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Found 10 papers, 3 papers with code
Weak Distribution Detectors Lead to Stronger Generalizability of Vision-Language Prompt Tuning
The idea is realized by exploiting out-of-distribution (OOD) detection to predict whether a sample belongs to a base distribution or a novel distribution and then using the score generated by a dedicated competition based scoring function to fuse the zero-shot and few-shot classifier.
Compositional Kronecker Context Optimization for Vision-Language Models
Context Optimization (CoOp) has emerged as a simple yet effective technique for adapting CLIP-like vision-language models to downstream image recognition tasks.
Prompt Tuning with Soft Context Sharing for Vision-Language Models
Inspired by the fact that modeling task relationship by multi-task learning can usually boost performance, we propose a novel method SoftCPT (Soft Context Sharing for Prompt Tuning) to tune pre-trained vision-language models on multiple target few-shot tasks jointly.
Deep Spike Learning with Local Classifiers
We then propose two variants that additionally incorporate temporal dependencies through a backward and forward process, respectively.
Consensus Graph Representation Learning for Better Grounded Image Captioning
The contemporary visual captioning models frequently hallucinate objects that are not actually in a scene, due to the visual misclassification or over-reliance on priors that resulting in the semantic inconsistency between the visual information and the target lexical words.
Disentangled Motif-aware Graph Learning for Phrase Grounding
In this paper, we propose a novel graph learning framework for phrase grounding in the image.
Ranked #6 on
Phrase Grounding
on Flickr30k Entities Test
Synaptic Learning with Augmented Spikes
Traditional neuron models use analog values for information representation and computation, while all-or-nothing spikes are employed in the spiking ones.
Constructing Accurate and Efficient Deep Spiking Neural Networks with Double-threshold and Augmented Schemes
We examine the performance of our methods based on MNIST, Fashion-MNIST and CIFAR10 datasets.
Towards Efficient Processing and Learning with Spikes: New Approaches for Multi-Spike Learning
They are also believed to play an essential role in low-power consumption of the biological systems, whose efficiency attracts increasing attentions to the field of neuromorphic computing.
Robust Environmental Sound Recognition with Sparse Key-point Encoding and Efficient Multi-spike Learning
Our framework is a unifying system with a consistent integration of three major functional parts which are sparse encoding, efficient learning and robust readout.
