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Found 9 papers, 3 papers with code
Customizing Segmentation Foundation Model via Prompt Learning for Instance Segmentation
Recently, foundation models trained on massive datasets to adapt to a wide range of domains have attracted considerable attention and are actively being explored within the computer vision community.
Prompt Tuning of Deep Neural Networks for Speaker-adaptive Visual Speech Recognition
As it focuses on visual information to model the speech, its performance is inherently sensitive to personal lip appearances and movements, and this makes the VSR models show degraded performance when they are applied to unseen speakers.
HAZE-Net: High-Frequency Attentive Super-Resolved Gaze Estimation in Low-Resolution Face Images
Although gaze estimation methods have been developed with deep learning techniques, there has been no such approach as aim to attain accurate performance in low-resolution face images with a pixel width of 50 pixels or less.
Face Shape-Guided Deep Feature Alignment for Face Recognition Robust to Face Misalignment
This is mainly attributed to the mismatch between training and testing sets.
Weakly Paired Associative Learning for Sound and Image Representations via Bimodal Associative Memory
Existing sound and image representation learning methods necessarily require a large number of sound and image with corresponding pairs.
Entropy-Aware Model Initialization for Effective Exploration in Deep Reinforcement Learning
Encouraging exploration is a critical issue in deep reinforcement learning.
Video Prediction Recalling Long-term Motion Context via Memory Alignment Learning
Our work addresses long-term motion context issues for predicting future frames.
Ranked #1 on
Video Prediction
on KTH
(Cond metric)
Diverse Temporal Aggregation and Depthwise Spatiotemporal Factorization for Efficient Video Classification
By using the proposed temporal modeling method (T-OSA), and the efficient factorized component (D(2+1)D), we construct two types of VoV3D networks, VoV3D-M and VoV3D-L.
Ranked #29 on
Action Recognition
on Something-Something V1
(using extra training data)
Localization Uncertainty Estimation for Anchor-Free Object Detection
To tackle these limitations, we propose a new localization uncertainty estimation method called UAD for anchor-free object detection.
Ranked #116 on
Object Detection
on COCO test-dev
