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Found 40 papers, 14 papers with code
Sentiment Interpretable Logic Tensor Network for Aspect-Term Sentiment Analysis
SILTN is interpretable because it is a neurosymbolic formalism and a computational model that supports learning and reasoning about data with a differentiable first-order logic language (FOL).
Noise Learning for Text Classification: A Benchmark
However, we find that noise learning in text classification is relatively underdeveloped: 1. many methods that have been proven effective in the image domain are not explored in text classification, 2. it is difficult to conduct a fair comparison between previous studies as they do experiments in different noise settings.
Large Language Models as Zero-Shot Human Models for Human-Robot Interaction
In this work, we explore the potential of large-language models (LLMs) -- which have consumed vast amounts of human-generated text data -- to act as zero-shot human models for HRI.
Semantic Communications with Variable-Length Coding for Extended Reality
Wireless extended reality (XR) has attracted wide attentions as a promising technology to improve users' mobility and quality of experience.
Geometric Deep Learning for Autonomous Driving: Unlocking the Power of Graph Neural Networks With CommonRoad-Geometric
Heterogeneous graphs offer powerful data representations for traffic, given their ability to model the complex interaction effects among a varying number of traffic participants and the underlying road infrastructure.
STAIR: Learning Sparse Text and Image Representation in Grounded Tokens
We extend the CLIP model and build a sparse text and image representation (STAIR), where the image and text are mapped to a sparse token space.
Multimodal Video Adapter for Parameter Efficient Video Text Retrieval
State-of-the-art video-text retrieval (VTR) methods usually fully fine-tune the pre-trained model (e. g.
A separation logic for sequences in pointer programs and its decidability
To deal with properties on variable-length sequences and multilevel data structures, we propose sequence-heap separation logic which integrates sequences into logical reasoning on heap-manipulated programs.
How would Stance Detection Techniques Evolve after the Launch of ChatGPT?
ChatGPT has the potential to be the best AI model for stance detection tasks in NLP, or at least change the research paradigm of this field.
Semantic Sensing and Communications for Ultimate Extended Reality
In particular, semantic sensing is used to improve the sensing efficiency by exploring the spatial-temporal distributions of semantic information.
MetaPortrait: Identity-Preserving Talking Head Generation with Fast Personalized Adaptation
In this work, we propose an ID-preserving talking head generation framework, which advances previous methods in two aspects.
ZegCLIP: Towards Adapting CLIP for Zero-shot Semantic Segmentation
Recently, CLIP has been applied to pixel-level zero-shot learning tasks via a two-stage scheme.
Context-Matched Collage Generation for Underwater Invertebrate Detection
However, in many scenarios, it can be difficult to collect images for training, not to mention the costs associated with collecting annotations suitable for training these object detectors.
Safety-Constrained Policy Transfer with Successor Features
In this work, we focus on the problem of safe policy transfer in reinforcement learning: we seek to leverage existing policies when learning a new task with specified constraints.
SegViT: Semantic Segmentation with Plain Vision Transformers
We explore the capability of plain Vision Transformers (ViTs) for semantic segmentation and propose the SegVit.
Ranked #6 on
Semantic Segmentation
on PASCAL Context
Shape Completion with Points in the Shadow
The core challenge is to generate plausible geometries to fill the unobserved part of the object based on a partial scan, which is under-constrained and suffers from a huge solution space.
Context-Driven Detection of Invertebrate Species in Deep-Sea Video
Each video includes annotations indicating the start and end times of substrates across the video in addition to counts of species of interest.
CSI-fingerprinting Indoor Localization via Attention-Augmented Residual Convolutional Neural Network
On the other hand, considering the generality of a tracking system, we decouple the tracking system from the CSI environments so that one tracking system for all environments becomes possible.
Deep Learning-Based Perceptual Stimulus Encoder for Bionic Vision
Retinal implants have the potential to treat incurable blindness, yet the quality of the artificial vision they produce is still rudimentary.
StyleSwin: Transformer-based GAN for High-resolution Image Generation
To this end, we believe that local attention is crucial to strike the balance between computational efficiency and modeling capacity.
