pwc-archive/papers-with-abstracts huggingface dataset:
Semantic Segmentation of Transparent and Opaque Drinking Glasses with the Help of Zero-shot Learning
Authors: Annalena Blänsdorf, Tristan Wirth, Arne Rak, Thomas Pöllabauer, Volker Knauthe, Arjan Kuijper
Date: 2025-03-19 00:00:00
Tasks: Segmentation, Semantic Segmentation, Transparent objects, Zero-Shot Learning

Segmenting transparent structures in images is challenging since they are difficult to distinguish from the background. Common examples are drinking glasses, which are a ubiquitous part of our lives and appear in many different shapes and sizes. In this work we propose TransCaGNet, a modified version of the zero-shot model CaGNet. We exchange the segmentation backbone with the architecture of Trans4Trans to be capable of segmenting transparent objects. Since some glasses are rarely captured, we use zeroshot learning to be able to create semantic segmentations of glass categories not given during training. We propose a novel synthetic dataset covering a diverse set of different environmental conditions. Additionally we capture a real-world evaluation dataset since most applications take place in the real world. Comparing our model with Zeg-Clip we are able to show that TransCaGNet produces better mean IoU and accuracy values while ZegClip outperforms it mostly for unseen classes. To improve the segmentation results, we combine the semantic segmentation of the models with the segmentation results of SAM 2. Our evaluation emphasizes that distinguishing between different classes is challenging for the models due to similarity, points of view, or coverings. Taking this behavior into account, we assign glasses multiple possible categories. The modification leads to an improvement up to 13.68% for the mean IoU and up to 17.88% for the mean accuracy values on the synthetic dataset. Using our difficult synthetic dataset for training, the models produce even better results on the real-world dataset. The mean IoU is improved up to 5.55% and the mean accuracy up to 5.72% on the real-world dataset.
Standardizing Intelligence: Aligning Generative AI for Regulatory and Operational Compliance
Authors: Joseph Marvin Imperial, Matthew D. Jones, Harish Tayyar Madabushi
Date: 2025-02-03 00:00:00

Technical standards, or simply standards, are established documented guidelines and rules that facilitate the interoperability, quality, and accuracy of systems and processes. In recent years, we have witnessed an emerging paradigm shift where the adoption of generative AI (GenAI) models has increased tremendously, spreading implementation interests across standard-driven industries, including engineering, legal, healthcare, and education. In this paper, we assess the criticality levels of different standards across domains and sectors and complement them by grading the current compliance capabilities of state-of-the-art GenAI models. To support the discussion, we outline possible challenges and opportunities with integrating GenAI for standard compliance tasks while also providing actionable recommendations for entities involved with developing and using standards. Overall, we argue that aligning GenAI with standards through computational methods can help strengthen regulatory and operational compliance. We anticipate this area of research will play a central role in the management, oversight, and trustworthiness of larger, more powerful GenAI-based systems in the near future.
TULIP: Towards Unified Language-Image Pretraining
Authors: Zineng Tang, Long Lian, Seun Eisape, Xudong Wang, Roei Herzig, Adam Yala, Alane Suhr, Trevor Darrell, David M. Chan
Date: 2025-03-19 00:00:00
Tasks: Contrastive Learning, Data Augmentation, Depth Estimation, Object Recognition

Despite the recent success of image-text contrastive models like CLIP and SigLIP, these models often struggle with vision-centric tasks that demand high-fidelity image understanding, such as counting, depth estimation, and fine-grained object recognition. These models, by performing language alignment, tend to prioritize high-level semantics over visual understanding, weakening their image understanding. On the other hand, vision-focused models are great at processing visual information but struggle to understand language, limiting their flexibility for language-driven tasks. In this work, we introduce TULIP, an open-source, drop-in replacement for existing CLIP-like models. Our method leverages generative data augmentation, enhanced image-image and text-text contrastive learning, and image/text reconstruction regularization to learn fine-grained visual features while preserving global semantic alignment. Our approach, scaling to over 1B parameters, outperforms existing state-of-the-art (SOTA) models across multiple benchmarks, establishing a new SOTA zero-shot performance on ImageNet-1K, delivering up to a $2\times$ enhancement over SigLIP on RxRx1 in linear probing for few-shot classification, and improving vision-language models, achieving over $3\times$ higher scores than SigLIP on MMVP. Our code/checkpoints are available at https://tulip-berkeley.github.io
Boosting HDR Image Reconstruction via Semantic Knowledge Transfer
Authors: Qingsen Yan, Tao Hu, Genggeng Chen, Wei Dong, Yanning Zhang
Date: 2025-03-19 00:00:00
Tasks: HDR Reconstruction, Image Reconstruction, Transfer Learning

