19 citations · 56 across the 12 of their papers we have counts for
12 papers
Logic Diffusion for Knowledge Graph Reasoning
Xiaoying Xie, Biao Gong, Yiliang Lv +3
Most recent works focus on answering first order logical queries to explore the knowledge graph reasoning via multi-hop logic predictions. However, existing reasoning models are li…
Scanning Only Once: An End-to-end Framework for Fast Temporal Grounding in Long Videos
Yulin Pan, Xiangteng He, Biao Gong +4
Video temporal grounding aims to pinpoint a video segment that matches the query description. Despite the recent advance in short-form videos (\textit{e.g.}, in minutes), temporal…
Enhancing Unsupervised Audio Representation Learning via Adversarial Sample Generation
Yulin Pan, Xiangteng He, Biao Gong +2
Existing audio analysis methods generally first transform the audio stream to spectrogram, and then feed it into CNN for further analysis. A standard CNN recognizes specific visual…
ViM: Vision Middleware for Unified Downstream Transferring
Yutong Feng, Biao Gong, Jianwen Jiang +4
Foundation models are pre-trained on massive data and transferred to downstream tasks via fine-tuning. This work presents Vision Middleware (ViM), a new learning paradigm that targ…
Troika: Multi-Path Cross-Modal Traction for Compositional Zero-Shot Learning
Siteng Huang, Biao Gong, Yutong Feng +3
Recent compositional zero-shot learning (CZSL) methods adapt pre-trained vision-language models (VLMs) by constructing trainable prompts only for composed state-object pairs. Relyi…
Rethinking Efficient Tuning Methods from a Unified Perspective
Zeyinzi Jiang, Chaojie Mao, Ziyuan Huang +3
Parameter-efficient transfer learning (PETL) based on large-scale pre-trained foundation models has achieved great success in various downstream applications. Existing tuning metho…
UKnow: A Unified Knowledge Protocol with Multimodal Knowledge Graph Datasets for Reasoning and Vision-Language Pre-Training
Biao Gong, Shuai Tan, Yutong Feng +6
This work presents a unified knowledge protocol, called UKnow, which facilitates knowledge-based studies from the perspective of data. Particularly focusing on visual and linguisti…
VoP: Text-Video Co-operative Prompt Tuning for Cross-Modal Retrieval
Siteng Huang, Biao Gong, Yulin Pan +4
Many recent studies leverage the pre-trained CLIP for text-video cross-modal retrieval by tuning the backbone with additional heavy modules, which not only brings huge computationa…
MAR: Masked Autoencoders for Efficient Action Recognition
Zhiwu Qing, Shiwei Zhang, Ziyuan Huang +5
Standard approaches for video recognition usually operate on the full input videos, which is inefficient due to the widely present spatio-temporal redundancy in videos. Recent prog…
Video Similarity and Alignment Learning on Partial Video Copy Detection
Zhen Han, Xiangteng He, Mingqian Tang +1
Existing video copy detection methods generally measure video similarity based on spatial similarities between key frames, neglecting the latent similarity in temporal dimension, s…
HANet: Hierarchical Alignment Networks for Video-Text Retrieval
Peng Wu, Xiangteng He, Mingqian Tang +2
Video-text retrieval is an important yet challenging task in vision-language understanding, which aims to learn a joint embedding space where related video and text instances are c…
Self-supervised Video Retrieval Transformer Network
Xiangteng He, Yulin Pan, Mingqian Tang +1
Content-based video retrieval aims to find videos from a large video database that are similar to or even near-duplicate of a given query video. Video representation and similarity…