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Found 24 papers, 9 papers with code
Analyzing the Efficacy of an LLM-Only Approach for Image-based Document Question Answering
Recent document question answering models consist of two key components: the vision encoder, which captures layout and visual elements in images, and a Large Language Model (LLM) that helps contextualize questions to the image and supplements them with external world knowledge to generate accurate answers.
Is it an i or an l: Test-time Adaptation of Text Line Recognition Models
While text line recognition models are generally trained on large corpora of real and synthetic data, such models can still make frequent mistakes if the handwriting is inscrutable or the image acquisition process adds corruptions, such as noise, blur, compression, etc.
Weakly supervised information extraction from inscrutable handwritten document images
In this work, we focus on the complex problem of extracting medicine names from handwritten prescriptions using only weakly labeled data.
Novel Class Discovery without Forgetting
Inspired by this, we identify and formulate a new, pragmatic problem setting of NCDwF: Novel Class Discovery without Forgetting, which tasks a machine learning model to incrementally discover novel categories of instances from unlabeled data, while maintaining its performance on the previously seen categories.
Spacing Loss for Discovering Novel Categories
Novel Class Discovery (NCD) is a learning paradigm, where a machine learning model is tasked to semantically group instances from unlabeled data, by utilizing labeled instances from a disjoint set of classes.
Test-time Adaptation with Slot-Centric Models
In our work, we find evidence that these losses are insufficient for the task of scene decomposition, without also considering architectural inductive biases.
SITA: Single Image Test-time Adaptation
In Test-time Adaptation (TTA), given a source model, the goal is to adapt it to make better predictions for test instances from a different distribution than the source.
Unsupervised Adaptation of Semantic Segmentation Models without Source Data
Unsupervised domain adaptation aims to adapt a model learned on the labeled source data, to a new unlabeled target dataset.
Reconstruction guided Meta-learning for Few Shot Open Set Recognition
However, it is a critical task in many applications like environmental monitoring, where the number of labeled examples for each class is limited.
Cross-domain Imitation from Observations
Once this correspondence is found, we can directly transfer the demonstrations on one domain to the other and use it for imitation.
Unsupervised Multi-source Domain Adaptation Without Access to Source Data
A recent line of work addressed this problem and proposed an algorithm that transfers knowledge to the unlabeled target domain from a single source model without requiring access to the source data.
Adversarial Knowledge Transfer from Unlabeled Data
While machine learning approaches to visual recognition offer great promise, most of the existing methods rely heavily on the availability of large quantities of labeled training data.
Domain Adaptive Semantic Segmentation Using Weak Labels
In this work, we propose a novel framework for domain adaptation in semantic segmentation with image-level weak labels in the target domain.
Learning Person Re-identification Models from Videos with Weak Supervision
In order to cope with this issue, we introduce the problem of learning person re-identification models from videos with weak supervision.
Multiple Instance Learning
Video-Based Person Re-Identification
Learning from Trajectories via Subgoal Discovery
Learning to solve complex goal-oriented tasks with sparse terminal-only rewards often requires an enormous number of samples.
Context-Aware Query Selection for Active Learning in Event Recognition
We formulate a conditional random field model that encodes the context and devise an information-theoretic approach that utilizes entropy and mutual information of the nodes to compute the set of most informative queries, which are labeled by a human.
Weakly Supervised Video Moment Retrieval From Text Queries
The weak nature of the supervision is because, during training, we only have access to the video-text pairs rather than the temporal extent of the video to which different text descriptions relate.
Trajectory-based Learning for Ball-in-Maze Games
We show that in spite of not using human-generated trajectories and just using the simulator as a model to generate a limited number of trajectories, we can get a speed-up of about 2-3x in the learning process.
Incorporating Scalability in Unsupervised Spatio-Temporal Feature Learning
This necessitates learning of visual features from videos in an unsupervised setting.
W-TALC: Weakly-supervised Temporal Activity Localization and Classification
Most activity localization methods in the literature suffer from the burden of frame-wise annotation requirement.
Ranked #1 on
Action Classification
on ActivityNet-1.2
Adversarial Perturbations Against Real-Time Video Classification Systems
We exploit recent advances in generative adversarial network (GAN) architectures to account for temporal correlations and generate adversarial samples that can cause misclassification rates of over 80% for targeted activities.
Exploiting Transitivity for Learning Person Re-Identification Models on a Budget
Minimization of labeling effort for person re-identification in camera networks is an important problem as most of the existing popular methods are supervised and they require large amount of manual annotations, acquiring which is a tedious job.
The Impact of Typicality for Informative Representative Selection
In computer vision, selection of the most informative samples from a huge pool of training data in order to learn a good recognition model is an active research problem.
Non-Uniform Subset Selection for Active Learning in Structured Data
We construct a graph from the unlabeled data to represent the underlying structure, such that each node represents a data point, and edges represent the inter-relationships between them.
