33 citations · 71 across the 15 of their papers we have counts for
7 papers · 1 filter
TempCompass: Do Video LLMs Really Understand Videos?
Yuanxin Liu, Shicheng Li, Yi Liu +6
Recently, there is a surge in interest surrounding video large language models (Video LLMs). However, existing benchmarks fail to provide a comprehensive feedback on the temporal p…
FETV: A Benchmark for Fine-Grained Evaluation of Open-Domain Text-to-Video Generation
Yuanxin Liu, Lei Li, Shuhuai Ren +5
Recently, open-domain text-to-video (T2V) generation models have made remarkable progress. However, the promising results are mainly shown by the qualitative cases of generated vid…
VITATECS: A Diagnostic Dataset for Temporal Concept Understanding of Video-Language Models
Shicheng Li, Lei Li, Shuhuai Ren +5
The ability to perceive how objects change over time is a crucial ingredient in human intelligence. However, current benchmarks cannot faithfully reflect the temporal understanding…
Language Prior Is Not the Only Shortcut: A Benchmark for Shortcut Learning in VQA
Qingyi Si, Fandong Meng, Mingyu Zheng +6
Visual Question Answering (VQA) models are prone to learn the shortcut solution formed by dataset biases rather than the intended solution. To evaluate the VQA models' reasoning ab…
Towards Robust Visual Question Answering: Making the Most of Biased Samples via Contrastive Learning
Qingyi Si, Yuanxin Liu, Fandong Meng +5
Models for Visual Question Answering (VQA) often rely on the spurious correlations, i.e., the language priors, that appear in the biased samples of training set, which make them br…
Compressing And Debiasing Vision-Language Pre-Trained Models for Visual Question Answering
Qingyi Si, Yuanxin Liu, Zheng Lin +2
Despite the excellent performance of vision-language pre-trained models (VLPs) on conventional VQA task, they still suffer from two problems: First, VLPs tend to rely on language b…
Exploring and Distilling Cross-Modal Information for Image Captioning
Fenglin Liu, Xuancheng Ren, Yuanxin Liu +2
Recently, attention-based encoder-decoder models have been used extensively in image captioning. Yet there is still great difficulty for the current methods to achieve deep image u…