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Found 11 papers, 2 papers with code
CORN: Co-Reasoning Network for Commonsense Question Answering
In this paper, we propose a novel model, Co-Reasoning Network (CORN), which adopts a bidirectional multi-level connection structure based on Co-Attention Transformer.
Query-Based Knowledge Sharing for Open-Vocabulary Multi-Label Classification
Identifying labels that did not appear during training, known as multi-label zero-shot learning, is a non-trivial task in computer vision.
Text as Image: Learning Transferable Adapter for Multi-Label Classification
Pre-trained vision-language models have notably accelerated progress of open-world concept recognition.
Beyond Visual Cues: Synchronously Exploring Target-Centric Semantics for Vision-Language Tracking
However, current VL trackers have not fully exploited the power of VL learning, as they suffer from limitations such as heavily relying on off-the-shelf backbones for feature extraction, ineffective VL fusion designs, and the absence of VL-related loss functions.
Causal-Story: Local Causal Attention Utilizing Parameter-Efficient Tuning For Visual Story Synthesis
The current state-of-the-art method combines the features of historical captions, historical frames, and the current captions as conditions for generating the current frame.
Multi-hop Commonsense Knowledge Injection Framework for Zero-Shot Commonsense Question Answering
Commonsense question answering (QA) research requires machines to answer questions based on commonsense knowledge.
No Place to Hide: Dual Deep Interaction Channel Network for Fake News Detection based on Data Augmentation
Different from popular methods which take full advantage of the propagation topology structure, in this paper, we propose a novel framework for fake news detection from perspectives of semantic, emotion and data enhancement, which excavates the emotional evolution patterns of news participants during the propagation process, and a dual deep interaction channel network of semantic and emotion is designed to obtain a more comprehensive and fine-grained news representation with the consideration of comments.
Scene-Aware Label Graph Learning for Multi-Label Image Classification
Multi-label image classification refers to assigning a set of labels for an image.
AlignVE: Visual Entailment Recognition Based on Alignment Relations
Such approaches, however, ignore the VE's unique nature of relation inference between the premise and hypothesis.
Two-Stream Transformer for Multi-Label Image Classification
Specifically, in each layer of TSFormer, a cross-modal attention module is developed to aggregate visual features from spatial stream into semantic stream and update label semantics via a residual connection.
A Comprehensive Survey and Taxonomy on Single Image Dehazing Based on Deep Learning
With the development of convolutional neural networks, hundreds of deep learning based dehazing methods have been proposed.
