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Found 7 papers, 6 papers with code
Audio-Visual Generalized Zero-Shot Learning using Pre-Trained Large Multi-Modal Models
However, existing benchmarks predate the popularization of large multi-modal models, such as CLIP and CLAP.
Time-, Memory- and Parameter-Efficient Visual Adaptation
Here, we outperform a prior adaptor-based method which could only scale to a 1 billion parameter backbone, or fully-finetuning a smaller backbone, with the same GPU and less training time.
Video-adverb retrieval with compositional adverb-action embeddings
We propose a framework for video-to-adverb retrieval (and vice versa) that aligns video embeddings with their matching compositional adverb-action text embedding in a joint embedding space.
Text-to-feature diffusion for audio-visual few-shot learning
Training deep learning models for video classification from audio-visual data commonly requires immense amounts of labeled training data collected via a costly process.
PlanT: Explainable Planning Transformers via Object-Level Representations
Planning an optimal route in a complex environment requires efficient reasoning about the surrounding scene.
Ranked #6 on
CARLA longest6
on CARLA
Temporal and cross-modal attention for audio-visual zero-shot learning
We show that our proposed framework that ingests temporal features yields state-of-the-art performance on the \ucf, \vgg, and \activity benchmarks for (generalised) zero-shot learning.
Ranked #2 on
GZSL Video Classification
on UCF-GZSL(main)
Audio-visual Generalised Zero-shot Learning with Cross-modal Attention and Language
Focusing on the relatively underexplored task of audio-visual zero-shot learning, we propose to learn multi-modal representations from audio-visual data using cross-modal attention and exploit textual label embeddings for transferring knowledge from seen classes to unseen classes.
Ranked #1 on
ZSL Video Classification
on UCF-GZSL (cls)