Recovering High Dynamic Range (HDR) images from multiple Low Dynamic Range (LDR) images becomes challenging when the LDR images exhibit noticeable degradation and missing content. Leveraging scene-specific semantic priors offers a promising solution for restoring heavily degraded regions. However, these priors are typically extracted from sRGB Standard Dynamic Range (SDR) images, the domain/format gap poses a significant challenge when applying it to HDR imaging. To address this issue, we propose a general framework that transfers semantic knowledge derived from SDR domain via self-distillation to boost existing HDR reconstruction. Specifically, the proposed framework first introduces the Semantic Priors Guided Reconstruction Model (SPGRM), which leverages SDR image semantic knowledge to address ill-posed problems in the initial HDR reconstruction results. Subsequently, we leverage a self-distillation mechanism that constrains the color and content information with semantic knowledge, aligning the external outputs between the baseline and SPGRM. Furthermore, to transfer the semantic knowledge of the internal features, we utilize a semantic knowledge alignment module (SKAM) to fill the missing semantic contents with the complementary masks. Extensive experiments demonstrate that our method can significantly improve the HDR imaging quality of existing methods.
SemEval-2025 Task 1: AdMIRe -- Advancing Multimodal Idiomaticity Representation
Authors: Thomas Pickard, Aline Villavicencio, Maggie Mi, wei he, Dylan Phelps, Marco Idiart
Date: 2025-03-19 00:00:00
Tasks: Mixture-of-Experts

Idiomatic expressions present a unique challenge in NLP, as their meanings are often not directly inferable from their constituent words. Despite recent advancements in Large Language Models (LLMs), idiomaticity remains a significant obstacle to robust semantic representation. We present datasets and tasks for SemEval-2025 Task 1: AdMiRe (Advancing Multimodal Idiomaticity Representation), which challenges the community to assess and improve models' ability to interpret idiomatic expressions in multimodal contexts and in multiple languages. Participants competed in two subtasks: ranking images based on their alignment with idiomatic or literal meanings, and predicting the next image in a sequence. The most effective methods achieved human-level performance by leveraging pretrained LLMs and vision-language models in mixture-of-experts settings, with multiple queries used to smooth over the weaknesses in these models' representations of idiomaticity.
Recover and Match: Open-Vocabulary Multi-Label Recognition through Knowledge-Constrained Optimal Transport
Authors: Hao Tan, Zichang Tan, Jun Li, Ajian Liu, Jun Wan, Zhen Lei
Date: 2025-03-19 00:00:00
Proceeding: CVPR 2025 1

Identifying multiple novel classes in an image, known as open-vocabulary multi-label recognition, is a challenging task in computer vision. Recent studies explore the transfer of powerful vision-language models such as CLIP. However, these approaches face two critical challenges: (1) The local semantics of CLIP are disrupted due to its global pre-training objectives, resulting in unreliable regional predictions. (2) The matching property between image regions and candidate labels has been neglected, relying instead on naive feature aggregation such as average pooling, which leads to spurious predictions from irrelevant regions. In this paper, we present RAM (Recover And Match), a novel framework that effectively addresses the above issues. To tackle the first problem, we propose Ladder Local Adapter (LLA) to enforce refocusing on local regions, recovering local semantics in a memory-friendly way. For the second issue, we propose Knowledge-Constrained Optimal Transport (KCOT) to suppress meaningless matching to non-GT labels by formulating the task as an optimal transport problem. As a result, RAM achieves state-of-the-art performance on various datasets from three distinct domains, and shows great potential to boost the existing methods. Code: https://github.com/EricTan7/RAM.
DCA: Dividing and Conquering Amnesia in Incremental Object Detection
Authors: Aoting Zhang, Dongbao Yang, Chang Liu, Xiaopeng Hong, Miao Shang, Yu Zhou
Date: 2025-03-19 00:00:00
Tasks: Knowledge Distillation, object-detection, Object Detection

