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Found 52 papers, 22 papers with code
Towards Training A Chinese Large Language Model for Anesthesiology
3) We introduce a standardized benchmark for evaluating medical LLM in Anesthesiology.
Healthcare Copilot: Eliciting the Power of General LLMs for Medical Consultation
The copilot framework, which aims to enhance and tailor large language models (LLMs) for specific complex tasks without requiring fine-tuning, is gaining increasing attention from the community.
Confronting Reward Overoptimization for Diffusion Models: A Perspective of Inductive and Primacy Biases
Then, we surprisingly discover that dormant neurons in our critic model act as a regularization against overoptimization, while active neurons reflect primacy bias in this setting.
Large Language Models as an Indirect Reasoner: Contrapositive and Contradiction for Automated Reasoning
The experimental results on popular LLMs, such as GPT-3. 5-turbo and Gemini-pro, show that our IR method enhances the overall accuracy of factual reasoning by 27. 33% and mathematical proof by 31. 43%, when compared with traditional DR methods.
TD^2-Net: Toward Denoising and Debiasing for Dynamic Scene Graph Generation
To address the above problems, in this paper, we introduce a network named TD$^2$-Net that aims at denoising and debiasing for dynamic SGG.
Exploring Sparsity in Graph Transformers
Graph Transformers (GTs) have achieved impressive results on various graph-related tasks.
SpliceMix: A Cross-scale and Semantic Blending Augmentation Strategy for Multi-label Image Classification
The "splice" in our method is two-fold: 1) Each mixed image is a splice of several downsampled images in the form of a grid, where the semantics of images attending to mixing are blended without object deficiencies for alleviating co-occurred bias; 2) We splice mixed images and the original mini-batch to form a new SpliceMixed mini-batch, which allows an image with different scales to contribute to training together.
Careful Selection and Thoughtful Discarding: Graph Explicit Pooling Utilizing Discarded Nodes
Graph pooling has been increasingly recognized as crucial for Graph Neural Networks (GNNs) to facilitate hierarchical graph representation learning.
Parameter Efficient Multi-task Model Fusion with Partial Linearization
We demonstrate that our partial linearization technique enables a more effective fusion of multiple tasks into a single model, outperforming standard adapter tuning and task arithmetic alone.
Unlikelihood Tuning on Negative Samples Amazingly Improves Zero-Shot Translation
Zero-shot translation (ZST), which is generally based on a multilingual neural machine translation model, aims to translate between unseen language pairs in training data.
Chasing Consistency in Text-to-3D Generation from a Single Image
In light of the above issues, we present Consist3D, a three-stage framework Chasing for semantic-, geometric-, and saturation-Consistent Text-to-3D generation from a single image, in which the first two stages aim to learn parameterized consistency tokens, and the last stage is for optimization.
Free-Form Composition Networks for Egocentric Action Recognition
We evaluated our method on three popular egocentric action recognition datasets, Something-Something V2, H2O, and EPIC-KITCHENS-100, and the experimental results demonstrate the effectiveness of the proposed method for handling data scarcity problems, including long-tailed and few-shot egocentric action recognition.
On Exploring Node-feature and Graph-structure Diversities for Node Drop Graph Pooling
A pooling operation is essential for effective graph-level representation learning, where the node drop pooling has become one mainstream graph pooling technology.
Divide, Conquer, and Combine: Mixture of Semantic-Independent Experts for Zero-Shot Dialogue State Tracking
Zero-shot transfer learning for Dialogue State Tracking (DST) helps to handle a variety of task-oriented dialogue domains without the cost of collecting in-domain data.
Noise-Resistant Multimodal Transformer for Emotion Recognition
To this end, we present a novel paradigm that attempts to extract noise-resistant features in its pipeline and introduces a noise-aware learning scheme to effectively improve the robustness of multimodal emotion understanding.
Token Contrast for Weakly-Supervised Semantic Segmentation
Secondly, to further differentiate the low-confidence regions in CAM, we devised a Class Token Contrast module (CTC) inspired by the fact that class tokens in ViT can capture high-level semantics.
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
OmniForce: On Human-Centered, Large Model Empowered and Cloud-Edge Collaborative AutoML System
Automated machine learning (AutoML) seeks to build ML models with minimal human effort.
