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Works187 from public data
- Learning Affinity from Attention: End-to-End Weakly-Supervised Semantic Segmentation with Transformers287
An Affinity from Attention (AFA) module is proposed to learn semantic affinity from the multi-head self-attention (MHSA) in Transformers, which naturally integrate global information, to generate more integral initial pseudo labels for end-to-end WSSS.
- Token Contrast for Weakly-Supervised Semantic Segmentation213
Experiments on the PASCAL VOC and MS COCO datasets show the proposed ToCo can remarkably surpass other single-stage competitors and achieve comparable performance with state-of-the-art multi-stage methods.
- Deep Graph-neighbor Coherence Preserving Network for Unsupervised Cross-modal Hashing207
A deep graph-neighbor coherence preserving network (DGCPN), which addresses the inaccurate similarity problem by exploring and exploiting the data's intrinsic relationships in a graph and regulates comprehensive similarity preserving losses.
- Contrastive Boundary Learning for Point Cloud Segmentation204
This paper proposes a novel contrastive boundary learning (CBL) framework for point cloud segmentation that enhances feature discrimination between points across boundaries by contrasting their representations with the assistance of scene contexts at multiple scales.
- Expression snippet transformer for robust video-based facial expression recognition119
This work designs an attention-augmented snippet feature extractor (AA-SFE) to enhance the encoding of subtle facial movements of each snippet by gradually attending to more salient information, and introduces a shuffled snippet order prediction (SSOP) head and a corresponding loss to improve the modeling of subtle motion changes across subsequent snippets.
- Comprehensive Linguistic-Visual Composition Network for Image Retrieval98
A Comprehensive Linguistic-Visual Composition Network (CLVC-Net) for image retrieval is proposed, targeting comprehensive multi-modal compositions and a mutual enhancement module is designed to promote local-wise and global-wise composition processes by forcing them to share knowledge with each other.
- Estimating Noise Transition Matrix with Label Correlations for Noisy Multi-Label Learning95
This work studies the identifiability problem of the class-dependent transition matrix in noisy multi-label learning, and inspired by the identifiability results, a new estimator is proposed by exploiting label correlations without neither anchor points nor accurate class posterior, leading to better classification performance.
- On Exploring Undetermined Relationships for Visual Relationship Detection92
This paper proposes a novel multi-modal feature based undetermined relationship learning network (MF-URLN) and achieves great improvements in relationship detection, compared with state-of-the-art methods.
- Harnessing the Power of MLLMs for Transferable Text-to-Image Person ReID91
A novel method is introduced that automatically identifies words in a description that do not correspond with the image, based on the similarity between one text and all patch token embeddings in the image, which alleviates the impact of noisy textual descriptions.
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- Parameter Efficient Multi-task Model Fusion with Partial Linearization79
This work proposes a novel method to improve multi-task fusion for parameter-efficient fine-tuning techniques like LoRA fine-tuning, which partially linearizes only the adapter modules and applies task arithmetic over the linearized adapters.
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- Weakly-Supervised Semantic Segmentation with Visual Words Learning and Hybrid Pooling64
The second drawback of CAMs is alleviated with the proposed hybrid pooling, which incorporates the global average and local discriminative information to simultaneously ensure object completeness and reduce background regions.
- Multi-target Knowledge Distillation via Student Self-reflection63
A newknowledge distillation framework entitled multi-target knowledge distillation via student self-reflection or MTKD-SSR, which can not only enhance the teacher's ability in unfolding the knowledge to be distilled, but also improve the student’s capacity of digesting the knowledge.
- Gapformer: Graph Transformer with Graph Pooling for Node Classification61
Gapformer is presented, a method for node classification that deeply incorporates Graph Transformer with Graph Pooling and coarsens the large-scale nodes of a graph into a smaller number of pooling node via local or global graph pooling methods, and then computes the attention solely with the pooling nodes rather than all other nodes.
- Resistance Training Using Prior Bias: Toward Unbiased Scene Graph Generation61
Resistance Training using Prior Bias (RTPB) uses a distributed-based prior bias to improve models' detecting ability on less frequent relationships during training, thus improving the model generalizability on tail categories.
- Comprehensive Graph Gradual Pruning for Sparse Training in Graph Neural Networks59
A comprehensive graph gradual pruning framework termed CGP is proposed, designed by designing a during-training graph pruning paradigm to dynamically prune GNNs within one training process, which significantly reduces the computation costs.
- CLNode: Curriculum Learning for Node Classification55
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.
- HL-Net: Heterophily Learning Network for Scene Graph Generation54
A novel Heterophily Learning Network (HL-Net) is proposed to comprehensively explore the homophily and heterophily be-tween objects/relationships in scene graphs and demonstrates the superiority of the proposed HL-Net over existing state-of-the-art approaches.
- DIVOTrack: A Novel Dataset and Baseline Method for Cross-View Multi-Object Tracking in DIVerse Open Scenes49
The DIVOTrack is introduced: a novel baseline cross-view tracking method with a unified joint detection and cross-view tracking framework named CrossMOT, which learns object detection, single-view association, and cross-view matching with an all-in-one embedding model.
- RU-Net: Regularized Unrolling Network for Scene Graph Generation48
This paper first study the relation between GMP and graph Laplacian denoising (GLD) from the perspective of the unrolling technique, determining that GMP can be formulated as a solver for GLD, and proposes an unrolled message passing module and an fp-based graph regularization to suppress spurious connections between nodes.
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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 2026-10-12. 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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