flexible-Length Vision Tokenization

Sukriti Paul, Arpit Bansal, Tom Goldstein

Leading flexible vision tokenizers achieve SOTA quality at an extreme cost, relying on parameter-heavy backbones and slow, multi-step generative decoders. We depart from this compl…

cs.CV2026

TransFIRA: Transfer Learning for Face Image Recognizability Assessment

Allen Tu, Kartik Narayan, Joshua Gleason +4

Face recognition in unconstrained environments such as surveillance, video, and web imagery must contend with extreme variation in pose, blur, illumination, and occlusion, where co…

cs.CV2026

SplatSuRe: Selective Super-Resolution for Multi-view Consistent 3D Gaussian Splatting

Pranav Asthana, Alex Hanson, Allen Tu +3

3D Gaussian Splatting (3DGS) enables high-quality novel view synthesis, motivating interest in generating higher-resolution renders than those available during training. A natural…

cs.CV2025

Speedy-Splat: Fast 3D Gaussian Splatting with Sparse Pixels and Sparse Primitives

Alex Hanson, Allen Tu, Geng Lin +3

3D Gaussian Splatting (3D-GS) is a recent 3D scene reconstruction technique that enables real-time rendering of novel views by modeling scenes as parametric point clouds of differe…

cs.CV2025

ARGUS: Hallucination and Omission Evaluation in Video-LLMs

Ruchit Rawal, Reza Shirkavand, Heng Huang +2

Video large language models have not yet been widely deployed, largely due to their tendency to hallucinate. Typical benchmarks for Video-LLMs rely simply on multiple-choice questi…