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Found 52 papers, 32 papers with code
PruningBench: A Comprehensive Benchmark of Structural Pruning
PruningBench showcases the following three characteristics: 1) PruningBench employs a unified and consistent framework for evaluating the effectiveness of diverse structural pruning techniques; 2) PruningBench systematically evaluates 16 existing pruning methods, encompassing a wide array of models (e. g., CNNs and ViTs) and tasks (e. g., classification and detection); 3) PruningBench provides easily implementable interfaces to facilitate the implementation of future pruning methods, and enables the subsequent researchers to incorporate their work into our leaderboards.
A Large-scale Universal Evaluation Benchmark For Face Forgery Detection
Consequently, various face forgery detection techniques have been proposed to identify such fake facial content.
Improving Adversarial Robustness via Feature Pattern Consistency Constraint
Recognizing that a correct prediction relies on the correctness of the latent feature's pattern, we introduce a novel and effective Feature Pattern Consistency Constraint (FPCC) method to reinforce the latent feature's capacity to maintain the correct feature pattern.
Angle Robustness Unmanned Aerial Vehicle Navigation in GNSS-Denied Scenarios
Due to the inability to receive signals from the Global Navigation Satellite System (GNSS) in extreme conditions, achieving accurate and robust navigation for Unmanned Aerial Vehicles (UAVs) is a challenging task.
Progressive Feature Self-reinforcement for Weakly Supervised Semantic Segmentation
Building upon this, we introduce a complementary self-enhancement method that constrains the semantic consistency between these confident regions and an augmented image with the same class labels.
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
Graph Neural Networks-based Hybrid Framework For Predicting Particle Crushing Strength
Therefore, we firstly generate a dataset with 45, 000 numerical simulations and 900 particle types to facilitate the research progress of machine learning for particle crushing.
AFPN: Asymptotic Feature Pyramid Network for Object Detection
Multi-scale features are of great importance in encoding objects with scale variance in object detection tasks.
Spatiotemporal-Augmented Graph Neural Networks for Human Mobility Simulation
The human mobility simulation task aims to generate human mobility trajectories given a small set of trajectory data, which have aroused much concern due to the scarcity and sparsity of human mobility data.
Improving Expressivity of GNNs with Subgraph-specific Factor Embedded Normalization
Graph Neural Networks (GNNs) have emerged as a powerful category of learning architecture for handling graph-structured data.
Improving Knowledge Distillation via Regularizing Feature Norm and Direction
Finally, we propose a rather simple loss term (dubbed ND loss) to simultaneously (1) encourage student to produce large-\emph{norm} features, and (2) align the \emph{direction} of student features and teacher class-means.
Ranked #1 on
Knowledge Distillation
on ImageNet
Temporal Aggregation and Propagation Graph Neural Networks for Dynamic Representation
The whole temporal neighborhood of nodes reveals the varying preferences of nodes.
Transition Propagation Graph Neural Networks for Temporal Networks
The proposed TIP-GNN focuses on the bilevel graph structure in temporal networks: besides the explicit interaction graph, a node's sequential interactions can also be constructed as a transition graph.
Life Regression based Patch Slimming for Vision Transformers
In this study, we tackle the patch slimming problem from a different perspective by proposing a life regression module that determines the lifespan of each image patch in one go.
ViT-Calibrator: Decision Stream Calibration for Vision Transformer
In this work, we propose a new paradigm dubbed Decision Stream Calibration that boosts the performance of general Vision Transformers.
Propheter: Prophetic Teacher Guided Long-Tailed Distribution Learning
This is specifically achieved by devising an elaborated ``prophetic'' teacher, termed as ``Propheter'', that aims to learn the potential class distributions.
Model Doctor for Diagnosing and Treating Segmentation Error
Despite the remarkable progress in semantic segmentation tasks with the advancement of deep neural networks, existing U-shaped hierarchical typical segmentation networks still suffer from local misclassification of categories and inaccurate target boundaries.
