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- A Discriminative Feature Learning Approach for Deep Face Recognition4,103
This paper proposes a new supervision signal, called center loss, for face recognition task, which simultaneously learns a center for deep features of each class and penalizes the distances between the deep features and their corresponding class centers.
- OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models539
An Omnidirectionally calibrated Quantization technique for LLMs is introduced, which achieves good performance in diverse quantization settings while maintaining the computational efficiency of PTQ by efficiently optimizing various quantization parameters.
- MMT-Bench: A Comprehensive Multimodal Benchmark for Evaluating Large Vision-Language Models Towards Multitask AGI217
This study presents MMT-Bench, a comprehensive benchmark designed to assess LVLMs across massive multimodal tasks requiring expert knowledge and deliberate visual recognition, localization, reasoning, and planning, and hopes that MMT-Bench will inspire the community to develop next-generation multimodal foundation models aimed at achieving general-purpose multimodal intelligence.
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- Towards Lossless Dataset Distillation via Difficulty-Aligned Trajectory Matching165
This work proposes to align the difficulty of the generated patterns with the size of the synthetic dataset, and successfully scale trajectory matching-based methods to larger synthetic datasets, achieving lossless dataset distillation for the first time.
- Super-Identity Convolutional Neural Network for Face Hallucination154
This paper defines a super-identity loss to measure the identity difference between a hallucinated face and its corresponding high-resolution face within the hypersphere identity metric space and presents a domain-integrated training approach by constructing a robust identity metric for faces from these two domains.
- A Comprehensive Study on Center Loss for Deep Face Recognition102
This paper addresses the open-set property of face recognition by developing the center loss, which simultaneously learns a center for each class, and penalizes the distances between the deep features of the face images and their corresponding class centers.
- ChartAssisstant: A Universal Chart Multimodal Language Model via Chart-to-Table Pre-training and Multitask Instruction Tuning99
ChartAssistant, a chart-based vision-language model for universal chart comprehension and reasoning, is proposed, which leverages ChartSFT, a comprehensive dataset covering diverse chart-related tasks with basic and specialized chart types.
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- Group emotion recognition with individual facial emotion CNNs and global image based CNNs74
This paper presents the approach for group-level emotion recognition in the Emotion Recognition in the Wild Challenge 2017, based on two types of Convolutional Neural Networks, namely individual facial emotion CNNs and global image based CNNs.
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- BESA: Pruning Large Language Models with Blockwise Parameter-Efficient Sparsity Allocation62
A novel LLM pruning technique dubbed blockwise parameter-efficient sparsity allocation (BESA) by applying a blockwise reconstruction loss, characterized by two distinctive attributes: i) it targets the overall pruning error with respect to individual transformer blocks, and ii) it allocates layer-specific sparsity in a differentiable manner, both of which ensure reduced performance degradation after pruning.
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- Cascade Attention Networks For Group Emotion Recognition with Face, Body and Image Cues44
This paper presents the approach for group-level emotion recognition sub-challenge in the EmotiW 2018 and proposes a cascade attention network for the face cue in images to generate a global representation based on all faces.
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- B-AVIBench: Toward Evaluating the Robustness of Large Vision-Language Model on Black-Box Adversarial Visual-Instructions40
B-AVIBench is introduced, a framework designed to analyze the robustness of LVLMs when facing various Black-box Adversarial Visual-Instructions (B-AVIs), including four types of image-based B-AVIs, ten types of text-based B-AVIs, and nine types of content bias B-AVIs (such as gender, violence, cultural, and racial biases, among others).
- OpenING: A Comprehensive Benchmark for Judging Open-ended Interleaved Image-Text Generation36
OpenING, a comprehensive benchmark comprising 5,400 high-quality human-annotated instances across 56 real-world tasks, is introduced and IntJudge, a judge model for evaluating open-ended multimodal generation methods, is presented.
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- Dynamic Multimodal Evaluation with Flexible Complexity by Vision-Language Bootstrapping30
VLB dynamically generates new visual question-answering samples through a multimodal bootstrapping module that modifies both images and language, while ensuring that newly generated samples remain consistent with the original ones by a judge module.
