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Found 14 papers, 7 papers with code
Bridging Vision and Language Spaces with Assignment Prediction
This paper introduces VLAP, a novel approach that bridges pretrained vision models and large language models (LLMs) to make frozen LLMs understand the visual world.
Knowing Where to Focus: Event-aware Transformer for Video Grounding
Recent DETR-based video grounding models have made the model directly predict moment timestamps without any hand-crafted components, such as a pre-defined proposal or non-maximum suppression, by learning moment queries.
PartMix: Regularization Strategy to Learn Part Discovery for Visible-Infrared Person Re-identification
Modern data augmentation using a mixture-based technique can regularize the models from overfitting to the training data in various computer vision applications, but a proper data augmentation technique tailored for the part-based Visible-Infrared person Re-IDentification (VI-ReID) models remains unexplored.
Dual-path Adaptation from Image to Video Transformers
In this paper, we efficiently transfer the surpassing representation power of the vision foundation models, such as ViT and Swin, for video understanding with only a few trainable parameters.
Ranked #1 on
Action Classification
on Diving-48
SimOn: A Simple Framework for Online Temporal Action Localization
In addition, the evaluation for Online Detection of Action Start (ODAS) demonstrates the effectiveness and robustness of our method in the online setting.
Language-free Training for Zero-shot Video Grounding
Given an untrimmed video and a language query depicting a specific temporal moment in the video, video grounding aims to localize the time interval by understanding the text and video simultaneously.
PointFix: Learning to Fix Domain Bias for Robust Online Stereo Adaptation
To mitigate this issue, we propose to incorporate an auxiliary point-selective network into a meta-learning framework, called PointFix, to provide a robust initialization of stereo models for online stereo adaptation.
Probabilistic Representations for Video Contrastive Learning
This paper presents Probabilistic Video Contrastive Learning, a self-supervised representation learning method that bridges contrastive learning with probabilistic representation.
Pin the Memory: Learning to Generalize Semantic Segmentation
The rise of deep neural networks has led to several breakthroughs for semantic segmentation.
Self-balanced Learning For Domain Generalization
Domain generalization aims to learn a prediction model on multi-domain source data such that the model can generalize to a target domain with unknown statistics.
Bridge to Answer: Structure-aware Graph Interaction Network for Video Question Answering
As a result, our method can learn the question conditioned visual representations attributed to appearance and motion that show powerful capability for video question answering.
Cross-Domain Grouping and Alignment for Domain Adaptive Semantic Segmentation
Existing techniques to adapt semantic segmentation networks across the source and target domains within deep convolutional neural networks (CNNs) deal with all the samples from the two domains in a global or category-aware manner.
SumGraph: Video Summarization via Recursive Graph Modeling
The goal of video summarization is to select keyframes that are visually diverse and can represent a whole story of an input video.
Context-Aware Emotion Recognition Networks
We present deep networks for context-aware emotion recognition, called CAER-Net, that exploit not only human facial expression but also context information in a joint and boosting manner.
Ranked #1 on
Emotion Recognition in Context
on CAER-Dynamic