Ranked #1 on
Image Generation
on CelebA 256x256
(FID metric)
Margin Calibration for Long-Tailed Visual Recognition
The long-tailed class distribution in visual recognition tasks poses great challenges for neural networks on how to handle the biased predictions between head and tail classes, i. e., the model tends to classify tail classes as head classes.
Co-training Transformer with Videos and Images Improves Action Recognition
We term this approach as Co-training Videos and Images for Action Recognition (CoVeR).
Ranked #5 on
Action Classification
on Moments in Time
(using extra training data)
FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling
However, like other modern SSL algorithms, FixMatch uses a pre-defined constant threshold for all classes to select unlabeled data that contribute to the training, thus failing to consider different learning status and learning difficulties of different classes.
Visually Grounded Concept Composition
We investigate ways to compose complex concepts in texts from primitive ones while grounding them in images.
Systematic Generalization on gSCAN: What is Nearly Solved and What is Next?
We analyze the grounded SCAN (gSCAN) benchmark, which was recently proposed to study systematic generalization for grounded language understanding.
Dynamic Neural Representational Decoders for High-Resolution Semantic Segmentation
This neural representation enables our decoder to leverage the smoothness prior in the semantic label space, and thus makes our decoder more efficient.
Enhanced Hyperspectral Image Super-Resolution via RGB Fusion and TV-TV Minimization
Such methods, however, cannot guarantee that the input measurements are satisfied in the recovered image, since the learned parameters by the network are applied to every test image.
CREATe: Clinical Report Extraction and Annotation Technology
Clinical case reports are written descriptions of the unique aspects of a particular clinical case, playing an essential role in sharing clinical experiences about atypical disease phenotypes and new therapies.
Instance and Panoptic Segmentation Using Conditional Convolutions
In the literature, top-performing instance segmentation methods typically follow the paradigm of Mask R-CNN and rely on ROI operations (typically ROIAlign) to attend to each instance.
A Hierarchical Multi-Modal Encoder for Moment Localization in Video Corpus
Identifying a short segment in a long video that semantically matches a text query is a challenging task that has important application potentials in language-based video search, browsing, and navigation.
Solving Sparse Linear Inverse Problems in Communication Systems: A Deep Learning Approach With Adaptive Depth
Sparse signal recovery problems from noisy linear measurements appear in many areas of wireless communications.
Learning to Represent Image and Text with Denotation Graph
Recent progresses have leveraged the ideas of pre-training (from language modeling) and attention layers in Transformers to learn representation from datasets containing images aligned with linguistic expressions that describe the images.
Online Action Detection in Streaming Videos with Time Buffers
We formulate the problem of online temporal action detection in live streaming videos, acknowledging one important property of live streaming videos that there is normally a broadcast delay between the latest captured frame and the actual frame viewed by the audience.
Enhancing Cross-target Stance Detection with Transferable Semantic-Emotion Knowledge
Specifically, a semantic-emotion heterogeneous graph is constructed from external semantic and emotion lexicons, which is then fed into a graph convolutional network to learn multi-hop semantic connections between words and emotion tags.
Challenging the adversarial robustness of DNNs based on error-correcting output codes
The existence of adversarial examples and the easiness with which they can be generated raise several security concerns with regard to deep learning systems, pushing researchers to develop suitable defense mechanisms.
Visual Storytelling via Predicting Anchor Word Embeddings in the Stories
To narrate a sequence of images, we use the predicted anchor word embeddings and the image features as the joint input to a seq2seq model.
Attacking CNN-based anti-spoofing face authentication in the physical domain
In this paper, we study the vulnerability of anti-spoofing methods based on deep learning against adversarial perturbations.
Cryptography and Security
Cross-Modal and Hierarchical Modeling of Video and Text
Similarly, a paragraph may contain sentences with different topics, which collectively conveys a coherent message or story.
Few Shot Learning with Simplex
During testing, combined with the test sample and the points in the class, a new simplex is formed.
Real-time Action Recognition with Enhanced Motion Vector CNNs
The deep two-stream architecture exhibited excellent performance on video based action recognition.
Ranked #70 on
Action Recognition
on UCF101