Incremental object detection (IOD) aims to cultivate an object detector that can continuously localize and recognize novel classes while preserving its performance on previous classes. Existing methods achieve certain success by improving knowledge distillation and exemplar replay for transformer-based detection frameworks, but the intrinsic forgetting mechanisms remain underexplored. In this paper, we dive into the cause of forgetting and discover forgetting imbalance between localization and recognition in transformer-based IOD, which means that localization is less-forgetting and can generalize to future classes, whereas catastrophic forgetting occurs primarily on recognition. Based on these insights, we propose a Divide-and-Conquer Amnesia (DCA) strategy, which redesigns the transformer-based IOD into a localization-then-recognition process. DCA can well maintain and transfer the localization ability, leaving decoupled fragile recognition to be specially conquered. To reduce feature drift in recognition, we leverage semantic knowledge encoded in pre-trained language models to anchor class representations within a unified feature space across incremental tasks. This involves designing a duplex classifier fusion and embedding class semantic features into the recognition decoding process in the form of queries. Extensive experiments validate that our approach achieves state-of-the-art performance, especially for long-term incremental scenarios. For example, under the four-step setting on MS-COCO, our DCA strategy significantly improves the final AP by 6.9%.
LEGION: Learning to Ground and Explain for Synthetic Image Detection
Authors: Hengrui Kang, Siwei Wen, Zichen Wen, Junyan Ye, Weijia Li, Peilin Feng, Baichuan Zhou, Bin Wang, Dahua Lin, Linfeng Zhang, Conghui He
Date: 2025-03-19 00:00:00
Tasks: Artifact Detection, Image Manipulation, Image Manipulation Detection, Large Language Model, Multimodal Large Language Model, Synthetic Image Detection

The rapid advancements in generative technology have emerged as a double-edged sword. While offering powerful tools that enhance convenience, they also pose significant social concerns. As defenders, current synthetic image detection methods often lack artifact-level textual interpretability and are overly focused on image manipulation detection, and current datasets usually suffer from outdated generators and a lack of fine-grained annotations. In this paper, we introduce SynthScars, a high-quality and diverse dataset consisting of 12,236 fully synthetic images with human-expert annotations. It features 4 distinct image content types, 3 categories of artifacts, and fine-grained annotations covering pixel-level segmentation, detailed textual explanations, and artifact category labels. Furthermore, we propose LEGION (LEarning to Ground and explain for Synthetic Image detectiON), a multimodal large language model (MLLM)-based image forgery analysis framework that integrates artifact detection, segmentation, and explanation. Building upon this capability, we further explore LEGION as a controller, integrating it into image refinement pipelines to guide the generation of higher-quality and more realistic images. Extensive experiments show that LEGION outperforms existing methods across multiple benchmarks, particularly surpassing the second-best traditional expert on SynthScars by 3.31% in mIoU and 7.75% in F1 score. Moreover, the refined images generated under its guidance exhibit stronger alignment with human preferences. The code, model, and dataset will be released.
DEPT: Deep Extreme Point Tracing for Ultrasound Image Segmentation
Authors: Lei Shi, Xi Fang, Naiyu Wang, Junxing Zhang
Date: 2025-03-19 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, Semantic Segmentation, Weakly-supervised Learning

Automatic medical image segmentation plays a crucial role in computer aided diagnosis. However, fully supervised learning approaches often require extensive and labor-intensive annotation efforts. To address this challenge, weakly supervised learning methods, particularly those using extreme points as supervisory signals, have the potential to offer an effective solution. In this paper, we introduce Deep Extreme Point Tracing (DEPT) integrated with Feature-Guided Extreme Point Masking (FGEPM) algorithm for ultrasound image segmentation. Notably, our method generates pseudo labels by identifying the lowest-cost path that connects all extreme points on the feature map-based cost matrix. Additionally, an iterative training strategy is proposed to refine pseudo labels progressively, enabling continuous network improvement. Experimental results on two public datasets demonstrate the effectiveness of our proposed method. The performance of our method approaches that of the fully supervised method and outperforms several existing weakly supervised methods.
CoE: Chain-of-Explanation via Automatic Visual Concept Circuit Description and Polysemanticity Quantification
Authors: Wenlong Yu, Qilong Wang, Chuang Liu, Dong Li, QinGhua Hu
Date: 2025-03-19 00:00:00
Proceeding: CVPR 2025 1
Tasks: Disentanglement

