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- Gated Fusion Network for Single Image Dehazing973
An efficient algorithm to directly restore a clear image from a hazy input using an end-to-end trainable neural network that consists of an encoder and a decoder is proposed.
- Prognostic modelling options for remaining useful life estimation by industry966
Business issues that need to be considered when selecting an appropriate modelling approach for trial are discussed and classification tables and process flow diagrams are presented to assist industry and research personnel select appropriate prognostic models for predicting the remaining useful life of engineering assets within their specific business environment.
- Slake: A Semantically-Labeled Knowledge-Enhanced Dataset For Medical Visual Question Answering742
A large bilingual dataset, SLAKE, with comprehensive semantic labels annotated by experienced physicians and a new structural medical knowledge base for Med-VQA is presented, which includes richer modalities and covers more human body parts than the currently available dataset.
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- Deblurring by Realistic Blurring494
This paper proposes a new method which combines two GAN models, i.e., a learning-to-Blur GAN (BGAN) and learning- to-DeBlurGAN (DBGAN), in order to learn a better model for image deblurring by primarily learning how to blur images.
- Similarity Reasoning and Filtration for Image-Text Matching445
The superiority of the proposed Similarity Graph Reasoning and Attention Filtration network with achieving state-of-the-art performances on the Flickr30K and MSCOCO datasets is demonstrated, and the good interpretability of SGR and SAF with extensive qualitative experiments and analyses are demonstrated.
- Reconstruction Network for Video Captioning358
A reconstruction network with a novel encoder-decoder-reconstructor architecture, which leverages both the forward (video to sentence) and backward (sentence to video) flows for video captioning, and can boost the encoding models and leads to significant gains in video caption accuracy.
- Multimodal Convolutional Neural Networks for Matching Image and Sentence353
The m-CNN provides an end-to-end framework with convolutional architectures to exploit image representation, word composition, and the matching relations between the two modalities to significantly outperform the state-of-the-art approaches for bidirectional image and sentence retrieval on the Flickr8K and Flickr30K datasets.
- Temporally Grounding Natural Sentence in Video345
An effective and efficient method that grounds (i.e., localizes) natural sentences in long, untrimmed video sequences using a novel Temporal GroundNet to temporally capture the evolving fine-grained frame-by-word interactions between video and sentence.
- Semantic Conditioned Dynamic Modulation for Temporal Sentence Grounding in Videos303
A novel semantic conditioned dynamic modulation mechanism, which leverages the sentence semantics to modulate the temporal convolution operations for better correlating and composing the sentence-relevant video contents over time.
- Liquid Warping GAN: A Unified Framework for Human Motion Imitation, Appearance Transfer and Novel View Synthesis292
A 3D body mesh recovery module is proposed to disentangle the pose and shape, which can not only model the joint location and rotation but also characterize the personalized body shape and is able to support a more flexible warping from multiple sources.
- Recurrent Fusion Network for Image Captioning286
This paper proposes a novel recurrent fusion network (RFNet) for the image captioning task, which can exploit the interactions among the outputs of the image encoders and generate new compact and informative representations for the decoder.
- Learning to Answer Questions from Image Using Convolutional Neural Network269
The proposed CNN provides an end-to-end framework with convolutional architectures for learning not only the image and question representations, but also their inter-modal interactions to produce the answer.
- Image Deformation Meta-Networks for One-Shot Learning258
This work combines a meta-learner with an image deformation sub-network that produces additional training examples, and optimize both models in an end-to-end manner to significantly outperform state-of-the-art approaches on widely used one-shot learning benchmarks.
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- PromptDet: Towards Open-Vocabulary Detection Using Uncurated Images245
The goal of this work is to establish a scalable pipeline for expanding an object detector towards novel/unseen categories, using zero manual annotations, and the proposed detector, termed as PromptDet, shows superior performance over existing approaches with fewer additional training images andzero manual annotations whatsoever.
- Bidirectional Attentive Fusion with Context Gating for Dense Video Captioning239
This work proposes a bidirectional proposal method that effectively exploits both past and future contexts to make proposal predictions, and proposes a novel context gating mechanism to balance the contributions from the current event and its surrounding contexts dynamically.
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- Unsupervised Image Captioning234
This paper makes the first attempt to train an image captioning model in an unsupervised manner, and requires an image set, a sentence corpus, and an existing visual concept detector.
- Multi-Granularity Generator for Temporal Action Proposal216
Through temporally adjusting the segment proposals with fine-grained information based on frame actionness, MGG achieves the superior performance over state-of-the-art methods on the public THUMOS-14 and ActivityNet-1.3 datasets.
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- Temporally Grounding Language Queries in Videos by Contextual Boundary-Aware Prediction212
This work proposes an end-to-end boundary-aware model, which uses a lightweight branch to predict semantic boundaries corresponding to the given linguistic information, and outperforms its competitors with a clear margin on three public datasets.
- Learning to Generate Time-Lapse Videos Using Multi-stage Dynamic Generative Adversarial Networks207
This work presents a generative adversarial network (GAN) based two-stage approach to generating realistic time-lapse videos of high resolution and demonstrates the superiority of this model over the state-of-the-art models.
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- Consensus-Aware Visual-Semantic Embedding for Image-Text Matching196
A Consensus-aware Visual-Semantic Embedding model is proposed to incorporate the consensus information, namely the commonsense knowledge shared between both modalities, into image-text matching and learns the associations and alignments between image and text based on the exploited consensus.
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.
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