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- SearchLVLMs: A Plug-and-Play Framework for Augmenting Large Vision-Language Models by Searching Up-to-Date Internet Knowledge26
This paper proposes a plug-and-play framework, for augmenting existing LVLMs in handling visual question answering (VQA) about up-to-date knowledge, dubbed SearchLVLMs, where a hierarchical filtering model is trained to effectively and efficiently find the most helpful content from the websites returned by a search engine to prompt LVLMs with up-to-date knowledge.
- Foundation Model is Efficient Multimodal Multitask Model Selector26
An efficient multi-task model selector (EMMS), which employs large-scale foundation models to transform diverse label formats such as categories, texts, and bounding boxes of different downstream tasks into a unified noisy label embedding, making it the first model selection method in the multi- task scenario.
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- ConvBench: A Multi-Turn Conversation Evaluation Benchmark with Hierarchical Capability for Large Vision-Language Models25
ConvBench is presented, a novel multi-turn conversation evaluation benchmark tailored for Large Vision-Language Models (LVLMs) that adopts a three-level multimodal capability hierarchy, mimicking human cognitive processes by stacking up perception, reasoning, and creativity.
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- Bootstrap Model Ensemble and Rank Loss for Engagement Intensity Regression25
This paper presents the approach for the engagement intensity regression task of EmotiW 2019, and uses the classical bootstrap aggregation method to perform model ensemble which randomly samples a certain training data by several times and then averages the model predictions.
- Open-Vocabulary Animal Keypoint Detection with Semantic-Feature Matching24
This work introduces the Open-Vocabulary Keypoint Detection (OVKD) task, which is innovatively designed to use text prompts for identifying arbitrary keypoints across any species, and develops a novel framework named Open-Vocabulary Keypoint Detection with Semantic-feature Matching (KDSM), which synergistically combines vision and language models.
- MLLMs-Augmented Visual-Language Representation Learning23
This work demonstrates that Multi-modal Large Language Models (MLLMs) can enhance visual-language representation learning by establishing richer image-text associations for image-text datasets.
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- LeX-Art: Rethinking Text Generation via Scalable High-Quality Data Synthesis18
This work builds a high-quality data synthesis pipeline based on Deepseek-R1 to curate LeX-10K, a dataset of 10K high-resolution, aesthetically refined 1024$\times$1024 images, and introduces LeX-Art, a comprehensive suite for high-quality text-image synthesis that systematically bridges the gap between prompt expressiveness and text rendering fidelity.
- Exploring Regularizations with Face, Body and Image Cues for Group Cohesion Prediction18
This paper elaborately design two regularizations, namely a rank loss and a hourglass loss, where the former aims to give a margin between the distance of distant categories and near categories and the later to avoid centralization predictions with only MSE loss.
- MDK12-Bench: A Multi-Discipline Benchmark for Evaluating Reasoning in Multimodal Large Language Models17
This work introduces MDK12-Bench, a multi-disciplinary benchmark assessing the reasoning capabilities of MLLMs via real-world K-12 examinations, and presents a novel dynamic evaluation framework to mitigate data contamination issues by bootstrapping question forms, question types, and image styles during evaluation.
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- Align, Adapt and Inject: Sound-guided Unified Image Generation10
This paper proposes a unified framework 'Align, Adapt, and Inject' (AAI), able to extract the dynamic information of varied sounds, while utilizing the formidable capability of existing T2I models to facilitate sound-guided image generation, editing, and stylization in a convenient and cost-effective manner.
- DREAM+: Efficient Dataset Distillation by Bidirectional Representative Matching9
A novel matching strategy called Dataset Distillation by Bidirectional REpresentAtive Matching (DREAM+), which selects representative original images for bidirectional matching, is proposed, which significantly reduces the number of distillation iterations by more than 15 times without affecting performance.
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- Adapting LLaMA Decoder to Vision Transformer6
The tailored model, dubbed as image LLaMA (iLLaMA), is akin to LLaMA in architecture and enables direct supervised learning, and boosts computational efficiency and learns complex representation by elevating attention map ranks.
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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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