33 citations · 71 across the 15 of their papers we have counts for
15 papers
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…
COST-EFF: Collaborative Optimization of Spatial and Temporal Efficiency with Slenderized Multi-exit Language Models
Bowen Shen, Zheng Lin, Yuanxin Liu +3
Transformer-based pre-trained language models (PLMs) mostly suffer from excessive overhead despite their advanced capacity. For resource-constrained devices, there is an urgent nee…
A Win-win Deal: Towards Sparse and Robust Pre-trained Language Models
Yuanxin Liu, Fandong Meng, Zheng Lin +5
Despite the remarkable success of pre-trained language models (PLMs), they still face two challenges: First, large-scale PLMs are inefficient in terms of memory footprint and compu…
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…
Learning to Win Lottery Tickets in BERT Transfer via Task-agnostic Mask Training
Yuanxin Liu, Fandong Meng, Zheng Lin +4
Recent studies on the lottery ticket hypothesis (LTH) show that pre-trained language models (PLMs) like BERT contain matching subnetworks that have similar transfer learning perfor…
Marginal Utility Diminishes: Exploring the Minimum Knowledge for BERT Knowledge Distillation
Yuanxin Liu, Fandong Meng, Zheng Lin +2
Recently, knowledge distillation (KD) has shown great success in BERT compression. Instead of only learning from the teacher's soft label as in conventional KD, researchers find th…
ROSITA: Refined BERT cOmpreSsion with InTegrAted techniques
Yuanxin Liu, Zheng Lin, Fengcheng Yuan
Pre-trained language models of the BERT family have defined the state-of-the-arts in a wide range of NLP tasks. However, the performance of BERT-based models is mainly driven by th…
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…
Unsupervised Pre-training for Natural Language Generation: A Literature Review
Yuanxin Liu, Zheng Lin
Recently, unsupervised pre-training is gaining increasing popularity in the realm of computational linguistics, thanks to its surprising success in advancing natural language under…
Aligning Visual Regions and Textual Concepts for Semantic-Grounded Image Representations
Fenglin Liu, Yuanxin Liu, Xuancheng Ren +2
In vision-and-language grounding problems, fine-grained representations of the image are considered to be of paramount importance. Most of the current systems incorporate visual fe…
simNet: Stepwise Image-Topic Merging Network for Generating Detailed and Comprehensive Image Captions
Fenglin Liu, Xuancheng Ren, Yuanxin Liu +2
The encode-decoder framework has shown recent success in image captioning. Visual attention, which is good at detailedness, and semantic attention, which is good at comprehensivene…