Search Results for author:
Found 3 papers, 1 papers with code
Detecting Multimodal Situations with Insufficient Context and Abstaining from Baseless Predictions
Despite the widespread adoption of Vision-Language Understanding (VLU) benchmarks such as VQA v2, OKVQA, A-OKVQA, GQA, VCR, SWAG, and VisualCOMET, our analysis reveals a pervasive issue affecting their integrity: these benchmarks contain samples where answers rely on assumptions unsupported by the provided context.
RAP: Retrieval-Augmented Planner for Adaptive Procedure Planning in Instructional Videos
(3) Annotation cost: Annotating instructional videos with step-level labels (i. e., timestamp) or sequence-level labels (i. e., action category) is demanding and labor-intensive, limiting its generalizability to large-scale datasets. In this work, we propose a new and practical setting, called adaptive procedure planning in instructional videos, where the procedure length is not fixed or pre-determined.
Weakly-Supervised Temporal Article Grounding
Specifically, given an article and a relevant video, WSAG aims to localize all ``groundable'' sentences to the video, and these sentences are possibly at different semantic scales.