Team DETR: Guide Queries as a Professional Team in Detection Transformers
Recent proposed DETR variants have made tremendous progress in various scenarios due to their streamlined processes and remarkable performance.
Recent advances in artificial intelligence for retrosynthesis
Afterwards, we analyze these methods in terms of their mechanism and performance, and introduce popular evaluation metrics for them, in which we also provide a detailed comparison among representative methods on several public datasets.
A Loopback Network for Explainable Microvascular Invasion Classification
The loopback between two branches enables the category label to supervise the cell locating branch to learn the locating ability for cancerous areas.
How To Prevent the Continuous Damage of Noises To Model Training?
To address this problem, the proposed GSS alleviates the damage by switching the current gradient direction of each sample to a new direction selected from a gradient direction pool, which contains all-class gradient directions with different probabilities.
Conservative-Progressive Collaborative Learning for Semi-supervised Semantic Segmentation
Pseudo supervision is regarded as the core idea in semi-supervised learning for semantic segmentation, and there is always a tradeoff between utilizing only the high-quality pseudo labels and leveraging all the pseudo labels.
Transferability Estimation Based On Principal Gradient Expectation
Transfer learning aims to improve the performance of target tasks by transferring knowledge acquired in source tasks.
Contrastive Identity-Aware Learning for Multi-Agent Value Decomposition
Value Decomposition (VD) aims to deduce the contributions of agents for decentralized policies in the presence of only global rewards, and has recently emerged as a powerful credit assignment paradigm for tackling cooperative Multi-Agent Reinforcement Learning (MARL) problems.
Mid-level Representation Enhancement and Graph Embedded Uncertainty Suppressing for Facial Expression Recognition
On the other hand, GUS is introduced to suppress the feature ambiguity in the representation space.
Facial Expression Recognition
Facial Expression Recognition (FER)
+1
Federated Selective Aggregation for Knowledge Amalgamation
The goal of FedSA is to train a student model for a new task with the help of several decentralized teachers, whose pre-training tasks and data are different and agnostic.
Interaction Pattern Disentangling for Multi-Agent Reinforcement Learning
In this work, we introduce a novel interactiOn Pattern disenTangling (OPT) method, to disentangle the entity interactions into interaction prototypes, each of which represents an underlying interaction pattern within a subgroup of the entities.
Ask-AC: An Initiative Advisor-in-the-Loop Actor-Critic Framework
Despite the promising results achieved, state-of-the-art interactive reinforcement learning schemes rely on passively receiving supervision signals from advisor experts, in the form of either continuous monitoring or pre-defined rules, which inevitably result in a cumbersome and expensive learning process.
Distribution-Aware Graph Representation Learning for Transient Stability Assessment of Power System
As the topology of the power system is in the form of graph structure, graph neural network based representation learning is naturally suitable for learning the status of the power system.
Comparison Knowledge Translation for Generalizable Image Classification
In this paper, we attempt to build a generalizable framework that emulates the humans' recognition mechanism in the image classification task, hoping to improve the classification performance on unseen categories with the support of annotations of other categories.
CNN LEGO: Disassembling and Assembling Convolutional Neural Network
the feature maps are adopted to locate the critical features in each layer.
Root-aligned SMILES: A Tight Representation for Chemical Reaction Prediction
Chemical reaction prediction, involving forward synthesis and retrosynthesis prediction, is a fundamental problem in organic synthesis.
Knowledge Amalgamation for Object Detection with Transformers
Moreover, the transformer-based students excel in learning amalgamated knowledge, as they have mastered heterogeneous detection tasks rapidly and achieved superior or at least comparable performance to those of the teachers in their specializations.
Imbalanced Sample Generation and Evaluation for Power System Transient Stability Using CTGAN
This paper proposes a controllable sample generation framework based on Conditional Tabular Generative Adversarial Network (CTGAN) to generate specified transient stability samples.
