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Works110 from public data
- Recent advances in convolutional neural networks6,499
This paper details the improvements of CNN on different aspects, including layer design, activation function, loss function, regularization, optimization and fast computation, and introduces various applications of convolutional neural networks in computer vision, speech and natural language processing.
- LRM: Large Reconstruction Model for Single Image to 3D1,030
This work proposes the first Large Reconstruction Model (LRM) that predicts the 3D model of an object from a single input image within just 5 seconds, and adopts a highly scalable transformer-based architecture with 500 million learnable parameters to directly predict a neural radiance field (NeRF) from the input image.
- Look, Imagine and Match: Improving Textual-Visual Cross-Modal Retrieval with Generative Models394
This work proposes to incorporate generative processes into the cross-modal feature embedding, through which it is able to learn not only the global abstract features but also the local grounded features of image-text pairs.
- Scene Graph Generation With External Knowledge and Image Reconstruction322
This paper proposes a novel scene graph generation algorithm with external knowledge and image reconstruction loss to overcome dataset issues, and extracts commonsense knowledge from the external knowledge base to refine object and phrase features for improving generalizability inscene graph generation.
- Towards Language-Free Training for Text-to-Image Generation235
The first work to train text-to-image generation models without any text data is proposed, which leverages the well-aligned multi-modal semantic space of the powerful pre-trained CLIP model: the requirement of text-conditioning is seamlessly alleviated via generating text features from image features.
- Unpaired Image Captioning via Scene Graph Alignments199
This paper proposes an unsupervised feature alignment method that maps the scene graph features from the image to the sentence modality and can generate quite promising results without using any image-caption training pairs, outperforming existing methods by a wide margin.
- Stack-Captioning: Coarse-to-Fine Learning for Image Captioning196
A coarse-to-fine multi-stage prediction framework for image captioning, composed of multiple decoders each of which operates on the output of the previous stage, producing increasingly refined image descriptions, which simultaneously solves the well-known exposure bias problem and the loss-evaluation mismatch problem.
- SelfDoc: Self-Supervised Document Representation Learning187
This work proposes SelfDoc, a task-agnostic pre-training framework for document image understanding that benefits from self-supervised pre- training on documents without requiring annotations by a feature masking training strategy, and proposes a novel modality-adaptive attention mechanism for multimodal feature fusion by adaptively emphasizing language and vision signals.
- An Empirical Study of Language CNN for Image Captioning150
This paper introduces a language CNN model which is suitable for statistical language modeling tasks and shows competitive performance in image captioning, and is competitive with the state-of-the-art methods.
- Towards Interpreting and Mitigating Shortcut Learning Behavior of NLU models125
This work shows that the words in the NLU training set can be modeled as a long-tailed distribution, and proposes a shortcut mitigation framework LGTR, to suppress the model from making overconfident predictions for samples with large shortcut degree.
- Open-Vocabulary Instance Segmentation via Robust Cross-Modal Pseudo-Labeling119
This work proposes a cross-modal pseudo-labeling framework, which generates training pseudo masks by aligning word semantics in captions with visual features of object masks in images and designs a robust student model that selectively distills mask knowledge by estimating the mask noise levels, hence mitigating the adverse impact of noisy pseudo masks.
- High Quality Entity Segmentation115
CropFormer is the first query-based Transformer architecture that can effectively fuse mask predictions from multiple image views, by learning queries that effectively associate the same entities across the full image and its crop.
- Open World Entity Segmentation103
This work proposes a CondInst-like fully-convolutional architecture with two novel modules specifically designed to exploit the class-agnostic and non-overlapping requirements of ES, and investigates the feasibility of convolutional center-based representation to segment things and stuffs in a unified manner.
- Unpaired Image Captioning by Language Pivoting91
This work can effectively capture the characteristics of an image captioner from the pivot language (Chinese) and align it to the target language (English) using another pivot-target (Chinese-English) parallel corpus.
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- Finding It at Another Side: A Viewpoint-Adapted Matching Encoder for Change Captioning79
A novel visual encoder is proposed to explicitly distinguish viewpoint changes from semantic changes in the change captioning task and further simulate the attention preference of humans and proposes a novel reinforcement learning process to fine-tune the attention directly with language evaluation rewards.
- Multi-Scale Aligned Distillation for Low-Resolution Detection74
The idea of spatially aligning feature maps between models of varying input resolutions by shifting feature pyramid position is explored and crossed feature-level fusion is proposed to dynamically fuse teacher’s multi-resolution features to guide the student better.
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- Resilient Load Restoration in Microgrids Considering Mobile Energy Storage Fleets: A Deep Reinforcement Learning Approach56
A Markov decision process (MDP) formulation for an integrated service restoration strategy that coordinates the scheduling of MESSs and resource dispatching of microgrids and simulation results indicate that mobile and stationary energy resources can be well coordinated to improve system resilience.
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- Watch It Twice29
A novel recurrent video encoding method and a novel visual spatial feature for the video captioning task that encodes the video twice with a predicted key frame to avoid irrelevant temporal information often occurring at the beginning and at the end of a video.
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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 2026-10-13. 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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