Is this you? Claim this profile to correct it, add a bio and choose the work people see first.
Claim this profileWorks248 from public data
- X-CLIP: End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval541
This paper presents a novel multi-grained contrastive model, namely X-CLIP, and proposes the Attention Over Similarity Matrix (AOSM) module to make the model focus on the contrast between essential frames and words, thus lowering the impact of unnecessary frames and Words on retrieval results.
- Pix2Vox: Context-Aware 3D Reconstruction From Single and Multi-View Images392
Experimental results on the ShapeNet and Pix3D benchmarks indicate that the proposed Pix2Vox outperforms state-of-the-arts by a large margin, and the proposed method is 24 times faster than 3D-R2N2 in terms of backward inference time.
- Multi-Task Collaborative Network for Joint Referring Expression Comprehension and Segmentation385
A novel Multi-task Collaborative Network (MCN) is proposed to achieve a joint learning of REC and RES for the first time and addresses a key challenge in this multi-task setup, i.e., the prediction conflict.
- Dual-level Collaborative Transformer for Image Captioning366
A novel Dual-Level Collaborative Transformer (DLCT) network is introduced to realize the complementary advantages ofriptive region features extracted by object detection networks and address the semantic noises caused by the direct fusion of these two features.
- Exploring Principles-of-Art Features For Image Emotion Recognition363
Experiments demonstrate the superiority of PAEF for affective image classification and regression (with about 5% improvement on classification accuracy and 0.2 decrease in mean squared error), as compared to the state-of-the-art approaches.
- 275
- 263
- SeqTR: A Simple Yet Universal Network for Visual Grounding247
Experiments on five benchmark datasets demonstrate that the proposed SeqTR outperforms (or is on par with) the existing state-of-the-arts, proving that a simple yet universal approach for visual grounding is indeed feasible.
- Improving Image Captioning by Leveraging Intra- and Inter-layer Global Representation in Transformer Network213
This paper introduces a Global Enhanced Transformer (termed GET) to enable the extraction of a more comprehensive global representation, and then adaptively guide the decoder to generate high-quality captions.
- Cascade Grouped Attention Network for Referring Expression Segmentation160
A Cascade Grouped Attention Network with two innovative designs: Cascade Grouping Attention (CGA) and Instance-level Attention (ILA) loss that can achieve the high efficiency of one-stage RES while possessing a strong reasoning ability comparable to the two-stage methods.
- Rotated Multi-Scale Interaction Network for Referring Remote Sensing Image Segmentation149
The Rotated Multi-Scale In-teraction Network (RMSIN) is introduced, an innovative approach de-signed for the unique demands of RRSIS, surpassing existing state-of-the-art models by a signif-icant margin.
- Towards local visual modeling for image captioning137
Experimental results show that the proposed Locality-Sensitive Transformer Network (LSTNet) is not only capable of local visual modeling, but also outperforms a bunch of state-of-the-art captioning models on offline and online testings, i.e., 134.8 CIDEr and 136.3 C IDEr, respectively.
- 134
- Exploiting the complementary strengths of multi-layer CNN features for image retrieval133
A mapping function is designed to highlight the effectiveness of low-layer similarity, when measuring fine-grained similarity between query image and its nearest neighbors with similar semantic, and can achieve competitive performance on popular retrieval benchmarks.
- Photo assessment based on computational visual attention model101
By integrating top-down supervision into the visual attention model, this work achieves personalized photo assessment to take user preference into quality evaluation, which can be extended into object or semantic oriented photo assessment scenarios.
- Active Teacher for Semi-Supervised Object Detection98
The experimental results not only validate the superior performance gain of Active Teacher over the compared methods, but also show that it enables the baseline network, i.e., Faster-RCNN, to achieve 100% supervised performance with much less label expenditure,i.e. 40% labeled examples on MS-COCO.
- 98
- A Real-Time Global Inference Network for One-Stage Referring Expression Comprehension86
The proposed RealGIN outperforms most existing methods and achieves very competitive performances against the most advanced one, i.e., MAttNet, under the same hardware, and can boost the processing speed by 10–20 times over the existing methods.
- 86
- 81
- Deep Saliency Hashing for Fine-Grained Retrieval77
This work proposes a novel deep hashing model, named deep saliency hashing (DSaH), which automatically mines salient regions and learns semantic-preserving hashing codes simultaneously, and performs the best for the fine-grained retrieval task and beats the strongest competitor by approximately 10% on both Stanford Dogs and CUB Birds.
- Towards Optimal Fine Grained Retrieval via Decorrelated Centralized Loss with Normalize-Scale Layer77
A novel metric learning scheme, termed Normalize-Scale Layer and Decorrelated Global Centralized Ranking Loss, which achieves extremely efficient and discriminative learning, i.e., 5× speedup over triplet loss and 12% recall boost on CARS196.
- 72
- Clover: Towards A Unified Video-Language Alignment and Fusion Model65
Clover is introduced, a Correlated Video-Language pre-training method-towards a universal Video- language model for solving multiple video understanding tasks with neither performance nor efficiency compromise, and improves cross-modal feature alignment and fusion via a novel tri- modal alignment pre- training task.
- Towards Lightweight Transformer Via Group-Wise Transformation for Vision-and-Language Tasks65
WL-Transformer applies Group-wise Transformation to reduce both the parameters and computations of Transformer, while also preserving its two main properties, i.e., the efficient attention modeling on diverse subspaces of MHA, and the expanding-scaling feature transformation of FFN.
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 2026-10-13. 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.