Search Results for author:
Found 14 papers, 6 papers with code
Unsolvable Problem Detection: Evaluating Trustworthiness of Vision Language Models
This paper introduces a novel and significant challenge for Vision Language Models (VLMs), termed Unsolvable Problem Detection (UPD).
Can Pre-trained Networks Detect Familiar Out-of-Distribution Data?
We consider that such data may significantly affect the performance of large pre-trained networks because the discriminability of these OOD data depends on the pre-training algorithm.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Open-Set Domain Adaptation with Visual-Language Foundation Models
Unsupervised domain adaptation (UDA) has proven to be very effective in transferring knowledge obtained from a source domain with labeled data to a target domain with unlabeled data.
LoCoOp: Few-Shot Out-of-Distribution Detection via Prompt Learning
CLIP's local features have a lot of ID-irrelevant nuisances (e. g., backgrounds), and by learning to push them away from the ID class text embeddings, we can remove the nuisances in the ID class text embeddings and enhance the separation between ID and OOD.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Zero-Shot In-Distribution Detection in Multi-Object Settings Using Vision-Language Foundation Models
First, images should be collected using only the name of the ID class without training on the ID data.
Listening Human Behavior: 3D Human Pose Estimation With Acoustic Signals
Aiming to capture subtle sound changes to reveal detailed pose information, we explicitly extract phase features from the acoustic signals together with typical spectrum features and feed them into our human pose estimation network.
Rethinking Rotation in Self-Supervised Contrastive Learning: Adaptive Positive or Negative Data Augmentation
The semantics of an image can be rotation-invariant or rotation-variant, so whether the rotated image is treated as positive or negative should be determined based on the content of the image.
Generalized Domain Adaptation
The key to our approach is self-supervised class-destructive learning, which enables the learning of class-invariant representations and domain-adversarial classifiers without using any domain labels.
Multi-Task Curriculum Framework for Open-Set Semi-Supervised Learning
Semi-supervised learning (SSL) has been proposed to leverage unlabeled data for training powerful models when only limited labeled data is available.
Computational Attention System for Children, Adults and Elderly
Observer's of different age-group have shown different scene viewing tendencies independent to the class of the image viewed.
Parallel Grid Pooling for Data Augmentation
Convolutional neural network (CNN) architectures utilize downsampling layers, which restrict the subsequent layers to learn spatially invariant features while reducing computational costs.
Alternating Co-Quantization for Cross-Modal Hashing
This paper addresses the problem of unsupervised learning of binary hash codes for efficient cross-modal retrieval.
Locally Linear Hashing for Extracting Non-Linear Manifolds
Previous efforts in hashing intend to preserve data variance or pairwise affinity, but neither is adequate in capturing the manifold structures hidden in most visual data.
A Bayesian Approach to Multimodal Visual Dictionary Learning
nary learning methods rely on image descriptors alone or together with class labels.
