398 citations · 1.1k across the 54 of their papers we have counts for
62 papers
POP: Prompt Of Prompts for Continual Learning
Zhiyuan Hu, Jiancheng Lyu, Dashan Gao +1
Continual learning (CL) has attracted increasing attention in the recent past. It aims to mimic the human ability to learn new concepts without catastrophic forgetting. While exist…
Single-Stage Visual Relationship Learning using Conditional Queries
Alakh Desai, Tz-Ying Wu, Subarna Tripathi +1
Research in scene graph generation (SGG) usually considers two-stage models, that is, detecting a set of entities, followed by combining them and labeling all possible relationship…
ProTeCt: Prompt Tuning for Taxonomic Open Set Classification
Tz-Ying Wu, Chih-Hui Ho, Nuno Vasconcelos
Visual-language foundation models, like CLIP, learn generalized representations that enable zero-shot open-set classification. Few-shot adaptation methods, based on prompt tuning,…
ActorsNeRF: Animatable Few-shot Human Rendering with Generalizable NeRFs
Jiteng Mu, Shen Sang, Nuno Vasconcelos +1
While NeRF-based human representations have shown impressive novel view synthesis results, most methods still rely on a large number of images / views for training. In this work, w…
SViTT: Temporal Learning of Sparse Video-Text Transformers
Yi Li, Kyle Min, Subarna Tripathi +1
Do video-text transformers learn to model temporal relationships across frames? Despite their immense capacity and the abundance of multimodal training data, recent work has reveal…
Taxonomic Class Incremental Learning
Yuzhao Chen, Zonghuan Li, Zhiyuan Hu +1
The problem of continual learning has attracted rising attention in recent years. However, few works have questioned the commonly used learning setup, based on a task curriculum of…