Explainability is a critical factor influencing the wide deployment of deep vision models (DVMs). Concept-based post-hoc explanation methods can provide both global and local insights into model decisions. However, current methods in this field face challenges in that they are inflexible to automatically construct accurate and sufficient linguistic explanations for global concepts and local circuits. Particularly, the intrinsic polysemanticity in semantic Visual Concepts (VCs) impedes the interpretability of concepts and DVMs, which is underestimated severely. In this paper, we propose a Chain-of-Explanation (CoE) approach to address these issues. Specifically, CoE automates the decoding and description of VCs to construct global concept explanation datasets. Further, to alleviate the effect of polysemanticity on model explainability, we design a concept polysemanticity disentanglement and filtering mechanism to distinguish the most contextually relevant concept atoms. Besides, a Concept Polysemanticity Entropy (CPE), as a measure of model interpretability, is formulated to quantify the degree of concept uncertainty. The modeling of deterministic concepts is upgraded to uncertain concept atom distributions. Finally, CoE automatically enables linguistic local explanations of the decision-making process of DVMs by tracing the concept circuit. GPT-4o and human-based experiments demonstrate the effectiveness of CPE and the superiority of CoE, achieving an average absolute improvement of 36% in terms of explainability scores.
When the Future Becomes the Past: Taming Temporal Correspondence for Self-supervised Video Representation Learning
Authors: Yang Liu, Qianqian Xu, Peisong Wen, Siran Dai, Qingming Huang
Date: 2025-03-19 00:00:00
Proceeding: CVPR 2025 1
Tasks: Representation Learning, Self-Supervised Learning

The past decade has witnessed notable achievements in self-supervised learning for video tasks. Recent efforts typically adopt the Masked Video Modeling (MVM) paradigm, leading to significant progress on multiple video tasks. However, two critical challenges remain: 1) Without human annotations, the random temporal sampling introduces uncertainty, increasing the difficulty of model training. 2) Previous MVM methods primarily recover the masked patches in the pixel space, leading to insufficient information compression for downstream tasks. To address these challenges jointly, we propose a self-supervised framework that leverages Temporal Correspondence for video Representation learning (T-CoRe). For challenge 1), we propose a sandwich sampling strategy that selects two auxiliary frames to reduce reconstruction uncertainty in a two-side-squeezing manner. Addressing challenge 2), we introduce an auxiliary branch into a self-distillation architecture to restore representations in the latent space, generating high-level semantic representations enriched with temporal information. Experiments of T-CoRe consistently present superior performance across several downstream tasks, demonstrating its effectiveness for video representation learning. The code is available at https://github.com/yafeng19/T-CORE.
A Data-driven Investigation of Euphemistic Language: Comparing the usage of "slave" and "servant" in 19th century US newspapers
Authors: Jaihyun Park, Ryan Cordell
Date: 2025-03-19 00:00:00
Tasks: Optical Character Recognition (OCR)

This study investigates the usage of "slave" and "servant" in the 19th century US newspapers using computational methods. While both terms were used to refer to enslaved African Americans, they were used in distinct ways. In the Chronicling America corpus, we included possible OCR errors by using FastText embedding and excluded text reprints to consider text reprint culture in the 19th century. Word2vec embedding was used to find semantically close words to "slave" and "servant" and log-odds ratio was calculated to identify over-represented discourse words in the Southern and Northern newspapers. We found that "slave" is associated with socio-economic, legal, and administrative words, however, "servant" is linked to religious words in the Northern newspapers while Southern newspapers associated "servant" with domestic and familial words. We further found that slave discourse words in Southern newspapers are more prevalent in Northern newspapers while servant discourse words from each side are prevalent in their own region. This study contributes to the understanding of how newspapers created different discourses around enslaved African Americans in the 19th century US.
Multivariate Gaussian Topic Modelling: A novel approach to discover topics with greater semantic coherence
Authors: Satyajeet Sahoo, Jhareswar Maiti, Virendra Kumar Tewari
Date: 2025-03-19 00:00:00
Tasks: Information Retrieval, Topic Models