Bag of Tricks for Effective Language Model Pretraining and Downstream Adaptation: A Case Study on GLUE
This technical report briefly describes our JDExplore d-team's submission Vega v1 on the General Language Understanding Evaluation (GLUE) leaderboard, where GLUE is a collection of nine natural language understanding tasks, including question answering, linguistic acceptability, sentiment analysis, text similarity, paraphrase detection, and natural language inference.
DIVOTrack: A Novel Dataset and Baseline Method for Cross-View Multi-Object Tracking in DIVerse Open Scenes
Although cross-view multi-object tracking has received increased attention in recent years, existing datasets still have several issues, including 1) missing real-world scenarios, 2) lacking diverse scenes, 3) owning a limited number of tracks, 4) comprising only static cameras, and 5) lacking standard benchmarks, which hinder the investigation and comparison of cross-view tracking methods.
Combating Noisy Labels with Sample Selection by Mining High-Discrepancy Examples
As selected data have high discrepancies in probabilities, the divergence of two networks can be maintained by training on such data.
Original or Translated? On the Use of Parallel Data for Translation Quality Estimation
Machine Translation Quality Estimation (QE) is the task of evaluating translation output in the absence of human-written references.
Toward Efficient Language Model Pretraining and Downstream Adaptation via Self-Evolution: A Case Study on SuperGLUE
This technical report briefly describes our JDExplore d-team's Vega v2 submission on the SuperGLUE leaderboard.
Ranked #1 on
Common Sense Reasoning
on ReCoRD
Pose-disentangled Contrastive Learning for Self-supervised Facial Representation
Self-supervised facial representation has recently attracted increasing attention due to its ability to perform face understanding without relying on large-scale annotated datasets heavily.
TASA: Deceiving Question Answering Models by Twin Answer Sentences Attack
We present Twin Answer Sentences Attack (TASA), an adversarial attack method for question answering (QA) models that produces fluent and grammatical adversarial contexts while maintaining gold answers.
Vega-MT: The JD Explore Academy Translation System for WMT22
As for model sizes, we scale the Transformer-Big up to the extremely large model that owns nearly 4. 7 Billion parameters, to fully enhance the model capacity for our Vega-MT.
Ranked #1 on
Machine Translation
on WMT 2022 English-Russian
Not All Instances Contribute Equally: Instance-adaptive Class Representation Learning for Few-Shot Visual Recognition
To address the above issues, we propose a novel metric-based meta-learning framework termed instance-adaptive class representation learning network (ICRL-Net) for few-shot visual recognition.
Improving Adversarial Robustness via Mutual Information Estimation
To alleviate this negative effect, in this paper, we investigate the dependence between outputs of the target model and input adversarial samples from the perspective of information theory, and propose an adversarial defense method.
Learning Graph Neural Networks for Image Style Transfer
To this end, we develop an elaborated GNN model with content and style local patches as the graph vertices.
Hierarchical Semi-Supervised Contrastive Learning for Contamination-Resistant Anomaly Detection
Anomaly detection aims at identifying deviant samples from the normal data distribution.
Comprehensive Graph Gradual Pruning for Sparse Training in Graph Neural Networks
However, the LTH-based methods suffer from two major drawbacks: 1) they require exhaustive and iterative training of dense models, resulting in an extremely large training computation cost, and 2) they only trim graph structures and model parameters but ignore the node feature dimension, where significant redundancy exists.
CLNode: Curriculum Learning for Node Classification
Experimental results on real-world networks demonstrate that CLNode is a general framework that can be combined with various GNNs to improve their accuracy and robustness.
Pluralistic Image Completion with Probabilistic Mixture-of-Experts
Second, the constraints for diversity are designed to be task-agnostic, which causes the constraints to not work well.
RU-Net: Regularized Unrolling Network for Scene Graph Generation
Scene graph generation (SGG) aims to detect objects and predict the relationships between each pair of objects.
HL-Net: Heterophily Learning Network for Scene Graph Generation
Despite their effectiveness, however, current SGG methods only assume scene graph homophily while ignoring heterophily.
Graph Pooling for Graph Neural Networks: Progress, Challenges, and Opportunities
Graph neural networks have emerged as a leading architecture for many graph-level tasks, such as graph classification and graph generation.
