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Works144 from public data
- Normalization Techniques in Training DNNs: Methodology, Analysis and Application537
A unified picture of the main motivation behind different approaches from the perspective of optimization is provided, and a taxonomy for understanding the similarities and differences between them is presented.
- 312
- Feature Shrinkage Pyramid for Camouflaged Object Detection with Transformers292
A novel transformer-based Feature Shrinkage Pyramid Network (FSPNet), which aims to hierarchically decode locality-enhanced neighboring transformer features through progressive shrinking for camou-flaged object detection and significantly outperforms the existing 24 competitors on three challenging COD benchmark datasets under six widely-used evaluation metrics.
- Partial Is Better Than All: Revisiting Fine-tuning Strategy for Few-shot Learning210
This paper proposes to transfer partial knowledge by freezing or fine-tuning particular layer(s) in the base model to control the extent of preserved transferability, and introduces an evolutionary search based method that is efficient to simultaneously locate the target layers and determine their individual learning rates.
- 210
- TokenPacker: Efficient Visual Projector for Multimodal LLM206
This work introduces TokenPacker, a novel visual projector designed to generate condensed visual tokens through a coarse-to-fine scheme, and proposes a dynamic image slicing scheme to efficiently handle high-resolution images with TokenPacker.
- 184
- stagNet: An Attentive Semantic RNN for Group Activity Recognition164
A novel attentive semantic recurrent neural network (RNN) for understanding group activities in videos, dubbed as stagNet, is proposed, based on the spatio-temporal attention and semantic graph, and adopted to attend to key persons/frames for improved performance.
- Learning Multi-Granular Hypergraphs for Video-Based Person Re-Identification162
This work proposes a novel graph-based framework, namely Multi-Granular Hypergraph (MGH), to pursue better representational capabilities by modeling spatiotemporal dependencies in terms of multiple granularities to enhance the overall video representation.
- Jointly Learning Structured Analysis Discriminative Dictionary and Analysis Multiclass Classifier160
The classification approach of the ADDL model is very efficient, because it can avoid the extra time-consuming sparse reconstruction process with trained dictionary for each new test data as most existing DL algorithms.
- 160
- Activation Modulation and Recalibration Scheme for Weakly Supervised Semantic Segmentation146
A novel activation modulation and recalibration scheme that establishes a new state-of-the-art performance on the PASCAL VOC 2012 dataset, surpassing not only current methods trained with the image-level of supervision but also some methods relying on stronger supervision, such as saliency label.
- 135
- Auto-Encoding Twin-Bottleneck Hashing133
This paper proposes an efficient and adaptive code-driven graph, which is updated by decoding in the context of an auto-encoder, and introduces into the framework twin bottlenecks that exchange crucial information collaboratively.
- Anchor-Free Person Search132
This work presents the Feature-Aligned Person Search Network (AlignPS), the first anchor-free framework to efficiently tackle this challenging task, and proposes an aligned feature aggregation module to generate more discriminative and robust feature embeddings by following a "re-id first" principle.
- 131
- Invertible Zero-Shot Recognition Flows119
The proposed Invertible Zero-shot Flow (IZF) learns factorized data embeddings with the forward pass of an invertible flow network, while the reverse pass generates data samples and theoretically extends conventional generative flows to a factorized conditional scheme.
- Semi-supervised local multi-manifold Isomap by linear embedding for feature extraction108
This paper proposes a semi-supervised local multi-manifold Isomap learning framework by linear embedding that can apply the labeled and unlabeled training samples to perform the joint learning of neighborhood preserving local nonlinear manifold features and a linear feature extractor, and evaluates SSMM-Isomap for manifold feature learning, data clustering and classification.
- TBN: Convolutional Neural Network with Ternary Inputs and Binary Weights105
This work proposes a Ternary-Binary Network (TBN), which provides an efficient approximation to standard CNNs, and demonstrates its consistent effectiveness when applied to various CNN architectures, and provides an optimal tradeoff between memory, efficiency and performance.
- 103
- Multi-stream Deep Networks for Person to Person Violence Detection in Videos103
This paper proposes a novel framework, i.e., multi-stream deep convolutional neural networks, for person to person violence detection in videos, and develops an acceleration stream to capture the important intense information usually involved in violent actions.
- Semantics-Aware Spatial-Temporal Binaries for Cross-Modal Video Retrieval93
A novel binary representation learning framework, named Semantics-aware Spatial-temporal Binaries, which simultaneously considers spatial-tem temporal context and semantic relationships for cross-modal video retrieval and adopts an iterative optimization scheme to learn deep encoding functions with attribute-guided stochastic training.
- 89
- P2-Net: Joint Description and Detection of Local Features for Pixel and Point Matching88
A dual fully-convolutional framework is presented that maps 2D and 3D inputs into a shared latent representation space to simultaneously describe and detect keypoints and achieves state-of-the-art results for the task of indoor visual localization.
- Clip-aware expressive feature learning for video-based facial expression recognition79
A video is divided into several short clips for processing and a clip-aware emotion-rich feature learning network (CEFLNet) is proposed for robust video-based FER, aiming to encode clip-based spatio-temporal features from the clips of a video.
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 2026-10-14. 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.
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