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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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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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