38 citations · 48 across the 7 of their papers we have counts for
9 papers · 1 filter
CPL: Counterfactual Prompt Learning for Vision and Language Models
Xuehai He, Diji Yang, Weixi Feng +7
Prompt tuning is a new few-shot transfer learning technique that only tunes the learnable prompt for pre-trained vision and language models such as CLIP. However, existing prompt t…
Anticipating the Unseen Discrepancy for Vision and Language Navigation
Yujie Lu, Huiliang Zhang, Ping Nie +4
Vision-Language Navigation requires the agent to follow natural language instructions to reach a specific target. The large discrepancy between seen and unseen environments makes i…
Compositional Temporal Grounding with Structured Variational Cross-Graph Correspondence Learning
Juncheng Li, Junlin Xie, Long Qian +6
Temporal grounding in videos aims to localize one target video segment that semantically corresponds to a given query sentence. Thanks to the semantic diversity of natural language…
Are Gender-Neutral Queries Really Gender-Neutral? Mitigating Gender Bias in Image Search
Jialu Wang, Yang Liu, Xin Eric Wang
Internet search affects people's cognition of the world, so mitigating biases in search results and learning fair models is imperative for social good. We study a unique gender bia…
VALUE: A Multi-Task Benchmark for Video-and-Language Understanding Evaluation
Linjie Li, Jie Lei, Zhe Gan +12
Most existing video-and-language (VidL) research focuses on a single dataset, or multiple datasets of a single task. In reality, a truly useful VidL system is expected to be easily…
L2C: Describing Visual Differences Needs Semantic Understanding of Individuals
An Yan, Xin Eric Wang, Tsu-Jui Fu +1
Recent advances in language and vision push forward the research of captioning a single image to describing visual differences between image pairs. Suppose there are two images, I_…