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Melissa Hall

12 accepted papers

2026

Inference-time Physics Alignment of Video Generative Models with Latent World Models

CVPR 2026

State-of-the-art video generative models produce promising visual content yet often violate basic physics principles, limiting their utility. While some attribute this deficiency to insufficient physics understanding from pre-training, we find that the shortfall in physics plausibility also stems fr

Cited by 0SourcecodeScholar
2026

Multimodal RewardBench 2: Evaluating Omni Reward Models for Interleaved Text and Image

CVPR 2026

Reward models (RMs) are essential for training large language models (LLMs), but remain underexplored for omni models that handle interleaved image and text sequences. We introduce Multimodal RewardBench 2 (MMRB2), the first comprehensive benchmark for reward models on multimodal understanding and (

Cited by 0SourcecodeScholar
2026

TV2TV: A Unified Framework for Interleaved Language and Video Generation

CVPR 2026

Video generation models are rapidly advancing, but can still struggle with complex video outputs that require significant semantic branching or repeated high-level reasoning about what should happen next. In this paper, we introduce a new class of omni video-text models that integrate ideas from rec

Cited by 0SourceScholar
2025

Boosting Latent Diffusion with Perceptual Objectives

ICLR 2025poster

Latent diffusion models (LDMs) power state-of-the-art high-resolution generative image models. LDMs learn the data distribution in the latent space of an autoencoder (AE) and produce images by mapping the generated latents into RGB image space using the AE decoder. While this approach allows for eff…

Cited by 0SourcePDFScholar
2025

DIMCIM: A Quantitative Evaluation Framework for Default-mode Diversity and Generalization in Text-to-Image Generative Models

ICCV 2025poster

Recent advances in text-to-image (T2I) models have achieved impressive quality and consistency. However, this has come at the cost of representation diversity. While automatic evaluation methods exist for benchmarking model diversity, they either require reference image datasets or lack specificity…

Cited by 0SourcePDFScholar
2025

Improving the Scaling Laws of Synthetic Data with Deliberate Practice

ICML 2025oral

Inspired by the principle of deliberate practice in human learning, we propose Deliberate Practice for Synthetic Data Generation (DP), a novel framework that improves sample efficiency through dynamic synthetic data generation. Prior work has shown that scaling synthetic data is inherently challengi…

Cited by 0SourcePDFScholar
2025

Increasing the Utility of Synthetic Images through Chamfer Guidance

NeurIPS 2025poster

Conditional image generative models hold considerable promise to produce infinite amounts of synthetic training data. Yet, recent progress in generation quality has come at the expense of generation diversity, limiting the utility of these models as a source of synthetic training data. Although gui…

Cited by 0SourceScholar
2024

On improved Conditioning Mechanisms and Pre-training Strategies for Diffusion Models

NeurIPS 2024poster

Large-scale training of latent diffusion models (LDMs) has enabled unprecedented quality in image generation. However, large-scale end-to-end training of these models is computationally costly, and hence most research focuses either on finetuning pretrained models or experiments at smaller scales…

Cited by 1SourcePDFScholar
2023

Exploring Why Object Recognition Performance Degrades Across Income Levels and Geographies with Factor Annotations

NeurIPS 2023spotlight

Despite impressive advances in object-recognition, deep learning systems’ performance degrades significantly across geographies and lower income levels---raising pressing concerns of inequity. Addressing such performance gaps remains a challenge, as little is understood about why performance degrade…

Cited by 4SourcePDFScholar
2023

FACET: Fairness in Computer Vision Evaluation Benchmark

ICCV 2023poster

Computer vision models have known performance disparities across attributes such as gender and skin tone. This means during tasks such as classification and detection, model performance differs for certain classes based on the demographics of the people in the image. These disparities have been show…

Cited by 46PDFScholar
2023

Quantifying and Mitigating the Impact of Label Errors on Model Disparity Metrics

ICLR 2023poster

Errors in labels obtained via human annotation adversely affect a trained model's performance. Existing approaches propose ways to mitigate the effect of label error on a model's downstream accuracy, yet little is known about its impact on a model's group-based disparity metrics\footnote{Group-based…

Cited by 10SourcePDFScholar
2022

“I’m sorry to hear that”: Finding New Biases in Language Models with a Holistic Descriptor Dataset

EMNLP 2022main

As language models grow in popularity, it becomes increasingly important to clearly measure all possible markers of demographic identity in order to avoid perpetuating existing societal harms. Many datasets for measuring bias currently exist, but they are restricted in their coverage of demographic…