visual-question-answering-warmup

Model · Visual question answering

visual-question-answering-study

Samuel Moore

This is an exploratory note for Visual Question Answering. It records the intended comparison, likely confounders, and reproducibility requirements before any benchmark result is reported. - the scope of the research question and likely confounders - a proposed comparison with matched baselines - concrete evaluation context such as VQAv2, GQA, and OK-VQA - reproducibility checks, failure modes, and open questions - topic-relevant references Start with review.md for the full note. Sections labeled as plans or hypotheses should not be interpreted as experimental results. If results are added later, they should include dataset versions, commands, seeds, hardware, and raw logs. The note is…

Model · Visual question answering

SnapVL

akesh Kumar

1. Pre-Convergence Checkpoint: The metrics reported in this document were evaluated on an intermediate experimental checkpoint where training was paused early to analyze representation dynamics. These numbers do not represent fully converged final weights. 2. Closed-Schema Evaluation Protocol: Unlike generative vision-language models that decode answers token-by-token from an open vocabulary, SnapVL operates as a closed-schema decision classifier. Questions are framed either as Boolean verification (True/False) or multiple-choice candidate selection (N-choice). Direct numerical comparison with generative LLM decoders should take this protocol difference into account. 3. Scope: SnapVL is…

trained option-scoring head. It answers choice / noul / score questions about a document and up to four images in one forward pass, and speaks the Images need the vision extra (Pillow, torchvision): pip install "rsi-jev[vision] @ git+https://github.com/Shanghua-Gao/RSI-Jev". The vision encoder and image processor are read from the base model. This checkpoint (seed 17, one seed): Re-computed from these exact files after upload-time reload. Kernel stack fla-0.5.2/torch-2.7.1+cu128; full recipe in meta.json. loadrelease applies calibration.safetensors when the checkpoint carries one, so the probabilities are the calibrated ones this card reports. A loader that skips those files returns the raw…

Model · Visual question answering

vlm-calibration-prefix-vqav2

Bhaskar C

A small non-autoregressive yes/no visual question answering model built for research on calibrated decisions. It answers a yes/no question about an image in one forward pass, with no generation and no verbalised confidence. The confidence is the softmax over two token logits followed by a single post-hoc temperature. - Image. A frozen SigLIP2-base encodes the image into its pooled vector (pooleroutput, 768-d). - Prefix token. That vector becomes one prefix token, prepended to the input embeddings of ModernBERT-base: - anchor is a learned token, initialised from the " image" embedding. - alpha is a scalar gate, initialised to 0, so the model starts as exactly the text-only model. - gmax caps…

https://www.doi.org/10.1007/s10694-026-02000-3 Existing vision-based methods suffer from high false alarm rates in urban flame detection. Applying Multimodal Large Language Models (MLLMs) for secondary filtering shows great potential in reducing false alarms, yet they have high inference latency and are prone to reasoning collapse on negative samples without explicit Chain-of-Thought (CoT) guidance. To overcome these challenges, this study proposed Flash-Cascade, the first sub-second MLLM-based firewall to leverage CoT to efficiently filter false alarms. We deconstructed the flame detection process into four logical stages (planning, observation, analysis, and judgment), which informed the…

https://doi.org/10.1007/s10694-026-02000-3 Existing vision-based methods suffer from high false alarm rates in urban flame detection. Applying Multimodal Large Language Models (MLLMs) for secondary filtering shows great potential in reducing false alarms, yet they have high inference latency and are prone to reasoning collapse on negative samples without explicit Chain-of-Thought (CoT) guidance. To overcome these challenges, this study proposed Flash-Cascade, the first sub-second MLLM-based firewall to leverage CoT to efficiently filter false alarms. We deconstructed the flame detection process into four logical stages (planning, observation, analysis, and judgment), which informed the…