Search Results for author:
Found 17 papers, 13 papers with code
X-MIC: Cross-Modal Instance Conditioning for Egocentric Action Generalization
Lately, there has been growing interest in adapting vision-language models (VLMs) to image and third-person video classification due to their success in zero-shot recognition.
OrCo: Towards Better Generalization via Orthogonality and Contrast for Few-Shot Class-Incremental Learning
To address these challenges, we propose the OrCo framework built on two core principles: features' orthogonality in the representation space, and contrastive learning.
SSB: Simple but Strong Baseline for Boosting Performance of Open-Set Semi-Supervised Learning
In experiments, SSB greatly improves both inlier classification and outlier detection performance, outperforming existing methods by a large margin.
HowToCaption: Prompting LLMs to Transform Video Annotations at Scale
Specifically, we prompt an LLM to create plausible video descriptions based on ASR narrations of the video for a large-scale instructional video dataset.
In-Style: Bridging Text and Uncurated Videos with Style Transfer for Text-Video Retrieval
Large-scale noisy web image-text datasets have been proven to be efficient for learning robust vision-language models.
Temperature Schedules for Self-Supervised Contrastive Methods on Long-Tail Data
Such a schedule results in a constant `task switching' between an emphasis on instance discrimination and group-wise discrimination and thereby ensures that the model learns both group-wise features, as well as instance-specific details.
TAEC: Unsupervised Action Segmentation with Temporal-Aware Embedding and Clustering
Temporal action segmentation in untrimmed videos has gained increased attention recently.
Learning by Sorting: Self-supervised Learning with Group Ordering Constraints
Contrastive learning has become an important tool in learning representations from unlabeled data mainly relying on the idea of minimizing distance between positive data pairs, e. g., views from the same images, and maximizing distance between negative data pairs, e. g., views from different images.
Leveraging Self-Supervised Training for Unintentional Action Recognition
To enhance representations via self-supervised training for the task of unintentional action recognition we propose temporal transformations, called Temporal Transformations of Inherent Biases of Unintentional Actions (T2IBUA).
CycDA: Unsupervised Cycle Domain Adaptation from Image to Video
To address these challenges, we propose Cycle Domain Adaptation (CycDA), a cycle-based approach for unsupervised image-to-video domain adaptation by leveraging the joint spatial information in images and videos on the one hand and, on the other hand, training an independent spatio-temporal model to bridge the modality gap.
Revisiting Consistency Regularization for Semi-Supervised Learning
Generally, the aim is to train a model that is invariant to various data augmentations.
CoSSL: Co-Learning of Representation and Classifier for Imbalanced Semi-Supervised Learning
In this paper, we propose a novel co-learning framework (CoSSL) with decoupled representation learning and classifier learning for imbalanced SSL.
Generalized and Incremental Few-Shot Learning by Explicit Learning and Calibration without Forgetting
Both generalized and incremental few-shot learning have to deal with three major challenges: learning novel classes from only few samples per class, preventing catastrophic forgetting of base classes, and classifier calibration across novel and base classes.
Learning Interactions and Relationships between Movie Characters
Localizing the pair of interacting characters in video is a time-consuming process, instead, we train our model to learn from clip-level weak labels.
Joint Visual-Temporal Embedding for Unsupervised Learning of Actions in Untrimmed Sequences
Understanding the structure of complex activities in untrimmed videos is a challenging task in the area of action recognition.
Utilizing Temporal Information in Deep Convolutional Network for Efficient Soccer Ball Detection and Tracking
We first solve the detection task for an image using fully convolutional encoder-decoder architecture, and later, we use it as an input to our temporal models and jointly learn the detection task in sequences of images.
Unsupervised learning of action classes with continuous temporal embedding
The task of temporally detecting and segmenting actions in untrimmed videos has seen an increased attention recently.
