3 citations · 7 across the 6 of their papers we have counts for
6 papers
Multimodal Unlearning Across Vision, Language, Video, and Audio: Survey of Methods, Datasets, and Benchmarks
Nobin Sarwar, Shubhashis Roy Dipta, Zheyuan Liu +1
With the growing adoption of VLMs, DMs, LLMs, and AFMs, these multimodal foundation models can inadvertently encode sensitive, copyrighted, biased, or unsafe cross-modal associatio…
WatchAct: A Benchmark for Behavior-Grounded Robot Manipulation
Baiqi Li, Ce Zhang, Yu Fang +4
A robot working alongside people must reason about what they have done, in what order, and with what intent. Video carries the spatial layouts, object histories, and gestures that…
TimeBlind: A Spatio-Temporal Compositionality Benchmark for Video LLMs
Baiqi Li, Kangyi Zhao, Ce Zhang +3
Fine-grained spatio-temporal understanding is essential for video reasoning and embodied AI. Yet, while Multimodal Large Language Models (MLLMs) master static semantics, their gras…
NaturalBench: Evaluating Vision-Language Models on Natural Adversarial Samples
Baiqi Li, Zhiqiu Lin, Wenxuan Peng +7
Vision-language models (VLMs) have made significant progress in recent visual-question-answering (VQA) benchmarks that evaluate complex visio-linguistic reasoning. However, are the…
GenAI-Bench: Evaluating and Improving Compositional Text-to-Visual Generation
Baiqi Li, Zhiqiu Lin, Deepak Pathak +8
While text-to-visual models now produce photo-realistic images and videos, they struggle with compositional text prompts involving attributes, relationships, and higher-order reaso…
Evaluating Text-to-Visual Generation with Image-to-Text Generation
Zhiqiu Lin, Deepak Pathak, Baiqi Li +5
Despite significant progress in generative AI, comprehensive evaluation remains challenging because of the lack of effective metrics and standardized benchmarks. For instance, the…