14 citations · 37 across the 10 of their papers we have counts for
10 papers
Towards Multimodal Video Paragraph Captioning Models Robust to Missing Modality
Sishuo Chen, Lei Li, Shuhuai Ren +5
Video paragraph captioning (VPC) involves generating detailed narratives for long videos, utilizing supportive modalities such as speech and event boundaries. However, the existing…
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…
Watch Out for Your Agents! Investigating Backdoor Threats to LLM-Based Agents
Wenkai Yang, Xiaohan Bi, Yankai Lin +3
Driven by the rapid development of Large Language Models (LLMs), LLM-based agents have been developed to handle various real-world applications, including finance, healthcare, and…
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…
RECALL: A Benchmark for LLMs Robustness against External Counterfactual Knowledge
Yi Liu, Lianzhe Huang, Shicheng Li +5
LLMs and AI chatbots have improved people's efficiency in various fields. However, the necessary knowledge for answering the question may be beyond the models' knowledge boundaries…
TESTA: Temporal-Spatial Token Aggregation for Long-form Video-Language Understanding
Shuhuai Ren, Sishuo Chen, Shicheng Li +2
Large-scale video-language pre-training has made remarkable strides in advancing video-language understanding tasks. However, the heavy computational burden of video encoding remai…
Communication Efficient Federated Learning for Multilingual Neural Machine Translation with Adapter
Yi Liu, Xiaohan Bi, Lei Li +3
Federated Multilingual Neural Machine Translation (Fed-MNMT) has emerged as a promising paradigm for institutions with limited language resources. This approach allows multiple ins…
Fine-Tuning Deteriorates General Textual Out-of-Distribution Detection by Distorting Task-Agnostic Features
Sishuo Chen, Wenkai Yang, Xiaohan Bi +1
Detecting out-of-distribution (OOD) inputs is crucial for the safe deployment of natural language processing (NLP) models. Though existing methods, especially those based on the st…
Expose Backdoors on the Way: A Feature-Based Efficient Defense against Textual Backdoor Attacks
Sishuo Chen, Wenkai Yang, Zhiyuan Zhang +2
Natural language processing (NLP) models are known to be vulnerable to backdoor attacks, which poses a newly arisen threat to NLP models. Prior online backdoor defense methods for…
Holistic Sentence Embeddings for Better Out-of-Distribution Detection
Sishuo Chen, Xiaohan Bi, Rundong Gao +1
Detecting out-of-distribution (OOD) instances is significant for the safe deployment of NLP models. Among recent textual OOD detection works based on pretrained language models (PL…