An important aspect of text mining involves information retrieval in form of discovery of semantic themes (topics) from documents using topic modelling. While generative topic models like Latent Dirichlet Allocation (LDA) elegantly model topics as probability distributions and are useful in identifying latent topics from large document corpora with minimal supervision, they suffer from difficulty in topic interpretability and reduced performance in shorter texts. Here we propose a novel Multivariate Gaussian Topic modelling (MGD) approach. In this approach topics are presented as Multivariate Gaussian Distributions and documents as Gaussian Mixture Models. Using EM algorithm, the various constituent Multivariate Gaussian Distributions and their corresponding parameters are identified. Analysis of the parameters helps identify the keywords having the highest variance and mean contributions to the topic, and from these key-words topic annotations are carried out. This approach is first applied on a synthetic dataset to demonstrate the interpretability benefits vis-\`a-vis LDA. A real-world application of this topic model is demonstrated in analysis of risks and hazards at a petrochemical plant by applying the model on safety incident reports to identify the major latent hazards plaguing the plant. This model achieves a higher mean topic coherence of 0.436 vis-\`a-vis 0.294 for LDA.
Forensics-Bench: A Comprehensive Forgery Detection Benchmark Suite for Large Vision Language Models
Authors: Jin Wang, Chenghui Lv, Xian Li, Shichao Dong, Huadong Li, Kelu Yao, Chao Li, Wenqi Shao, Ping Luo
Date: 2025-03-19 00:00:00
Proceeding: CVPR 2025 1

Recently, the rapid development of AIGC has significantly boosted the diversities of fake media spread in the Internet, posing unprecedented threats to social security, politics, law, and etc. To detect the ever-increasingly diverse malicious fake media in the new era of AIGC, recent studies have proposed to exploit Large Vision Language Models (LVLMs) to design robust forgery detectors due to their impressive performance on a wide range of multimodal tasks. However, it still lacks a comprehensive benchmark designed to comprehensively assess LVLMs' discerning capabilities on forgery media. To fill this gap, we present Forensics-Bench, a new forgery detection evaluation benchmark suite to assess LVLMs across massive forgery detection tasks, requiring comprehensive recognition, location and reasoning capabilities on diverse forgeries. Forensics-Bench comprises 63,292 meticulously curated multi-choice visual questions, covering 112 unique forgery detection types from 5 perspectives: forgery semantics, forgery modalities, forgery tasks, forgery types and forgery models. We conduct thorough evaluations on 22 open-sourced LVLMs and 3 proprietary models GPT-4o, Gemini 1.5 Pro, and Claude 3.5 Sonnet, highlighting the significant challenges of comprehensive forgery detection posed by Forensics-Bench. We anticipate that Forensics-Bench will motivate the community to advance the frontier of LVLMs, striving for all-around forgery detectors in the era of AIGC. The deliverables will be updated at https://Forensics-Bench.github.io/.
MAMM-Refine: A Recipe for Improving Faithfulness in Generation with Multi-Agent Collaboration
Authors: David Wan, Justin Chih-Yao Chen, Elias Stengel-Eskin, Mohit Bansal
Date: 2025-03-19 00:00:00
Tasks: Long Form Question Answering, Question Answering, Reranking

Multi-agent collaboration among models has shown promise in reasoning tasks but is underexplored in long-form generation tasks like summarization and question-answering. We extend multi-agent multi-model reasoning to generation, specifically to improving faithfulness through refinement, i.e., revising model-generated outputs to remove factual inconsistencies. We investigate how iterative collaboration among multiple instances and types of large language models (LLMs) enhances subtasks in the refinement process, such as error detection, critiquing unfaithful sentences, and making corrections based on critiques. We design intrinsic evaluations for each subtask, with our findings indicating that both multi-agent (multiple instances) and multi-model (diverse LLM types) approaches benefit error detection and critiquing. Additionally, reframing critiquing and refinement as reranking rather than generation tasks improves multi-agent performance. We consolidate these insights into a final "recipe" called Multi-Agent Multi-Model Refinement (MAMM-Refine), where multi-agent and multi-model collaboration significantly boosts performance on three summarization datasets as well as on long-form question answering, demonstrating the effectiveness and generalizability of our recipe.