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- Learning deep structured semantic models for web search using clickthrough data2,323
A series of new latent semantic models with a deep structure that project queries and documents into a common low-dimensional space where the relevance of a document given a query is readily computed as the distance between them are developed.
- Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks2,301
This paper proposes a new learning method Oscar (Object-Semantics Aligned Pre-training), which uses object tags detected in images as anchor points to significantly ease the learning of alignments.
- MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition2,241
A benchmark task to recognize one million celebrities from their face images, by using all the possibly collected face images of this individual on the web as training data, which could lead to one of the largest classification problems in computer vision.
- LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day2,195
This paper proposes a cost-efficient approach for training a vision-language conversational assistant that can answer open-ended research questions of biomedical images, and releases instruction-following data and the LLaVA-Med model, which exhibits excellent multimodal conversational capability.
- Stacked Attention Networks for Image Question Answering2,088
A multiple-layer SAN is developed in which an image is queried multiple times to infer the answer progressively, and the progress that the SAN locates the relevant visual clues that lead to the answer of the question layer-by-layer.
- Grounded Language-Image Pre-training1,831
A grounded language-image pretraining model for learning object-level, language-aware, and semantic-rich visual representations that unifies object detection and phrase grounding for pre-training and can leverage massive image-text pairs by generating grounding boxes in a self-training fashion.
- From captions to visual concepts and back1,363
This paper uses multiple instance learning to train visual detectors for words that commonly occur in captions, including many different parts of speech such as nouns, verbs, and adjectives, and develops a maximum-entropy language model.
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- A Persona-Based Neural Conversation Model1,122
This work presents persona-based models for handling the issue of speaker consistency in neural response generation that yield qualitative performance improvements in both perplexity and BLEU scores over baseline sequence-to-sequence models.
- Unified Vision-Language Pre-Training for Image Captioning and VQA1,084
VLP is the first reported model that achieves state-of-the-art results on both vision-language generation and understanding tasks, as disparate as image captioning and visual question answering, across three challenging benchmark datasets: COCO Captions, Flickr30k Captions and VQA 2.0.
- GLIGEN: Open-Set Grounded Text-to-Image Generation1,027
A novel approach that builds upon and extends the functionality of existing pre-trained text-to-image diffusion models by enabling them to also be conditioned on grounding inputs, and achieves open-world grounded text2img generation with caption and bounding box condition inputs.
- RegionCLIP: Region-based Language-Image Pretraining1,000
A new method is proposed that signifi-cantly extends CLIP to learn region-level visual representations, thus enabling fine-grained alignment between image regions and textual concepts, and the learned region representations support zero-shot inference for object detection, showing promising results on both COCO and LVIS datasets.
- Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models734
This paper presents a comprehensive review of the model's background, related technologies, applications, remaining challenges, and future directions of text-to-video AI models.
- Reinforced Cross-Modal Matching and Self-Supervised Imitation Learning for Vision-Language Navigation660
A novel Reinforced Cross-Modal Matching (RCM) approach that enforces cross-modal grounding both locally and globally via reinforcement learning (RL), and a Self-Supervised Imitation Learning (SIL) method to explore unseen environments by imitating its own past, good decisions is introduced.
- 600
- Domain Adaptation via Pseudo In-Domain Data Selection584
The results show that more training data is not always better, and that best results are attained via proper domain-relevant data selection, as well as combining in- and general-domain systems during decoding.
- Adapting boosting for information retrieval measures575
This work presents a new ranking algorithm that combines the strengths of two previous methods: boosted tree classification, and LambdaRank, and shows how to find the optimal linear combination for any two rankers, and uses this method to solve the line search problem exactly during boosting.
- Semantic Compositional Networks for Visual Captioning485
Experimental results show that the proposed method significantly outperforms prior state-of-the-art approaches, across multiple evaluation metrics.
- Selecting good expansion terms for pseudo-relevance feedback480
This study re-examines the assumption that most frequent terms in the pseudo-feedback documents are useful for the retrieval and proposes to integrate a term classification process to predict the usefulness of expansion terms.
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- Multi-Scale Vision Longformer: A New Vision Transformer for High-Resolution Image Encoding405
A comprehensive empirical study shows that the new ViT significantly outperforms several strong baselines, including the existing ViT models and their ResNet counterparts, and the Pyramid Vision Transformer from a concurrent work, on a range of vision tasks, including image classification, object detection, and segmentation.
- Generalized Decoding for Pixel, Image, and Language404
X-Decoder is the first work that provides a unified way to support all types of image segmentation and a variety of vision-language (VL) tasks with strong transferability to a wide range of downstream tasks in both zero-shot and finetuning settings.
- Towards Learning a Generic Agent for Vision-and-Language Navigation via Pre-Training388
This paper presents the first pre-training and fine-tuning paradigm for vision-and-language navigation (VLN) tasks, which leads to significant improvement over existing methods, achieving a new state of the art.
- TraceVLA: Visual Trace Prompting Enhances Spatial-Temporal Awareness for Generalist Robotic Policies353
A compact VLA model based on 4B Phi-3-Vision, pretrained on the Open-X-Embodiment and finetuned on the authors' dataset, rivals the 7B OpenVLA baseline while significantly improving inference efficiency.
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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 2026-10-12. 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.
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