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Found 16 papers, 11 papers with code
Iterative Multi-granular Image Editing using Diffusion Models
We hope our work would attract attention to this newly identified, pragmatic problem setting.
CoPL: Contextual Prompt Learning for Vision-Language Understanding
Our key innovations over earlier works include using local image features as part of the prompt learning process, and more crucially, learning to weight these prompts based on local features that are appropriate for the task at hand.
A-STAR: Test-time Attention Segregation and Retention for Text-to-image Synthesis
First, our attention segregation loss reduces the cross-attention overlap between attention maps of different concepts in the text prompt, thereby reducing the confusion/conflict among various concepts and the eventual capture of all concepts in the generated output.
$Δ$-Patching: A Framework for Rapid Adaptation of Pre-trained Convolutional Networks without Base Performance Loss
Models pre-trained on large-scale datasets are often fine-tuned to support newer tasks and datasets that arrive over time.
Data-Free Class-Incremental Hand Gesture Recognition
Our sampling scheme outperforms SOTA methods significantly on two 3D skeleton gesture datasets, the publicly available SHREC 2017, and EgoGesture3D -- which we extract from a publicly available RGBD dataset.
Class-Incremental Learning with Cross-Space Clustering and Controlled Transfer
This allows the model to learn classes in such a way that it maximizes positive forward transfer from similar prior classes, thus increasing plasticity, and minimizes negative backward transfer on dissimilar prior classes, whereby strengthening stability.
D3Former: Debiased Dual Distilled Transformer for Incremental Learning
In contrast to a recent ViT based CIL approach, our $\textrm{D}^3\textrm{Former}$ does not dynamically expand its architecture when new tasks are learned and remains suitable for a large number of incremental tasks.
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.
Energy-based Latent Aligner for Incremental Learning
Deep learning models tend to forget their earlier knowledge while incrementally learning new tasks.
OW-DETR: Open-world Detection Transformer
In the case of incremental object detection, OW-DETR outperforms the state-of-the-art for all settings on PASCAL VOC.
Towards Open World Object Detection
Humans have a natural instinct to identify unknown object instances in their environments.
Meta-Consolidation for Continual Learning
The ability to continuously learn and adapt itself to new tasks, without losing grasp of already acquired knowledge is a hallmark of biological learning systems, which current deep learning systems fall short of.
Incremental Object Detection via Meta-Learning
In a real-world setting, object instances from new classes can be continuously encountered by object detectors.
Submodular Batch Selection for Training Deep Neural Networks
Mini-batch gradient descent based methods are the de facto algorithms for training neural network architectures today.
MASON: A Model AgnoStic ObjectNess Framework
This paper proposes a simple, yet very effective method to localize dominant foreground objects in an image, to pixel-level precision.
