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- Learning 2D Temporal Adjacent Networks for Moment Localization with Natural Language640
A Temporal Adjacent Network (2D-TAN) is proposed, a single-shot framework for moment localization that is capable of encoding the adjacent temporal relation, while learning discriminative features for matching video moments with referring expressions.
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- Part-Aware Prototype Network for Few-Shot Semantic Segmentation424
A novel few-shot semantic segmentation framework based on the prototype representation, capable of capturing diverse and fine-grained object features, and a novel graph neural network model to generate and enhance the proposed part-aware prototypes based on labeled and unlabeled images.
- InternLM-XComposer2: Mastering Free-form Text-Image Composition and Comprehension in Vision-Language Large Model397
Experimental results demonstrate the superiority of InternLM-XComposer2 based on InternLM2-7B in producing high-quality long-text multi-modal content and its exceptional vision-language understanding performance across various benchmarks, where it not only significantly outperforms existing multimodal models but also matches or even surpasses GPT-4V and Gemini Pro in certain assessments.
- Distribution Alignment: A Unified Framework for Long-tail Visual Recognition376
This work develops an adaptive calibration function that enables us to adjust the classification scores for each data point in the two-stage learning to balance the class prior, which provides a flexible and unified solution to diverse scenarios in visual recognition tasks.
- InternLM-XComposer: A Vision-Language Large Model for Advanced Text-image Comprehension and Composition361
This work proposes InternLM-XComposer, a vision-language large model that enables advanced image-text comprehension and composition that achieves competitive text-image composition scores compared to public solutions, including GPT4-V and GPT3.5.
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- Bipartite Graph Network with Adaptive Message Passing for Unbiased Scene Graph Generation282
This work introduces a novel confidence-aware bipartite graph neural network with adaptive message propagation mechanism for unbiased scene graph generation and proposes an efficient bi-level data resampling strategy to alleviate the imbalanced data distribution problem in training the authors' graph network.
- InternLM-XComposer-2.5: A Versatile Large Vision Language Model Supporting Long-Contextual Input and Output216
A versatile large-vision language model that supports long-contextual input and output, InternLM-XComposer-2.5 excels in various text-image comprehension and composition applications, achieving GPT-4V level capabilities with merely 7B LLM backend.
- Boundary Proposal Network for Two-stage Natural Language Video Localization196
A novel Boundary Proposal Network (BPNet) is proposed, a universal two-stage framework that gets rid of the issues mentioned above and outperforms the state-of-the-art NLVL methods.
- SGTR: End-to-end Scene Graph Generation with Transformer193
A transformer-based end-to-end framework that first generates the entity and predicate proposal set, followed by inferring directed edges to form the relation triplets, and a new entity-aware predicate representation based on a structural predicate generator that leverages the compositional property of relationships is developed.
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- Latent-Shift: Latent Diffusion with Temporal Shift for Efficient Text-to-Video Generation152
This work proposes a parameter-free temporal shift module that can leverage the spatial U-Net as is for video generation and achieves comparable or better results while being significantly more efficient.
- The Devil is in the Labels: Noisy Label Correction for Robust Scene Graph Generation113
This paper proposes a novel model-agnostic NoIsy label CorrEction strategy for SGG: NICE, which can not only detect noisy samples but also reassign more high-quality predicate labels to them.
- T-Eval: Evaluating the Tool Utilization Capability of Large Language Models Step by Step112
T-Eval provides a more fine-grained analysis of the capabilities of LLMs, providing a new perspective in LLM evaluation on tool-utilization ability, and comprehensively decompose the tool utilization into multiple sub- processes, including instruction following, planning, reasoning, retrieval, understanding, and review.
- Dynamic Context Correspondence Network for Semantic Alignment98
This paper proposes a context-aware semantic representation that incorporates spatial layout for robust matching against local ambiguities and develops a novel dynamic fusion strategy based on attention mechanism to weave the advantages of both local and context features by integrating semantic cues from multiple scales.
- LawBench: Benchmarking Legal Knowledge of Large Language Models89
Using LawBench, the first evaluation benchmark composed of 20 tasks aimed to assess the ability of Large Language Models (LLMs) to perform Chinese legal-related tasks, this work presents a comprehensive evaluation of 21 popular LLMs and the first comparative analysis of the empirical results.
- From Pixels to Graphs: Open-Vocabulary Scene Graph Generation with Vision-Language Models81
A new open-vocabulary SGG framework based on sequence generation that leverages vision-language pre-trained models (VLM) by incorporating an image-to-graph generation paradigm and seamlessly integrate explicit relational modeling for enhancing the VL tasks.
- Exploiting Temporal Relationships in Video Moment Localization with Natural Language78
A novel Temporal Compositional Modular Network (TCMN) where a tree attention network first automatically decomposes a sentence into three descriptions with respect to the main event, context event and temporal signal is proposed.
- Multi-Scale 2D Temporal Adjacency Networks for Moment Localization With Natural Language75
A Multi-Scale Temporal Adjacency Network (MS-2D-TAN), a single-shot framework for moment localization capable of encoding the adjacent temporal contexts at each scale, while learning discriminative features for matching video moments with referring expressions is proposed.
- A Dual Attention Network with Semantic Embedding for Few-Shot Learning70
This work proposes a novel meta-learning method for few-shot classification based on two simple attention mechanisms: one is a spatial attention to localize relevant object regions and the other is a task attention to select similar training data for label prediction.
- NeedleBench: Evaluating LLM Retrieval and Reasoning Across Varying Information Densities67
The NeedleBench framework is used to assess how well the leading open-source models can identify key information relevant to the question and apply that information to reasoning in bilingual long texts, and the Ancestral Trace Challenge (ATC) is proposed to mimic the complexity of logical reasoning challenges that are likely to be present in real-world long-context tasks.
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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 2026-10-14. 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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