AV-Reasoner: Improving and Benchmarking Clue-Grounded Audio-Visual
  Counting for MLLMs
Despite progress in video understanding, current MLLMs struggle with counting
tasks. Existing benchmarks are limited by short videos, close-set queries, lack
of clue annotations, and weak multimodal coverage. In this paper, we introduce
CG-AV-Counting, a manually-annotated clue-grounded counting benchmark with
1,027 multimodal questions and 5,845 annotated clues over 497 long videos. It
supports both black-box and white-box evaluation, serving as a comprehensive
testbed for both end-to-end and reasoning-based counting. To explore ways to
improve model's counting capability, we propose AV-Reasoner, a model trained
with GRPO and curriculum learning to generalize counting ability from related
tasks. AV-Reasoner achieves state-of-the-art results across multiple
benchmarks, demonstrating the effectiveness of reinforcement learning. However,
experiments show that on out-of-domain benchmarks, reasoning in the language
space fails to bring performance gains. The code and benchmark have been
realeased on https://av-reasoner.github.io.
68424822f0c91a7dcb64193b
Lidong Lu
claimed_verified
2025-06-06T07:41:34.755Z
64a3de701698ad2985277148
/avatars/09eebadbbea53ed2800591564ff5c931.svg
lulidong
user
lulidong
68424822f0c91a7dcb64193c
Guo Chen
claimed_verified
2025-06-06T07:41:32.158Z
6392c73390b8e99a6779a7b0
/avatars/9ff824ab02848120aec5e8de6780bcf1.svg
Guo Chen
user
cg1177
68424822f0c91a7dcb64193d
Zhiqi Li
68424822f0c91a7dcb64193e
Yicheng Liu
68424822f0c91a7dcb64193f
Tong Lu
AV-Reasoner: Improving and Benchmarking Clue-Grounded Audio-Visual
  Counting for MLLMs
Despite progress in video understanding, current MLLMs struggle with counting
tasks. Existing benchmarks are limited by short videos, close-set queries, lack
of clue annotations, and weak multimodal coverage. In this paper, we introduce
CG-AV-Counting, a manually-annotated clue-grounded counting benchmark with
1,027 multimodal questions and 5,845 annotated clues over 497 long videos. It
supports both black-box and white-box evaluation, serving as a comprehensive
testbed for both end-to-end and reasoning-based counting. To explore ways to
improve model's counting capability, we propose AV-Reasoner, a model trained
with GRPO and curriculum learning to generalize counting ability from related
tasks. AV-Reasoner achieves state-of-the-art results across multiple
benchmarks, demonstrating the effectiveness of reinforcement learning. However,
experiments show that on out-of-domain benchmarks, reasoning in the language
space fails to bring performance gains. The code and benchmark have been
realeased on https://av-reasoner.github.io.
CG-AV-Counting is a new benchmark for video counting tasks that includes multimodal data and supports end-to-end and reasoning-based models. AV-Reasoner, trained with GRPO and curriculum learning, achieves top results but shows limitations on out-of-domain tasks.
MLLMs CG-AV-Counting multimodal questions clue-grounded black-box evaluation white-box evaluation AV-Reasoner GRPO curriculum learning reinforcement learning out-of-domain benchmarks