Search Results for author:
Found 11 papers, 6 papers with code
Logic Diffusion for Knowledge Graph Reasoning
Most recent works focus on answering first order logical queries to explore the knowledge graph reasoning via multi-hop logic predictions.
Troika: Multi-Path Cross-Modal Traction for Compositional Zero-Shot Learning
Recent compositional zero-shot learning (CZSL) methods adapt pre-trained vision-language models (VLMs) by constructing trainable prompts only for composed state-object pairs.
Scanning Only Once: An End-to-end Framework for Fast Temporal Grounding in Long Videos
Video temporal grounding aims to pinpoint a video segment that matches the query description.
ViM: Vision Middleware for Unified Downstream Transferring
ViM consists of a zoo of lightweight plug-in modules, each of which is independently learned on a midstream dataset with a shared frozen backbone.
Rethinking Efficient Tuning Methods from a Unified Perspective
The U-Tuning framework can simultaneously encompass existing methods and derive new approaches for parameter-efficient transfer learning, which prove to achieve on-par or better performances on CIFAR-100 and FGVC datasets when compared with existing PETL methods.
UKnow: A Unified Knowledge Protocol for Common-Sense Reasoning and Vision-Language Pre-training
This work presents a unified knowledge protocol, called UKnow, which facilitates knowledge-based studies from the perspective of data.
VoP: Text-Video Co-operative Prompt Tuning for Cross-Modal Retrieval
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 computational burdens with much more parameters, but also leads to the knowledge forgetting from upstream models.
MAR: Masked Autoencoders for Efficient Action Recognition
Inspired by this, we propose propose Masked Action Recognition (MAR), which reduces the redundant computation by discarding a proportion of patches and operating only on a part of the videos.
Ranked #12 on
Action Recognition
on Something-Something V2
Video Similarity and Alignment Learning on Partial Video Copy Detection
To address the above issues, we propose the Video Similarity and Alignment Learning (VSAL) approach, which jointly models spatial similarity, temporal similarity and partial alignment.
HANet: Hierarchical Alignment Networks for Video-Text Retrieval
Based on these, we naturally construct hierarchical representations in the individual-local-global manner, where the individual level focuses on the alignment between frame and word, local level focuses on the alignment between video clip and textual context, and global level focuses on the alignment between the whole video and text.
Self-supervised Video Retrieval Transformer Network
In addition, most retrieval systems are based on frame-level features for video similarity searching, making it expensive both storage wise and search wise.