BatchFormerV2: Exploring Sample Relationships for Dense Representation Learning
Specifically, when applying the proposed module, it employs a two-stream pipeline during training, i. e., either with or without a BatchFormerV2 module, where the batchformer stream can be removed for testing.
Exploring High-Order Structure for Robust Graph Structure Learning
In this paper, we analyze the adversarial attack on graphs from the perspective of feature smoothness which further contributes to an efficient new adversarial defensive algorithm for GNNs.
Contrastive Boundary Learning for Point Cloud Segmentation
Point cloud segmentation is fundamental in understanding 3D environments.
Ranked #17 on
Semantic Segmentation
on S3DIS
Where Does the Performance Improvement Come From? -- A Reproducibility Concern about Image-Text Retrieval
In contrast to previous works, we focus on the reproducibility of the approaches and the examination of the elements that lead to improved performance by pretrained and nonpretrained models in retrieving images and text.
Learning Affinity from Attention: End-to-End Weakly-Supervised Semantic Segmentation with Transformers
Motivated by the inherent consistency between the self-attention in Transformers and the semantic affinity, we propose an Affinity from Attention (AFA) module to learn semantic affinity from the multi-head self-attention (MHSA) in Transformers.
Ranked #28 on
Weakly-Supervised Semantic Segmentation
on COCO 2014 val
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
A Review of Affective Generation Models
Therefore, we propose to provide a comprehensive review of affective generation models, as models are most commonly leveraged to affect others' emotional states.
Hyper-relationship Learning Network for Scene Graph Generation
As a result, HLN significantly improves the performance of scene graph generation by integrating and reasoning from object interactions, relationship interactions, and transitive inference of hyper-relationships.
Weakly-Supervised Semantic Segmentation with Visual Words Learning and Hybrid Pooling
In the visual words learning module, we counter the first problem by enforcing the classification network to learn fine-grained visual word labels so that more object extents could be discovered.
Resistance Training using Prior Bias: toward Unbiased Scene Graph Generation
To address this problem, we propose Resistance Training using Prior Bias (RTPB) for the scene graph generation.
SkipNode: On Alleviating Performance Degradation for Deep Graph Convolutional Networks
In this paper, we conduct theoretical and experimental analysis to explore the fundamental causes of performance degradation in deep GCNs: over-smoothing and gradient vanishing have a mutually reinforcing effect that causes the performance to deteriorate more quickly in deep GCNs.
Contrastive Graph Poisson Networks: Semi-Supervised Learning with Extremely Limited Labels
Essentially, our CGPN can enhance the learning performance of GNNs under extremely limited labels by contrastively propagating the limited labels to the entire graph.
Co-variance: Tackling Noisy Labels with Sample Selection by Emphasizing High-variance Examples
The sample selection approach is popular in learning with noisy labels, which tends to select potentially clean data out of noisy data for robust training.
Expression Snippet Transformer for Robust Video-based Facial Expression Recognition
To this end, we propose to decompose each video into a series of expression snippets, each of which contains a small number of facial movements, and attempt to augment the Transformer's ability for modeling intra-snippet and inter-snippet visual relations, respectively, obtaining the Expression snippet Transformer (EST).
Ranked #14 on
Dynamic Facial Expression Recognition
on DFEW
Dynamic Facial Expression Recognition
Facial Expression Recognition
+1
Graph Pattern Loss based Diversified Attention Network for Cross-Modal Retrieval
In this paper, we propose a Graph Pattern Loss based Diversified Attention Network(GPLDAN) for unsupervised cross-modal retrieval to deeply analyze correlations among representations.
Not All Operations Contribute Equally: Hierarchical Operation-Adaptive Predictor for Neural Architecture Search
Despite their efficiency, current graph-based predictors treat all operations equally, resulting in biased topological knowledge of cell architectures.
Comprehensive Graph-conditional Similarity Preserving Network for Unsupervised Cross-modal Hashing
Essentially, DGCPN addresses the inaccurate similarity problem by exploring and exploiting the data's intrinsic relationships in a graph.
On Exploring Undetermined Relationships for Visual Relationship Detection
In this paper, we explore the beneficial effect of undetermined relationships on visual relationship detection.
