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Found 23 papers, 8 papers with code
Variational Connectionist Temporal Classification
However, CTC tends to output spiky distributions since it prefers to output blank symbol most of the time.
Learning Dynamic Tetrahedra for High-Quality Talking Head Synthesis
Recent works in implicit representations, such as Neural Radiance Fields (NeRF), have advanced the generation of realistic and animatable head avatars from video sequences.
M2-Encoder: Advancing Bilingual Image-Text Understanding by Large-scale Efficient Pretraining
Vision-language foundation models like CLIP have revolutionized the field of artificial intelligence.
Ranked #1 on
Zero-shot Image Retrieval
on Flickr30k-CN
(using extra training data)
Zero-Shot Cross-Modal Retrieval
Zero-shot Image Retrieval
+3
SkySense: A Multi-Modal Remote Sensing Foundation Model Towards Universal Interpretation for Earth Observation Imagery
Prior studies on Remote Sensing Foundation Model (RSFM) reveal immense potential towards a generic model for Earth Observation.
A computationally efficient semi-blind source separation based approach for nonlinear echo cancellation based on an element-wise iterative source steering
While the semi-blind source separation-based acoustic echo cancellation (SBSS-AEC) has received much research attention due to its promising performance during double-talk compared to the traditional adaptive algorithms, it suffers from system latency and nonlinear distortions.
Large Multimodal Model Compression via Efficient Pruning and Distillation at AntGroup
In our research, we constructed a dataset, the Multimodal Advertisement Audition Dataset (MAAD), from real-world scenarios within Alipay, and conducted experiments to validate the reliability of our proposed strategy.
LogicMP: A Neuro-symbolic Approach for Encoding First-order Logic Constraints
Integrating first-order logic constraints (FOLCs) with neural networks is a crucial but challenging problem since it involves modeling intricate correlations to satisfy the constraints.
The Multimodal Information Based Speech Processing (MISP) 2023 Challenge: Audio-Visual Target Speaker Extraction
This pioneering effort aims to set the first benchmark for the AVTSE task, offering fresh insights into enhancing the ac-curacy of back-end speech recognition systems through AVTSE in challenging and real acoustic environments.
Mapping EEG Signals to Visual Stimuli: A Deep Learning Approach to Match vs. Mismatch Classification
Existing approaches to modeling associations between visual stimuli and brain responses are facing difficulties in handling between-subject variance and model generalization.
Uncertainty-guided Learning for Improving Image Manipulation Detection
Image manipulation detection (IMD) is of vital importance as faking images and spreading misinformation can be malicious and harm our daily life.
Simultaneously Short- and Long-Term Temporal Modeling for Semi-Supervised Video Semantic Segmentation
In this work, we propose a novel feature enhancement network to simultaneously model short- and long-term temporal correlation.
Robust Manifold Nonnegative Tucker Factorization for Tensor Data Representation
Nonnegative Tucker Factorization (NTF) minimizes the euclidean distance or Kullback-Leibler divergence between the original data and its low-rank approximation which often suffers from grossly corruptions or outliers and the neglect of manifold structures of data.
SimAN: Exploring Self-Supervised Representation Learning of Scene Text via Similarity-Aware Normalization
Motivated by this common sense, we augment one image patch and use its neighboring patch as guidance to recover itself.
Hierarchical Memory Learning for Fine-Grained Scene Graph Generation
After the autonomous partition of coarse and fine predicates, the model is first trained on the coarse predicates and then learns the fine predicates.
Training Protocol Matters: Towards Accurate Scene Text Recognition via Training Protocol Searching
The development of scene text recognition (STR) in the era of deep learning has been mainly focused on novel architectures of STR models.
Training Object Detectors From Scratch: An Empirical Study in the Era of Vision Transformer
Instead of proposing a specific vision transformer based detector, in this work, our goal is to reveal the insights of training vision transformer based detectors from scratch.
CBNet: A Composite Backbone Network Architecture for Object Detection
With multi-scale testing, we push the current best single model result to a new record of 60. 1% box AP and 52. 3% mask AP without using extra training data.
Ranked #6 on
Object Detection
on COCO-O
(using extra training data)
MatchVIE: Exploiting Match Relevancy between Entities for Visual Information Extraction
Through key-value matching based on relevancy evaluation, the proposed MatchVIE can bypass the recognitions to various semantics, and simply focuses on the strong relevancy between entities.
LPSNet: A Lightweight Solution for Fast Panoptic Segmentation
Panoptic segmentation is a challenging task aiming to simultaneously segment objects (things) at instance level and background contents (stuff) at semantic level.
CMUA-Watermark: A Cross-Model Universal Adversarial Watermark for Combating Deepfakes
Then, we design a two-level perturbation fusion strategy to alleviate the conflict between the adversarial watermarks generated by different facial images and models.
Partial AUC optimization based deep speaker embeddings with class-center learning for text-independent speaker verification
We also propose a class-center based training trial construction method to improve the training efficiency, which is critical for the proposed loss function to be comparable to the identification loss in performance.
End-to-End Model for Speech Enhancement by Consistent Spectrogram Masking
The experiments comparing ourCSM based end-to-end model with other methods are conductedto confirm that the CSM accelerate the model training andhave significant improvements in speech quality.
Deep Speech 2: End-to-End Speech Recognition in English and Mandarin
We show that an end-to-end deep learning approach can be used to recognize either English or Mandarin Chinese speech--two vastly different languages.
