O ut O f D istribution Generalization in C omputer V ision
in conjunction with ECCV 2024
Semantic Shifts Benchmark
The Semantic Shift Benchmark (SSB) challenge focuses on open-set recognition and the generalized category discovery problem.
The SSB benchmark can be accessed from [here] or [here].
For an understanding of the generalized category discovery problem, the readers can refer to the works [here] and [here].
Track-1: Open-Set Recognition: This track evaluates the ability of the model to identify open-set examples. This track has only one leaderboard, only models that are not trained on ImageNet-22k can be submitted.
The ranking will be determined based on the average rank of FPR and AUROC.
A baseline is provided [here].
Track-2: Generalized Category Discovery: This track evaluates the ability of the model to discover and recognize novel concepts within an unlabeled dataset. This track has only one leaderboard for any self-supervised pretrained models.
The ranking will be determined based on the average clustering accuracy on all three datasets in the FGVC dataset from SSB benchmark.
We provide a baseline for GCD [here].
The UNICORN Benchmark
The UNICORN benchmark focuses on the robostness and safety evaluation of multi-modal large language models. The benchmark can be access from [here].
The UNICORN challenge has only one track, evaluting the model on both the OODCV-VQA benchmark and also the SketchyQA benchmark. The ranking will be determined by the average score of the two benchmarks. For the OOD evaluation, we have two novel VQA datasets --- OODCV-VQA and Sketchy-VQA, each with one variant (OODCV-Counterfactual with perturbated textual questions and Sketchy-Challenging with rarely seen sketchy objects), designed to test model performance under challenging conditions. In the OOD scenario, all questions are matched with boolean or numerical answers, and we use exact match accuracy for evaluation. The evaluation of various types of models on this benchmark can be found [here].