Is this you? Claim this profile to correct it, add a bio and choose the work people see first.
Claim this profileWorks94 from public data
- Recurrent Squeeze-and-Excitation Context Aggregation Net for Single Image Deraining940
A novel deep network architecture based on deep convolutional and recurrent neural networks for single image deraining based on contextual information is proposed and outperforms the state-of-the-art approaches under all evaluation metrics.
- Expectation-Maximization Attention Networks for Semantic Segmentation663
This paper forms the attention mechanism into an expectation-maximization manner and iteratively estimate a much more compact set of bases upon which the attention maps are computed, which is robust to the variance of input and is also friendly in memory and computation.
- Video parsing, retrieval and browsing400
This paper presents an integrated solution for computer assisted video parsing and content-based video retrieval and browsing that uses video content information provided by a parsing process driven by visual feature analysis.
- 306
- Dynamic Modality Interaction Modeling for Image-Text Retrieval230
A novel modality interaction modeling network based upon the routing mechanism, which is the first unified and dynamic multimodal interaction framework towards image-text retrieval and demonstrates superiority compared with several state-of-the-art baselines.
- Deep Comprehensive Correlation Mining for Image Clustering204
A novel clustering framework, named deep comprehensive correlation mining~(DCCM), for exploring and taking full advantage of various kinds of correlations behind the unlabeled data from three aspects: Instead of only using pair-wise information, pseudo-label supervision is proposed to investigate category information and learn discriminative features.
- Agree to Disagree: Adaptive Ensemble Knowledge Distillation in Gradient Space144
This paper examines the diversity of teacher models in the gradient space and regard the ensemble knowledge distillation as a multi-objective optimization problem so that the method can be seen as a dynamic weighting method for each teacher in the ensemble.
- Stacked Hybrid-Attention and Group Collaborative Learning for Unbiased Scene Graph Generation135
A novel Stacked Hybrid-Attention network is presented, which facilitates the intra-modal refinement as well as the intermodal interaction, to serve as the encoder and an innovative Group Collaborative Learning strategy is devised to optimize the decoder.
- Overcoming cold tumors: a combination strategy of immune checkpoint inhibitors113
Understanding the transformation from ‘cold’ to ‘hot’ tumors is essential in developing effective cancer treatments, and identifying key factors that impact tumor T cell infiltration is vital for creating effective treatments targeting ‘cold’ tumors.
- Unified Graph and Low-Rank Tensor Learning for Multi-View Clustering110
This paper proposes the unified graph and low-rank tensor learning (UGLTL) for multi-view clustering and unifies the view-specific affinity matrix based on projected graph learning and reorganizes the affinity matrices into tensor form and learns its intrinsic tensor based on low- rank tensor approximation.
- Detecting and Grounding Multi-Modal Media Manipulation and Beyond98
This paper aims to not only detect the authenticity of multi-modal media, but also ground the manipulated content (i.e., image bounding boxes and text tokens), which requires deeper reasoning of multi-modal media manipulation.
- WKVQuant: Quantizing Weight and Key/Value Cache for Large Language Models Gains More88
This paper proposes WKVQuant, a PTQ framework especially designed for quantizing weights and the key/value (KV) cache of LLMs that incorporates past-only quantization to improve the computation of attention and introduces two-dimensional quantization strategy to handle the distribution of KV cache.
- 75
- SOGNet: Scene Overlap Graph Network for Panoptic Segmentation65
This study aims to model overlap relations among instances and resolve them for panoptic segmentation, Inspired by scene graph representation, and forms the overlapping problem as a simplified case, named scene overlap graph.
- High Quality Segmentation for Ultra High-resolution Images63
The Continuous Refinement Model (CRM) continuously aligns the feature map with the refinement target and aggregates features to reconstruct these image details and shows its significant generalization ability to fill the resolution gap between low-resolution training images and ultra high-resolution testing ones.
- Local Correlation Consistency for Knowledge Distillation60
To make the student focus on those informative local regions of the teacher's feature maps, a novel class-aware attention module is proposed to highlight the class-relevant regions and remove the confusing class-irrelevant regions, which makes the local correlation knowledge more accurate and valuable.
- 57
- AffordGrasp: In-Context Affordance Reasoning for Open-Vocabulary Task-Oriented Grasping in Clutter45
AffordGrasp is a novel open-vocabulary grasping framework that leverages the reasoning capabilities of vision-language models (VLMs) for in-context affordance reasoning and infers tasks directly from implicit user instructions, enabling more intuitive and seamless human-robot interaction in everyday scenarios.
- Self-Training Boosted Multi-Factor Matching Network for Composed Image Retrieval41
This work designs an iterative dual self-training paradigm to further enhance the performance of LIMN by fully utilizing the potential unlabeled reference-target image pairs in a weakly-supervised manner and denotes the iterative dual self-training paradigm enhanced LIMN as LIMN+.
- Joint Latent Subspace Learning and Regression for Cross-Modal Retrieval41
This paper first adopts the spectral regression method to learn the optimal latent space shared by data of all modalities based on the orthogonal constraints, then builds a graph model to project the multi-modality data into the latent space.
- Multi-modal cooking workflow construction for food recipes39
MM-ReS is built, the first large-scale dataset for cooking workflow construction, consisting of 9,850 recipes with human-labeled workflow graphs and a neural encoder-decoder model that utilizes both visual and textual information to construct the cooking workflow, which achieved over 20% performance gain over existing hand-crafted baselines.
- Multiple Models Fusion for Emotion Recognition in the Wild37
This paper proposes a multiple models fusion method to automatically recognize the expression in the video clip as part of the third Emotion Recognition in the Wild Challenge (EmotiW 2015).
- 36
- A Survey on Video Temporal Grounding With Multimodal Large Language Model34
Current research on VTG-MLLMs is examined through a three-dimensional taxonomy: the functional roles of MLLMs, highlighting their architectural significance; training paradigms, analyzing strategies for temporal reasoning and task adaptation; and video feature processing techniques, which determine spatiotemporal representation effectiveness.
- Fashion Compatibility Modeling through a Multi-modal Try-on-guided Scheme31
A new compatibility modeling scheme which integrates the outfit try-on appearance into the traditional discrete item interaction modeling, and is demonstrated to have superiority to state-of-the-arts modeling.
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.
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.