5 papers
DRIFT: Detecting Representational Inconsistencies for Factual Truthfulness
Rohan Bhatnagar, Youran Sun, Chi Andrew Zhang +2
LLMs often produce fluent but incorrect answers, yet detecting such hallucinations typically requires multiple sampling passes or post-hoc verification, adding significant latency…
Knowledge Completes the Vision: A Multimodal Entity-aware Retrieval-Augmented Generation Framework for News Image Captioning
Xiaoxing You, Qiang Huang, Lingyu Li +4
News image captioning aims to produce journalistically informative descriptions by combining visual content with contextual cues from associated articles. Despite recent advances,…
Video-Bench: Human-Aligned Video Generation Benchmark
Hui Han, Siyuan Li, Jiaqi Chen +10
Video generation assessment is essential for ensuring that generative models produce visually realistic, high-quality videos while aligning with human expectations. Current video g…
Project-Probe-Aggregate: Efficient Fine-Tuning for Group Robustness
Beier Zhu, Jiequan Cui, Hanwang Zhang +1
While image-text foundation models have succeeded across diverse downstream tasks, they still face challenges in the presence of spurious correlations between the input and label.…
LoRA of Change: Learning to Generate LoRA for the Editing Instruction from A Single Before-After Image Pair
Xue Song, Jiequan Cui, Hanwang Zhang +4
In this paper, we propose the LoRA of Change (LoC) framework for image editing with visual instructions, i.e., before-after image pairs. Compared to the ambiguities, insufficient s…