The paper introduces a diagnostic test for class sensitivity in saliency methods, revealing that many popular techniques produce nearly identical explanations regardless of the predicted class. This limitation appears across different architectures and datasets, indicating a structural issue. To address this, the authors propose CASE, a contrastive explanation method that isolates features uniquely discriminative for the predicted class, and demonstrate its improved fidelity and class specificity through experiments.
By Dane Williamson, Yangfeng Ji, Matthew Dwyer
arXiv:2411. 05698v3 Announce Type: replace-cross Abstract: Convolutional Neural Networks (CNNs) have shown remarkable performance in image classification.
By Antonio De Santis, Riccardo Campi, Matteo Bianchi, Marco Brambilla
arXiv:2608. 12299v1 Announce Type: cross Abstract: Class activation mapping (CAM) is one of the most widely used visual explanation families in explainable artificial intelligence.
By AmirHossein Eshghi, Hamid Saadatfar, Seyyed Ali Hoseini, AmirMohsen Eshghi, Siavash Arjomand Bigdel
The paper introduces DAN, a training‑free inference‑time framework that improves affective reasoning in multimodal large language models. It combines a Hierarchical Emotional Reasoning Chain (HERC) to better capture fine‑grained visual cues and a Contrastive Discriminative Visual Pruning (CDVP) module to isolate discriminative tokens for semantically similar emotions. Experiments show significant gains, notably a +10.47% improvement on the WebEmo25 benchmark with Qwen3‑VL‑8B‑Instruct.
By Cheng Ye, Weidong Chen, Zhaobo Qi, Beier Zhu, Zhendong Mao
The paper introduces ACVA-Bench, a benchmark designed to evaluate text‑to‑image models on their ability to visualize abstract concepts—those without fixed visual referents—across three dimensions: semantic sufficiency, representational diversity, and conceptual distinctiveness. It proposes a fine‑grained visual‑semantic evaluation method that decomposes concept semantics into verifiable questions, bypassing the need for reference images. Benchmark results reveal that current models tend to rely on a limited set of prototypical visual patterns, indicating room for improvement in diversity and distinctiveness.
By Mengyan Wang, Jiyuan An, Yining Liu, YaoWen Zhang, Liner Yang ang Erhong Yang
arXiv:2608.15404v2 Announce Type: replace
Abstract: Concept Bottleneck Models (CBMs) are designed to make visual classification interpretable by expressing predictions through human-understandable co...
By Yusuf Meric Karadag, Gulay Oklan, Seref Baris Cagliyan, Umut Ozdemir, Emre Akbas
Hide and Seek (HnS) is a concept‑level attribution method that interprets black‑box vision models without needing model weights or labeled concept datasets. It retrieves concept‑aligned images via a vision‑language model, localizes and masks the concept region, and measures attribution by observing changes in the model’s output. HnS was validated on CelebA and COCO‑Stuff, showing stronger agreement with ground‑truth attributions than existing methods, and was applied to tasks like image captioning and satellite image segmentation where other concept attribution methods fail.
By Bhanu Tokas, Rahul Nair, Rockwell Jackson, Hannah Kerner
arXiv:2606. 07180v1 Announce Type: cross Abstract: The growing demand for transparency in automated decision-making has propelled eXplainable Artificial Intelligence (XAI) to the forefront of machine learning research.
By Arthur Hoarau, Chenrui Zhu, Vu Linh Nguyen
arXiv:2607. 18695v1 Announce Type: cross Abstract: A popular route to interpretable zero-shot classification asks a large language model (LLM) to describe each class name and prompts CLIP with the resulting descriptors.
By Gautam Rajendrakumar Gare, Jia Shi, Zhiqiu Lin, Deepak Pathak, John Galeotti, Deva Ramanan
arXiv:2607. 14125v1 Announce Type: new Abstract: Pre-trained vision-language models (VLMs) enable zero-shot image classification by computing the similarity score between an image and textual descriptions, typically formed by inserting a class label (e.
By Ruijiang Dong, Zesheng Ye, Jianzhong Qi, Lei Feng, Feng Liu, Gang Niu, Masashi Sugiyama
arXiv:2601. 21944v3 Announce Type: replace Abstract: The widespread adoption of deep learning models in computer vision has intensified concerns about interpretability.
By Konstantinos P. Panousis, Diego Marcos
arXiv:2605. 28215v2 Announce Type: replace Abstract: In-context learning (ICL) enables multimodal large language models (MLLMs) to classify images from a few labelled examples.
By Carmen Quiles-Ram\'irez, Leticia L. Rodr\'iguez, Nicol\'as Martorell, Natalia D\'iaz-Rodr\'iguez