Is this you? Claim this profile to correct it, add a bio and choose the work people see first.
Claim this profileWorks166 from public data
- DenseCLIP: Language-Guided Dense Prediction with Context-Aware Prompting886
This work presents a new framework for dense prediction by implicitly and explicitly leveraging the pre-trained knowledge from CLIP and converts the original image-text matching problem in CLIP to a pixel- text matching problem and uses the pixel-text score maps to guide the learning of dense prediction models.
- Stochastic Trajectory Prediction via Motion Indeterminacy Diffusion438
This paper presents a new framework to formulate the trajectory prediction task as a reverse process of motion indeterminacy diffusion (MID), in which the history behavior information and the social interactions are encoded as a state embedding and a Transformer-based diffusion model is devised to capture the temporal dependencies of trajectories.
- 419
- Counterfactual Attention Learning for Fine-Grained Visual Categorization and Re-identification380
This paper analyzes the effect of the learned visual attention on network prediction through counterfactual intervention and maximize the effect to encourage the network to learn more useful attention for fine-grained image recognition.
- FineDiving: A Fine-grained Dataset for Procedure-aware Action Quality Assessment203
This work proposes a procedure-aware approach for action quality assessment, learned by a new Temporal Segmentation Attention module, and proposes to parse pairwise query and exemplar action instances into consecutive steps with diverse semantic and temporal correspondences.
- 200
- Automatic EEG seizure detection using dual-tree complex wavelet-Fourier features182
A new Electroencephalography (EEG) seizure detection method by using the dual-tree complex wavelet (DTCWT) - Fourier features, which achieve perfect classification rates (100%) for the EEG database from the University of Bonn.
- 160
- PromptCAL: Contrastive Affinity Learning via Auxiliary Prompts for Generalized Novel Category Discovery139
This work proposes contrastive affinity learning to calibrate semantic representations based on the authors' iterative semi-supervised affinity graph generation method for semantically-enhanced supervision and proposes a discriminative prompt regularization loss to reinforce semantic discriminativeness of prompt-adapted pre-trained vision transformer for refined affinity relationships.
- Invariant Fourier-wavelet descriptor for pattern recognition135
The result shows that the Fourier-wavelet descriptor is an efficient representation which can provide for reliable recognition of complex patterns such as roadsigns, keys, aircrafts, characters, etc.
- Path planning of mobile robot with PSO-based APF and fuzzy-based DWA subject to moving obstacles106
This paper proposes a two-layer path-planning method, where an optimized artificial potential field method and an improved dynamic window approach (DWA) are used at the global and local layer, respectively, to enable the robot to plan a better path under a multi-obstacle environment while avoiding the moving obstacles effectively.
- 105
- Deep Credible Metric Learning for Unsupervised Domain Adaptation Person Re-identification100
Experimental results demonstrate that the proposed deep credible metric learning (DCM-L) method explores credible and valuable training data and improves the performance of unsupervised domain adaptation.
- Human Trajectory Prediction via Counterfactual Analysis98
This work proposes a counterfactual analysis method for human trajectory prediction to investigate the causality between the predicted trajectories and input clues and alleviate the negative effects brought by the environment bias.
- PLOT: Prompt Learning with Optimal Transport for Vision-Language Models86
This work proposes to apply optimal transport to match the vision and text modalities and first model images and the categories with visual and textual feature sets and applies a two-stage optimization strategy to learn the prompts.
- Temporal Coherence or Temporal Motion: Which Is More Critical for Video-Based Person Re-identification?83
This paper proposes a simple yet effective Adversarial Feature Augmentation (AFA) method, which highlights the temporal coherence features by introducing adversarial augmented temporal motion noise.
- Automatic Epileptic Seizure Detection in EEG Using Nonsubsampled Wavelet–Fourier Features78
A method for EEG seizure detection by decomposing EEG signals for up to six wavelet scales without downsampling and taking the magnitude of the Fourier coefficients as features for seizure detection is proposed.
- IMAGE DENOISING BASED ON WAVELET SHRINKAGE USING NEIGHBOR AND LEVEL DEPENDENCY78
Three different wavelet shrinkage methods, namely NeighShrink, NeighSure and NeighLevel, are presented, which give comparatively higher peak signal to noise ratio (PSNR), are much more efficient and have less visual artifacts compared to other methods.
- Personalized Trajectory Prediction via Distribution Discrimination71
A distribution discrimination method to predict personalized motion patterns by distinguishing the potential distributions, which can be integrated with existing multi-modal stochastic predictive models as a plug-and-play module to learn the more discriminative latent distribution.
- 69
- 61
- Denoising and dimensionality reduction of hyperspectral imagery using wavelet packets, neighbour shrinking and principal component analysis61
The proposed method that can fulfil both denoising and dimensionality reduction of hyperspectral data using wavelet packets, neighbour wavelet shrinking and PCA is confirmed.
- Simultaneous dimensionality reduction and denoising of hyperspectral imagery using bivariate wavelet shrinking and principal component analysis61
Experiments reported in this paper confirm that the proposed method outperforms PCA for endmember extraction and mineral detection using the Cuprite data cube.
- Understanding Masked Autoencoders via Hierarchical Latent Variable Models57
This work formally characterize and justify existing empirical in-sights and provide theoretical guarantees of MAE, and formulate the underlying data-generating process as a hierarchical latent variable model, and shows that under reasonable assumptions, MAE provably identifies a set of latent variables in the hierarchical model.
- 56
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-16. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
Report an error
Wrong papers, two people merged into one, or a profile that should not be here? Tell us and we will fix or hide it.