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Found 15 papers, 5 papers with code
Neural Field Classifiers via Target Encoding and Classification Loss
We successfully propose a novel Neural Field Classifier (NFC) framework which formulates existing neural field methods as classification tasks rather than regression tasks.
ViGoR: Improving Visual Grounding of Large Vision Language Models with Fine-Grained Reward Modeling
By combining natural language understanding and the generation capabilities and breadth of knowledge of large language models with image perception, recent large vision language models (LVLMs) have shown unprecedented reasoning capabilities in the real world.
AffordanceLLM: Grounding Affordance from Vision Language Models
Affordance grounding refers to the task of finding the area of an object with which one can interact.
On the Dynamics Under the Unhinged Loss and Beyond
In this paper, we introduce the unhinged loss, a concise loss function, that offers more mathematical opportunities to analyze the closed-form dynamics while requiring as few simplifications or assumptions as possible.
Visual Prompt Tuning for Test-time Domain Adaptation
First, DePT plugs visual prompts into the vision Transformer and only tunes these source-initialized prompts during adaptation.
Ranked #3 on
Domain Adaptation
on VisDA2017
Prototype-Anchored Learning for Learning with Imperfect Annotations
We verify the effectiveness of PAL on class-imbalanced learning and noise-tolerant learning by extensive experiments on synthetic and real-world datasets.
Learning Towards the Largest Margins
One of the main challenges for feature representation in deep learning-based classification is the design of appropriate loss functions that exhibit strong discriminative power.
ReSmooth: Detecting and Utilizing OOD Samples when Training with Data Augmentation
Further, we incorporate our ReSmooth framework with negative data augmentation strategies.
Learning with Noisy Labels via Sparse Regularization
In this paper, we theoretically prove that \textbf{any loss can be made robust to noisy labels} by restricting the network output to the set of permutations over a fixed vector.
Asymmetric Loss Functions for Learning with Noisy Labels
Symmetric loss functions are confirmed to be robust to label noise.
Exploiting Invariance in Training Deep Neural Networks
Inspired by two basic mechanisms in animal visual systems, we introduce a feature transform technique that imposes invariance properties in the training of deep neural networks.
Advances in Deep Learning for Hyperspectral Image Analysis--Addressing Challenges Arising in Practical Imaging Scenarios
Deep neural networks have proven to be very effective for computer vision tasks, such as image classification, object detection, and semantic segmentation -- these are primarily applied to color imagery and video.
Out-of-the-box channel pruned networks
We then demonstrate that if we found the profiles using a mid-sized dataset such as Cifar10/100, we are able to transfer them to even a large dataset such as Imagenet.
FineText: Text Classification via Attention-based Language Model Fine-tuning
Training deep neural networks from scratch on natural language processing (NLP) tasks requires significant amount of manually labeled text corpus and substantial time to converge, which usually cannot be satisfied by the customers.
$d$-SNE: Domain Adaptation using Stochastic Neighborhood Embedding
Deep neural networks often require copious amount of labeled-data to train their scads of parameters.
