36 citations · 96 across the 16 of their papers we have counts for
16 papers
OmniTabBench: Mapping the Empirical Frontiers of GBDTs, Neural Networks, and Foundation Models for Tabular Data at Scale
Dihong Jiang, Ruoqi Cao, Zhiyuan Dang +8
While traditional tree-based ensemble methods have long dominated tabular tasks, deep neural networks and emerging foundation models have challenged this primacy, yet no consensus…
A Survey of Generative Techniques for Spatial-Temporal Data Mining
Qianru Zhang, Haixin Wang, Cheng Long +8
This paper focuses on the integration of generative techniques into spatial-temporal data mining, considering the significant growth and diverse nature of spatial-temporal data. Wi…
When Parameter-efficient Tuning Meets General-purpose Vision-language Models
Yihang Zhai, Haixin Wang, Jianlong Chang +4
Instruction tuning has shown promising potential for developing general-purpose AI capabilities by using large-scale pre-trained models and boosts growing research to integrate mul…
Prune Spatio-temporal Tokens by Semantic-aware Temporal Accumulation
Shuangrui Ding, Peisen Zhao, Xiaopeng Zhang +3
Transformers have become the primary backbone of the computer vision community due to their impressive performance. However, the unfriendly computation cost impedes their potential…
Towards AGI in Computer Vision: Lessons Learned from GPT and Large Language Models
Lingxi Xie, Longhui Wei, Xiaopeng Zhang +4
The AI community has been pursuing algorithms known as artificial general intelligence (AGI) that apply to any kind of real-world problem. Recently, chat systems powered by large l…
Parameter-efficient Tuning of Large-scale Multimodal Foundation Model
Haixin Wang, Xinlong Yang, Jianlong Chang +5
Driven by the progress of large-scale pre-training, parameter-efficient transfer learning has gained immense popularity across different subfields of Artificial Intelligence. The c…
Visual Tuning
Bruce X. B. Yu, Jianlong Chang, Haixin Wang +9
Fine-tuning visual models has been widely shown promising performance on many downstream visual tasks. With the surprising development of pre-trained visual foundation models, visu…
LION: Implicit Vision Prompt Tuning
Haixin Wang, Jianlong Chang, Xiao Luo +3
Despite recent competitive performance across a range of vision tasks, vision Transformers still have an issue of heavy computational costs. Recently, vision prompt learning has pr…
Constraint and Union for Partially-Supervised Temporal Sentence Grounding
Chen Ju, Haicheng Wang, Jinxiang Liu +5
Temporal sentence grounding aims to detect the event timestamps described by the natural language query from given untrimmed videos. The existing fully-supervised setting achieves…
Distilling Vision-Language Pre-training to Collaborate with Weakly-Supervised Temporal Action Localization
Chen Ju, Kunhao Zheng, Jinxiang Liu +5
Weakly-supervised temporal action localization (WTAL) learns to detect and classify action instances with only category labels. Most methods widely adopt the off-the-shelf Classifi…
Being Comes from Not-being: Open-vocabulary Text-to-Motion Generation with Wordless Training
Junfan Lin, Jianlong Chang, Lingbo Liu +4
Text-to-motion generation is an emerging and challenging problem, which aims to synthesize motion with the same semantics as the input text. However, due to the lack of diverse lab…
Towards a Unified View on Visual Parameter-Efficient Transfer Learning
Bruce X. B. Yu, Jianlong Chang, Lingbo Liu +2
Parameter efficient transfer learning (PETL) aims at making good use of the representation knowledge in the pre-trained large models by fine-tuning a small number of parameters. Re…
Pro-tuning: Unified Prompt Tuning for Vision Tasks
Xing Nie, Bolin Ni, Jianlong Chang +6
In computer vision, fine-tuning is the de-facto approach to leverage pre-trained vision models to perform downstream tasks. However, deploying it in practice is quite challenging,…
Prompt-Matched Semantic Segmentation
Lingbo Liu, Jianlong Chang, Bruce X. B. Yu +3
The objective of this work is to explore how to effectively and efficiently adapt pre-trained visual foundation models to various downstream tasks of semantic segmentation. Previou…
Fine-grained Retrieval Prompt Tuning
Shijie Wang, Jianlong Chang, Zhihui Wang +3
Fine-grained object retrieval aims to learn discriminative representation to retrieve visually similar objects. However, existing top-performing works usually impose pairwise simil…
HiVLP: Hierarchical Vision-Language Pre-Training for Fast Image-Text Retrieval
Feilong Chen, Xiuyi Chen, Jiaxin Shi +3
In the past few years, the emergence of vision-language pre-training (VLP) has brought cross-modal retrieval to a new era. However, due to the latency and computation demand, it is…