Model Doctor: A Simple Gradient Aggregation Strategy for Diagnosing and Treating CNN Classifiers
The aggregate gradient strategy is a versatile module for mainstream CNN classifiers.
Learning Dynamic Preference Structure Embedding From Temporal Networks
Our proposed Dynamic Preference Structure (DPS) framework consists of two stages: structure sampling and graph fusion.
Distribution Knowledge Embedding for Graph Pooling
A DKEPool network de facto disassembles representation learning into two stages, structure learning and distribution learning.
Boundary Knowledge Translation based Reference Semantic Segmentation
In this paper, we introduce a novel Reference semantic segmentation Network (Ref-Net) to conduct visual boundary knowledge translation.
Edge-competing Pathological Liver Vessel Segmentation with Limited Labels
The diagnosis of MVI needs discovering the vessels that contain hepatocellular carcinoma cells and counting their number in each vessel, which depends heavily on experiences of the doctor, is largely subjective and time-consuming.
Visual Boundary Knowledge Translation for Foreground Segmentation
To this end, we propose a Translation Segmentation Network (Trans-Net), which comprises a segmentation network and two boundary discriminators.
Shape Controllable Virtual Try-on for Underwear Models
Existing 2D image-based virtual try-on methods aim to transfer a target clothing image onto a reference person, which has two main disadvantages: cannot control the size and length precisely; unable to accurately estimate the user's figure in the case of users wearing thick clothes, resulting in inaccurate dressing effect.
Mutual-Complementing Framework for Nuclei Detection and Segmentation in Pathology Image
The existing two unsupervised methods are prone to failure on degenerated samples.
One-sample Guided Object Representation Disassembling
In this paper, we introduce the One-sample Guided Object Representation Disassembling (One-GORD) method, which only requires one annotated sample for each object category to learn disassembled object representation from unannotated images.
DEAL: Difficulty-aware Active Learning for Semantic Segmentation
To deal with this problem, we propose a semantic Difficulty-awarE Active Learning (DEAL) network composed of two branches: the common segmentation branch and the semantic difficulty branch.
Factorizable Graph Convolutional Networks
In this paper, we introduce a novel graph convolutional network (GCN), termed as factorizable graph convolutional network(FactorGCN), that explicitly disentangles such intertwined relations encoded in a graph.
Ranked #3 on
Node Classification
on PATTERN 100k
Unsupervised Learning Facial Parameter Regressor for Action Unit Intensity Estimation via Differentiable Renderer
Facial action unit (AU) intensity is an index to describe all visually discernible facial movements.
Unsupervised Facial Action Unit Intensity Estimation via Differentiable Optimization
The generator learns to "render" a face image from a set of facial parameters in a differentiable way, and the feature extractor extracts deep features for measuring the similarity of the rendered image and input real image.
Disassembling Object Representations without Labels
In this paper, we study a new representation-learning task, which we termed as disassembling object representations.
Semantic Regularization: Improve Few-shot Image Classification by Reducing Meta Shift
This strategy leads to severe meta shift issues across multiple tasks, meaning the learned prototypes or class descriptors are not stable as each task only involves their own support set.
Interpretable Partitioned Embedding for Customized Fashion Outfit Composition
In the supervised attributes module, multiple attributes labels are adopted to ensure that different parts of the overall embedding correspond to different attributes.
Dual Swap Disentangling
To achieve disentangling using the labeled pairs, we follow a "encoding-swap-decoding" process, where we first swap the parts of their encodings corresponding to the shared attribute and then decode the obtained hybrid codes to reconstruct the original input pairs.
Stroke Controllable Fast Style Transfer with Adaptive Receptive Fields
In this paper, we present a stroke controllable style transfer network that can achieve continuous and spatial stroke size control.
Neural Style Transfer: A Review
We first propose a taxonomy of current algorithms in the field of NST.
