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Works165 from public data
- MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models3,392
MiniGPT-4 is presented, which aligns a frozen visual encoder with a frozen advanced LLM, Vicuna, using one projection layer to uncovers that properly aligning the visual features with an advanced large language model can possess numerous advanced multi-modal abilities demonstrated by G PT-4.
- Memory Aware Synapses: Learning What (not) to Forget2,207
This paper argues that, given the limited model capacity and the unlimited new information to be learned, knowledge has to be preserved or erased selectively and proposes a novel approach for lifelong learning, coined Memory Aware Synapses (MAS), which computes the importance of the parameters of a neural network in an unsupervised and online manner.
- Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction1,090
The Social Spatio-Temporal Graph Convolutional Neural Network (Social-STGCNN), which substitutes the need of aggregation methods by modeling the interactions as a graph, and is data efficient, and exceeds previous state of the art on the ADE metric with only 20% of the training data.
- MiniGPT-v2: large language model as a unified interface for vision-language multi-task learning759
This work introduces MiniGPT-v2, a model that can be treated as a unified interface for better handling various vision-language tasks and proposes using unique identifiers for different tasks when training the model to better distinguish each task instruction effortlessly.
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- ReferIt3D: Neural Listeners for Fine-Grained 3D Object Identification in Real-World Scenes527
This work designs an approach for combining linguistic and geometric information (in the form of 3D point clouds) and creating multi-modal (3D) neural listeners that can comprehend object-centric natural language and identify the referred object directly in a 3D scene.
- Continual learning with tiny episodic memories504
It is observed that a very simple baseline, which jointly trains on both examples from the current task as well as examples stored in the memory, outperforms state-of-the-art CL approaches with and without episodic memory.
- StyleGAN-V: A Continuous Video Generator with the Price, Image Quality and Perks of StyleGAN2405
This work rethink the traditional image + video discriminators pair and design a holistic dis-criminator that aggregates temporal information by simply concatenating frames' features, which decreases the training cost and provides richer learning signal to the generator, making it possible to train directly on 10242 videos for the first time.
- A Generative Adversarial Approach for Zero-Shot Learning from Noisy Texts403
This work proposes a simple yet effective generative model that takes as input noisy text descriptions about an unseen class and generates synthesized visual features for this class and shows that this method consistently outperforms the state of the art on the largest available benchmarks on Text-based Zero-shot Learning.
- VisualGPT: Data-efficient Adaptation of Pretrained Language Models for Image Captioning310
This work proposes VisualGPT, which employs a novel self-resurrecting encoder-decoder attention mechanism to quickly adapt the PLM with a small amount of in-domain image-text data and achieves the state-of-the-art result on IU X-ray, a medical report generation dataset.
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- ArtEmis: Affective Language for Visual Art206
A novel large-scale dataset and accompanying machine learning models aimed at providing a detailed understanding of the interplay between visual content, its emotional effect, and explanations for the latter in language are presented.
- Adversarial Generation of Continuous Images203
This paper proposes two novel architectural techniques for building INR-based image decoders: factorized multiplicative modulation and multi-scale INRs, and uses them to build a state-of-the-art continuous image GAN.
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- Large-Scale Visual Relationship Understanding176
A new relationship detection model is developed that embeds objects and relations into two vector spaces where both discriminative capability and semantic affinity are preserved and can achieve superior performance even when the visual entity categories scale up to more than 80,000, with extremely skewed class distribution.
- The Shape of Art History in the Eyes of the Machine148
A comprehensive study of several of the state-of-the-art convolutional neural networks applied to the task of style classification on 67K images of paintings and analyzed the learned representation through correlation analysis with concepts derived from art history found that there are a few underlying factors that explain the visual variations of style in art.
- Link the Head to the "Beak": Zero Shot Learning from Noisy Text Description at Part Precision145
A learning framework that is able to connect text terms to its relevant parts and suppress connections to non- visual text terms without any part-text annotations is proposed, which enables terms like beak to be sparsely linked to the visual representation of parts like head, while reduces the effect of non-visual terms like migrate on classifier prediction.
- DesIGN: Design Inspiration from Generative Networks139
This study designs and investigates different image generation models associated with different loss functions to boost creativity in fashion generation, and shows that the proposed creativity criterion yield better overall appreciation than the one employed in Creative Adversarial Networks.
- Learning Hypergraph-regularized Attribute Predictors136
A novel attribute learning framework named Hypergraph-based Attribute Predictor, which is casted as a regularized hypergraph cut problem, in which a collection of attribute projections is jointly learnt from the feature space to a hypergraph embedding space aligned with the attributes.
- ChatGPT Asks, BLIP-2 Answers: Automatic Questioning Towards Enriched Visual Descriptions135
This paper introduces ChatCaptioner, a novel automatic-questioning method deployed in image captioning that identifies 53% more objects within the image than BLIP-2 alone measured by WordNet synset matching.
- HRS-Bench: Holistic, Reliable and Scalable Benchmark for Text-to-Image Models128
HRS-Bench is introduced, a concrete evaluation benchmark for T2I models that is Holistic, Reliable, and Scalable and measures 13 skills that can be categorized into five major categories: accuracy, robustness, generalization, fairness, and bias.
- Aligning Latent and Image Spaces to Connect the Unconnectable103
This work develops a method to generate infinite high-resolution images with diverse and complex content based on a perfectly equivariant patch-wise generator with synchronous interpolations in the image and latent spaces and modify the AdaIN mechanism to work in such a setup.
- Video Object Segmentation using Teacher-Student Adaptation in a Human Robot Interaction (HRI) Setting88
A novel teacher-student learning paradigm to teach robots about their surrounding environment is proposed, which outperforms the state-of-theart on DAVIS and FBMS with 6.8% and 1.2% in F-measure respectively and improves over the baseline on IVOS dataset.
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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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