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Found 28 papers, 14 papers with code
ChatRex: Taming Multimodal LLM for Joint Perception and Understanding
From the data perspective, we build a fully automated data engine and construct the Rexverse-2M dataset which possesses multiple granularities to support the joint training of perception and understanding.
DINO-X: A Unified Vision Model for Open-World Object Detection and Understanding
DINO-X employs the same Transformer-based encoder-decoder architecture as Grounding DINO 1. 5 to pursue an object-level representation for open-world object understanding.
SGLP: A Similarity Guided Fast Layer Partition Pruning for Compressing Large Deep Models
Based on similarity matrix derived from CKA, we employ Fisher Optimal Segmentation to partition the network into multiple segments, which provides a basis for removing the layers in a segment-wise manner.
Deep reinforcement learning for tracking a moving target in jellyfish-like swimming
This swimmer is a flexible object equipped with a muscle model based on torsional springs.
USTC-KXDIGIT System Description for ASVspoof5 Challenge
In Track 2, we continued using the CM system from Track 1 and fused it with a CNN-based ASV system.
An Effective Information Theoretic Framework for Channel Pruning
Channel pruning is a promising method for accelerating and compressing convolutional neural networks.
Grounding DINO 1.5: Advance the "Edge" of Open-Set Object Detection
Empirical results demonstrate the effectiveness of Grounding DINO 1. 5, with the Grounding DINO 1. 5 Pro model attaining a 54. 3 AP on the COCO detection benchmark and a 55. 7 AP on the LVIS-minival zero-shot transfer benchmark, setting new records for open-set object detection.
Ranked #1 on
Zero-Shot Object Detection
on MSCOCO
(using extra training data)
An Empirical Study of Challenges in Machine Learning Asset Management
In machine learning (ML), efficient asset management, including ML models, datasets, algorithms, and tools, is vital for resource optimization, consistent performance, and a streamlined development lifecycle.
ADCNet: a unified framework for predicting the activity of antibody-drug conjugates
Antibody-drug conjugate (ADC) has revolutionized the field of cancer treatment in the era of precision medicine due to their ability to precisely target cancer cells and release highly effective drug.
TinySAM: Pushing the Envelope for Efficient Segment Anything Model
Extensive experiments on various zero-shot transfer tasks demonstrate the significantly advantageous performance of our TinySAM against counterpart methods.
AcademicGPT: Empowering Academic Research
Our training corpus mainly consists of academic papers, thesis, content from some academic domain, high-quality Chinese data and others.
Generic and Robust Root Cause Localization for Multi-Dimensional Data in Online Service Systems
Such attribute combinations are substantial clues to the underlying root causes and thus are called root causes of multidimensional data.
LipsFormer: Introducing Lipschitz Continuity to Vision Transformers
In contrast to previous practical tricks that address training instability by learning rate warmup, layer normalization, attention formulation, and weight initialization, we show that Lipschitz continuity is a more essential property to ensure training stability.
DisCo-CLIP: A Distributed Contrastive Loss for Memory Efficient CLIP Training
In this way, we can reduce the GPU memory consumption of contrastive loss computation from $\bigO(B^2)$ to $\bigO(\frac{B^2}{N})$, where $B$ and $N$ are the batch size and the number of GPUs used for training.
Exploring Vision Transformers as Diffusion Learners
We further provide a hypothesis on the implication of disentangling the generative backbone as an encoder-decoder structure and show proof-of-concept experiments verifying the effectiveness of a stronger encoder for generative tasks with ASymmetriC ENcoder Decoder (ASCEND).
The SpeakIn Speaker Verification System for Far-Field Speaker Verification Challenge 2022
In the pre-training stage we reserve the speaker weights, and there are no positive samples to train them in this stage.
The SpeakIn System Description for CNSRC2022
In the SV task fixed track, our system was a fusion of five models, and two models were fused in the SV task open track.
VDDB: a comprehensive resource and machine learning platform for antiviral drug discovery
Virus infection is one of the major diseases that seriously threaten human health.
3D Shuffle-Mixer: An Efficient Context-Aware Vision Learner of Transformer-MLP Paradigm for Dense Prediction in Medical Volume
Therefore, designing an elegant and efficient vision transformer learner for dense prediction in medical volume is promising and challenging.
1st Place Solution for ICDAR 2021 Competition on Mathematical Formula Detection
In this technical report, we present our 1st place solution for the ICDAR 2021 competition on mathematical formula detection (MFD).
Winner Team Mia at TextVQA Challenge 2021: Vision-and-Language Representation Learning with Pre-trained Sequence-to-Sequence Model
TextVQA requires models to read and reason about text in images to answer questions about them.
PingAn-VCGroup's Solution for ICDAR 2021 Competition on Scientific Literature Parsing Task B: Table Recognition to HTML
In our method, we divide the table content recognition task into foursub-tasks: table structure recognition, text line detection, text line recognition, and box assignment. Our table structure recognition algorithm is customized based on MASTER [1], a robust image textrecognition algorithm.
Ranked #2 on
Table Recognition
on PubTabNet
PingAn-VCGroup's Solution for ICDAR 2021 Competition on Scientific Table Image Recognition to Latex
This paper presents our solution for the ICDAR 2021 Competition on Scientific Table Image Recognition to LaTeX.
Melody-Conditioned Lyrics Generation with SeqGANs
Automatic lyrics generation has received attention from both music and AI communities for years.
Learning Graph Normalization for Graph Neural Networks
We conduct extensive experiments on benchmark datasets for different tasks, including node classification, link prediction, graph classification and graph regression, and confirm that the learned graph normalization leads to competitive results and that the learned weights suggest the appropriate normalization techniques for the specific task.
Hamming OCR: A Locality Sensitive Hashing Neural Network for Scene Text Recognition
Thus, we propose a lightweight scene text recognition model named Hamming OCR.
Neural Mesh Refiner for 6-DoF Pose Estimation
We utilise the overlay between the accurate mask prediction and less accurate mesh prediction to iteratively optimise the direct regressed 6D pose information with a focus on translation estimation.
MASTER: Multi-Aspect Non-local Network for Scene Text Recognition
Attention-based scene text recognizers have gained huge success, which leverages a more compact intermediate representation to learn 1d- or 2d- attention by a RNN-based encoder-decoder architecture.
