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Found 16 papers, 14 papers with code
Vocabulary-free Image Classification and Semantic Segmentation
To address VIC, we propose Category Search from External Databases (CaSED), a training-free method that leverages a pre-trained vision-language model and an external database.
Continual Contrastive Spoken Language Understanding
In this paper, we investigate the problem of learning sequence-to-sequence models for spoken language understanding in a class-incremental learning (CIL) setting and we propose COCONUT, a CIL method that relies on the combination of experience replay and contrastive learning.
Semi-supervised learning made simple with self-supervised clustering
Self-supervised learning models have been shown to learn rich visual representations without requiring human annotations.
Vocabulary-free Image Classification
We thus formalize a novel task, termed as Vocabulary-free Image Classification (VIC), where we aim to assign to an input image a class that resides in an unconstrained language-induced semantic space, without the prerequisite of a known vocabulary.
Improved baselines for vision-language pre-training
Indeed, we find that a simple CLIP baseline can also be improved substantially, up to a 25% relative improvement on downstream zero-shot tasks, by using well-known training techniques that are popular in other subfields.
StyLIP: Multi-Scale Style-Conditioned Prompt Learning for CLIP-based Domain Generalization
Our method focuses on a domain-agnostic prompt learning strategy, aiming to disentangle the visual style and content information embedded in CLIP's pre-trained vision encoder, enabling effortless adaptation to novel domains during inference.
A soft nearest-neighbor framework for continual semi-supervised learning
Despite significant advances, the performance of state-of-the-art continual learning approaches hinges on the unrealistic scenario of fully labeled data.
Multimodal Emotion Recognition with Modality-Pairwise Unsupervised Contrastive Loss
Our method, as being based on contrastive loss between pairwise modalities, is the first attempt in MER literature.
Cultural Vocal Bursts Intensity Prediction
Multimodal Emotion Recognition
Uncertainty-aware Contrastive Distillation for Incremental Semantic Segmentation
This problem has been widely investigated in the research community and several Incremental Learning (IL) approaches have been proposed in the past years.
Continual Attentive Fusion for Incremental Learning in Semantic Segmentation
To fill this gap, in this paper we introduce a novel attentive feature distillation approach to mitigate catastrophic forgetting while accounting for semantic spatial- and channel-level dependencies.
Self-Supervised Models are Continual Learners
Self-supervised models have been shown to produce comparable or better visual representations than their supervised counterparts when trained offline on unlabeled data at scale.
A Unified Objective for Novel Class Discovery
In this paper, we study the problem of Novel Class Discovery (NCD).
Ranked #3 on
Novel Object Detection
on LVIS v1.0 val
Solo-learn: A Library of Self-supervised Methods for Visual Representation Learning
This paper presents solo-learn, a library of self-supervised methods for visual representation learning.
Neighborhood Contrastive Learning for Novel Class Discovery
In this paper, we address Novel Class Discovery (NCD), the task of unveiling new classes in a set of unlabeled samples given a labeled dataset with known classes.
Online Continual Learning under Extreme Memory Constraints
Continual Learning (CL) aims to develop agents emulating the human ability to sequentially learn new tasks while being able to retain knowledge obtained from past experiences.
Supervised online diarization with sample mean loss for multi-domain data
Recently, a fully supervised speaker diarization approach was proposed (UIS-RNN) which models speakers using multiple instances of a parameter-sharing recurrent neural network.
Ranked #1 on
Speaker Diarization
on DIHARD II
