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20242026
most citedBe like a Goldfish, Don't Memorize! Mitigating Memorization in Generative LLMs

4 citations · 8 across the 7 of their papers we have counts for

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cs.CV2026

LoGo: Local-Global Rewards for Consistent Long-Horizon Video Generation

Ziqi Ma, Shreya Sharma, Mohamed El Banani +8

Camera-controlled video models are rapidly advancing toward long generation horizons and complex camera control. A key failure mode is 3D inconsistency: as the camera moves, object…

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.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…

cs.CV2024★ 1 cited

From Pixels to Prose: A Large Dataset of Dense Image Captions

Vasu Singla, Kaiyu Yue, Sukriti Paul +7

Training large vision-language models requires extensive, high-quality image-text pairs. Existing web-scraped datasets, however, are noisy and lack detailed image descriptions. To…

cs.CV2024★ 1 cited

Measuring Style Similarity in Diffusion Models

Gowthami Somepalli, Anubhav Gupta, Kamal Gupta +5

Generative models are now widely used by graphic designers and artists. Prior works have shown that these models remember and often replicate content from their training data durin…